diff --git a/.pylintdict b/.pylintdict
index f8ac2c55..a991c0d6 100644
--- a/.pylintdict
+++ b/.pylintdict
@@ -145,6 +145,7 @@ hopkins
 hoyer
 https
 hyperparameters
+iae
 idx
 im
 imag
@@ -155,6 +156,7 @@ interatomic
 ints
 iprint
 iqft
+isa
 ising
 iteratively
 iz
@@ -333,6 +335,7 @@ spsa
 sqrt
 statefn
 statevector
+statevectorestimator
 statevectors
 stddev
 stdout
@@ -367,8 +370,12 @@ trainability
 transpilation
 transpile
 transpiled
+transpiler
+transpiler's
+transpiling
 trotterization
 trotterized
+tweedledum
 uncompute
 unitaries
 univariate
diff --git a/docs/conf.py b/docs/conf.py
index 9b35ead3..b3b0d5e9 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -86,7 +86,7 @@
 )
 nbsphinx_prolog += link_str + "{{ docname }}"
 
-nbsphinx_timeout = 360
+nbsphinx_timeout = 1800
 nbsphinx_execute = os.getenv("QISKIT_DOCS_BUILD_TUTORIALS", "never")
 nbsphinx_widgets_path = ""
 nbsphinx_thumbnails = {
diff --git a/docs/tutorials/01_algorithms_introduction.ipynb b/docs/tutorials/01_algorithms_introduction.ipynb
index 01c3e7b2..a74fdf9d 100644
--- a/docs/tutorials/01_algorithms_introduction.ipynb
+++ b/docs/tutorials/01_algorithms_introduction.ipynb
@@ -29,10 +29,10 @@
    "outputs": [],
    "source": [
     "from qiskit_algorithms.optimizers import SLSQP\n",
-    "from qiskit.circuit.library import TwoLocal\n",
+    "from qiskit.circuit.library import n_local\n",
     "\n",
     "num_qubits = 2\n",
-    "ansatz = TwoLocal(num_qubits, \"ry\", \"cz\")\n",
+    "ansatz = n_local(num_qubits, \"ry\", \"cz\")\n",
     "optimizer = SLSQP(maxiter=1000)"
    ]
   },
@@ -54,7 +54,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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nHH7dwGrE37wd/3Yj+tJ/3mQ+uudpX5cmfuJJDzSJCGXIoplsm/mcP0sTP/H0OGALbkJ0fBs+uOv3hLZoyh3rnqIw8xxZ//off5XaoBRmLKIo81y1syshUREER4ZzxZ5fc9vsPKI6tqbk/CUAojq1IeezL/1VqviAN+MP0HZob4Y//zCf3L+Iy+k5/ipTfMjTHojuHEdUh1bcsfYPAIQ2awpBNkKbR7Hz0eX+LFkamDfHgeLsPE69txOX00lZfiHZn6TSemCSacOM7pmxiPwjGTgrKmk3sh8APaffzun1u3CWO2psm7lxDz3uvw1wB5m4Ycmc+eCLGtuJeXgz/m2H9OL7Lz3C1ulLKDia6e9SxUc87YFLX5/h731msGbww6wZ/DBHX9vMyb9vVZCxAG+OA6fe20mHUQMAaBIeStywZPKPmPd4oDMzFrJ99guMeH42oYt+QWGGne1zXiSmR3yN7Q7/dT3Dlz3M3XuW46p0svc3f6Msv9CAiqUheTr+w597mCahIYxY9vDVfR95iUtfn/FnueIDnvaAWJenPXD0lY0MXfIgkz5bhssFmZs/J3PTHgMqbhgKMxZy6eszbLrjsRtu5ygp47OHlvmhIvEnT8d/3fBH/FCNGMHTHrjWwefe9VE1YgRPe6CyrMJSZ+N0mcniKischMVEM/HjZz16/Dbld9Po+8hdlF0q8kN14mvejn/vWeMZsmgmpTpTZxnqAQmE1wGby+VyGV2EVFdxpZQVXe8zugyvTE1/h5BIDz6i2mCDN4ATd4r/YqLR1VyfesA3zDL+oB7wFfWAbxnVAzozIyIiIqamMCMiIiKmpstMjZDL5cJRUmZ0GV4JjgjDZmv8n0VtllPM6gHfMMv4g3rAV9QDvmVUD+hppkbIZrM1+uvO4lvqAVEPiHrAc7rMJCIiIqamMCMiIiKmpjAjIiIipqYwIyIiIqamMCMiIiKmpjAjIiIipqYwIyIiIqamMCMiIiKmpjAjIiIipqYwIyIiIqamMCMiIiKmpjAjIiIipqYwIyIiIqamMCMiIiKmpjAjIiIipqYwIyIiIqamMCMiIiKmFmx0AVKTy+XCUVJmdBleCY4Iw2azGV2GZagHRD0g6gEvfq7ff6LckKOkjBVd7zO6DK9MTX+HkMhwo8uwDPWAqAdEPeA5XWYSERERU1OYEREREVNTmBERERFTU5gRERERU1OYEREREVNTmBERERFTU5gRERERU1OYEREREVPTpHkWEjc0mTvWPVltWUVxCZdP5ZK+Zjtpr2/BVek0qDrxB/WAqAcCW6COv8KMBZ1at4OsralgsxHRugXdJt/C4Cen0zypA3vmv2J0eeIH6gFRDwS2QBt/hRkLunjoNKfW7qj6+tibH3LXjhfo/tMfkLpoFWUXLxtYnfiDekDUA4Et0MZf98wEAEdJGRdST2ALCqJZ57ZGlyMGUA+IeiCwWX38FWYCRHSCu3nLLhUZXIkYRT0g6oHAZuXxD4gwk5eXx4IFC+jWrRvh4eF06tSJRx99lOLiYmbMmIHNZmP58uVGl9lggiNCCYuNJqxlM1r0jOfmZ2bSsm8XLqSe4PKpXKPLEz9QD4h6ILAF2vhb/p6ZgwcPMnbsWOx2O02bNqV3797k5OTw4osvkp6eTn5+PgADBgwwttAGdNOCKdy0YEq1ZRmbP2fv438zqCJjZRXD2gz4NBe+vYffifvr77eFYAtGevXAVS4XfJkPazKqj//vU2FyAvSJAZvNuPp8RT1wlcMJ2+ywLqN6D7x0FO7uDB2aGlicjwTa+Fs6zOTl5TFhwgTsdjvz5s3jiSeeIDo6GoAlS5bw2GOPERwcjM1mo1+/fgZX23COvf0RGRv3EBQSTEzPePrMnkTTdi2pLCuv2uaWl38FQTY+e/DPVctCW0Qxadsy9j/1FqfW7ajtW5tKhRMWfgUbztS+fv4+iIuAJYOgdwu/luZz6gG3vFJYsA++Kqi5bkuW+9/3WsKiFIgN8399vqQecPsqHx7fD+dKa67775Pw1km4qzMs6GutNzaBNv4WGrqa5s6dS1ZWFnPmzGHp0qVVQQZgwYIF9O/fH4fDQUJCAs2aNTOw0oZ1+ZSd3B2HyN56gMN/Xc8nDyyi1YCuDF38YNU2ex5/jTaDepA4aXjVsiHPzOT8F1+bqoGvx+F0h5XrBZlv2Utg1i44XMuLnZmpB9xBZsbO2oPMtVIvwsydcKnMP3X5i3oADl6Eh3bXHmS+5QLWZboDT6XLb6X5XKCNv2XDTFpaGqtXr6ZVq1YsXLiw1m0GDhwIQP/+/auWfRt+Bg8eTFhYGDYLnH++sP8Y6Wu2kzhpOK1TegBQfqmI3fNe5uY/zSSibQydxw0hblgyex6zxvwDb56Anec827a0EuZ94f5fqwrEHnjiAGRf8WzbM8Xw5EGflmO4QOuBKw7333W5h/PDfWqHFem+rclIVh9/y4aZVatW4XQ6mTp1KlFRUbVuExERAVQPMydPnmTt2rXExcUxaNAgv9TqD18uW4PTUclN8++pWpb96UEyNu5m5PK5DFn0C3bPe5myAvPf5V7hhH9keLfPxTL4V7ZPymk0AqkHThXC3gve7bPjHJw1/6/+nQKpBz7Igm8qvNtn9WlrnZ35d1Yef8uGma1btwIwatSo626TlZUFVA8zI0eOJDc3lw0bNjB69GjfFulHhRl2Tq/fRfuR/Whzc6+q5fuffIvoxDiytx4g65NUAytsONty3eHEW94GILMJpB5Yk1G3/dZmNmgZjY564LudK/H8jK4ZWXn8LXsDcGam+6jUuXPnWtc7HA527doFVA8zQUENn+9SUlKw2+0ebx/iCuIJBjd4HV+9sJbEScO5af49fPjjPwDuiZSKMs9TkHaDm0tuoHtSdypsjePzPqLu+i1RY37p9X6HLzro2DGh4QuqA/VA/cT+13pCuwz0er83/rmHpbdP9kFF3lMP1ENQMHHLM+q064N/eJ6iTUsbtp468kUP+HL8oX49EBcXx/79++u0r2XDTHFxMQAlJSW1rl+9ejV5eXlER0eTmJjo01rsdjvZ2Z5fwwi1NYE6TNBo33OEN9v9+LrrvzmRzVsd77nu+vrIyc2h3NU4bjrpVF5J7RcWv5utSTA55/NwVRh/J6h6oH6aBYUQWof9KmzBXv2t+pJ6oO6CIpsRV8d9i8srTd0DRo4/GNcDlg0zcXFxFBQUkJqaytChQ6uty83NZf78+QD069fP5zf5xsV592cV4gq6OhmCSbRv175xvCMDIpvUrQ5XRSnt27Rq4GrqRj1QP8EVHt75++/7OUro0KFDA1dTN+qBerDZcDkrsQU18XrXyCZO9UA91KcHvH2tvJZlw8zo0aNJS0tj8eLFjBkzhu7duwOwb98+pk2bRl5eHuCfyfK8PW1WcaWUFV3v81E1vnH8xHFCIsONLgNwP445c5f3+93aKZxn//c+KqOpB+rnzROwPM37/X7945FMfUw9UFeNqQce/Rx2nfd+v/efnU+v1+Y3fEF1oB7wnGXDzIIFC1i5ciVnz54lOTmZnj17UlpaysmTJxk7diwJCQl8+OGH1e6XCVQf/OgJo0toUP1jIakZnPDyQ2F/7NurjY2a1XpgYjy8csz9ZJunwoJgQiff1dTYWa0HJid6H2aSW0CvFr6opvEz+/hb9mmmjh07smPHDsaNG0d4eDgZGRnExsbyyiuvsHnzZo4fPw6gMGNBNhvM6O7dPv1iYFDjuMIkDSA2zD1NvTd+kgjN6nKjjTRKQ9tAr+be7fNzL48b0nhY9swMQK9evdi0aVON5UVFRWRkZBAUFESfPn0MqEx8bXR7mNPLs0sNiVGwdDAEmX9+RLnGr5IhtwS2e/Ag4Q/awZzevq9J/KeJDZbd7J7h+0zxjbf/j2S4pe63bIjBLB1mrufIkSO4XC66d+9OZGRkjfVr1qwB4OjRo9W+TkhIICUlxX+FSr1MT4I24e7LDbXNBBtsgzEdYH4fvSO3ouAgWJICrx5zzyFUWMsEas1D3GdkZvZwv/iJtbQKhzdGwLOH4V85tU+I16kp/LIn3NY47vmVOgrIMHPo0CHg+peYJk+eXOvXDzzwAG+++aZPa5OG9cNOcEdH2HPe/am535S7743o2sx9X4XVPlxQqgsOgod7wc+T4MMcOHARih3QNBhSWsLoDhDu/QMvYiItwuBPA91n6jacgdOFUOaE5qHuM3KDW+usrBUozNTC5bLwfNYBKMgGw9u6/0lgCg+GO+Pd/yQwtQrXPTFWpjBjITG9OzNs6UOEREVQnHWB7Y+8REz3Toxe8Rsup+fw0ZSnKb14mW5TbiV51jiaJ3Vk/1NvcfS1zVXfI+V300i4cxj5h06z9WdLDPxtxFuejv9Nv76X+NtScFW6H/U5tPx9Tq93P8uu8Tc3T3tgxPOzaTeyH2UX3Y/85Wz/iv1Pvw2oB8zO0x4Y9cZ8oju1qbbf1p8t4exH++k9azw9p9+Oo7iUDWMax2PqNxKQYebbz22ymhHPz2HXr/5C/pEMuk25lUG/v5+Tf/+Uy+k51Rry4lfpbHvwz/R95O4a32P/029z6dhZ4sc2/DTq4luejv+Rv67nwKJVAETGxTJp+/Pk7PiKsvxCjb/JedoDAEde3lDtjcy31APm5mkPfPrzZ6v+u2X/roxZ+VuyPz0IwNFXN5F/6DSDn5ru5+rrzrKPZgea2D6JOK6Ukn8kA4D0d7fR6bYUgkJr5tWCo5l8cyIbnCabWlKuy5vxL7989W7o4Kbh2Gw2n8+CLb7nTQ+INdW1B5LuvZVTa7fjrHD4oUrfUJdbRFR8G2J6xjPx46tpOzgijMi4WAOrEn/xdvx7zfghPaffTmT7luye9zKlF72cYVAaHa97YOYP6TblVoqz8ziweFXVC6CYV11eB5qEh9Jl0gi2TPqdP0r0GYUZC7lw4AQf3/vHqq+nHH7dwGrE37wZ/7TXt5D2+hZiendm5PK55Hz2JWUFRf4oU3zI0x5IXbSKK+cKwOUifuxgRq/4LeuGPYLjSqm/ShUf8fZ1oPP4IXxzKodLX9f/E7ONpMtMFlGUeY6mHa5OYRsSFUFwZDhX7PkGViX+UtfxLziayRV7PnHDkn1doviYNz1wxZ4P//vU5pl/fkFF4RWad2vvt1rFN+pyHOh+7w84scr895EqzFhE/pEMnBWVtBvZD4Ce02/n9PpdOMvNew1UPOfN+Dfv3rHqv6M7tyW2TyKXjjeOD1eUuvOmByLbXb3s0Pp7SYTFRnP5tAdTJUuj5u3rQHRCHC37d+H0ezv9WaZP6DKThWyf/QIjnp9N6KJfUJhhZ/ucF4npUXNijW4/+T/c9Ni9hLZoSvwdg0l+aCKfPLCI/MOnDahaGoqn45/yf6cRFd8GV4UDZ6WTz3/zuvuGcDE9T3tgxPNziGjdHFelE0dpOdt+8RwVhbVMky2m42kPgPvG38zNe6koKvFzlQ1PYcZCLn19hk13PHbD7U6+u42T727zfUHiV56O/yf3L/RDNWIET3vgo3ue8kM1YgRPewAgdeFKH1fjP7rMZHGVFQ7CYqKZ+PGzhLdsdsPtU343jb6P3EXZJd0MagUaf1EPiLc90HvWeIYsmklpfqEfqmsYNpfm7m90Kq6UsqLrfUaX4ZWp6e8QEhludBmWoR4Q9YCoBzynMzMiIiJiagozIiIiYmq6zNQIuVwuHCVlRpfhleCIME2J34DUA6IeEPWA5xRmRERExNR0mUlERERMTWFGRERETE1hRkRERExNYUZERERMTWFGRERETE1hRkRERExNYUZERERMTWFGRERETE1hRkRERExNYUZERERMTWFGRERETE1hRkRERExNYUZERERMTWFGRERETE1hRkRERExNYUZERERMTWFGRERETE1hRkRERExNYUZERERMTWFGRERETE1hRkRERExNYUZERERMTWFGRERETE1hRkRERExNYUZERERM7f8DnS7qDL1tYM4AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 705.35x200.667 with 1 Axes>"
       ]
@@ -65,7 +65,7 @@
     }
    ],
    "source": [
-    "ansatz.decompose().draw(\"mpl\")"
+    "ansatz.draw(\"mpl\")"
    ]
   },
   {
@@ -83,7 +83,7 @@
     "\n",
     "Algorithms rely on the primitives to evaluate expectation values or sample circuits. The primitives can be based on a simulator or real device and can be used interchangeably in the algorithms, as they all implement the same interface.\n",
     "\n",
-    "In the VQE, we have to evaluate expectation values, so for example we can use the [qiskit.primitives.Estimator](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.primitives.Estimator) which is shipped with the default Qiskit installation."
+    "In the VQE, we have to evaluate expectation values, so for example we can use the [qiskit.primitives.StatevectorEstimator](https://quantum.cloud.ibm.com/docs/api/qiskit/qiskit.primitives.StatevectorEstimator) which is shipped with the default Qiskit installation."
    ]
   },
   {
@@ -92,16 +92,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit.primitives import Estimator\n",
+    "from qiskit.primitives import StatevectorEstimator\n",
     "\n",
-    "estimator = Estimator()"
+    "estimator = StatevectorEstimator()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This estimator uses an exact, statevector simulation to evaluate the expectation values. We can also use a shot-based and noisy simulators or real backends instead. For more information of the simulators you can check out [Qiskit Aer](https://qiskit.github.io/qiskit-aer/apidocs/aer_primitives.html) and for the actual hardware [Qiskit IBM Runtime](https://quantum.cloud.ibm.com/docs/api/qiskit-ibm-runtime).\n",
+    "This estimator uses an exact, statevector simulation to evaluate the expectation values. We can also use a noisy simulator or real backends instead. For more information of the simulators you can check out [Qiskit Aer](https://qiskit.github.io/qiskit-aer/apidocs/aer_primitives.html) and for the actual hardware [Qiskit IBM Runtime](https://quantum.cloud.ibm.com/docs/api/qiskit-ibm-runtime).\n",
     "\n",
     "With all the ingredients ready, we can now instantiate the VQE:"
    ]
@@ -171,23 +171,23 @@
      "output_type": "stream",
      "text": [
       "{   'aux_operators_evaluated': None,\n",
-      "    'cost_function_evals': 65,\n",
-      "    'eigenvalue': -1.8572749648726616,\n",
-      "    'optimal_circuit': <qiskit.circuit.library.n_local.two_local.TwoLocal object at 0x7fad40303c10>,\n",
-      "    'optimal_parameters': {   ParameterVectorElement(θ[0]): -1.8728053741446136,\n",
-      "                              ParameterVectorElement(θ[1]): -1.1391138641128078,\n",
-      "                              ParameterVectorElement(θ[2]): 5.869131287606581,\n",
-      "                              ParameterVectorElement(θ[3]): 6.351926438071783,\n",
-      "                              ParameterVectorElement(θ[4]): 4.99489396352954,\n",
-      "                              ParameterVectorElement(θ[5]): -0.5439930158788345,\n",
-      "                              ParameterVectorElement(θ[6]): -5.992252149482055,\n",
-      "                              ParameterVectorElement(θ[7]): -1.6792234013467686},\n",
-      "    'optimal_point': array([-1.87280537, -1.13911386,  5.86913129,  6.35192644,  4.99489396,\n",
-      "       -0.54399302, -5.99225215, -1.6792234 ]),\n",
-      "    'optimal_value': -1.8572749648726616,\n",
+      "    'cost_function_evals': 82,\n",
+      "    'eigenvalue': np.float64(-1.8572750063354504),\n",
+      "    'optimal_circuit': <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fcb68ea5390>,\n",
+      "    'optimal_parameters': {   ParameterVectorElement(θ[0]): np.float64(-3.9739140951969154),\n",
+      "                              ParameterVectorElement(θ[1]): np.float64(2.6400698387407227),\n",
+      "                              ParameterVectorElement(θ[2]): np.float64(1.1631430749044),\n",
+      "                              ParameterVectorElement(θ[3]): np.float64(2.9966775731663686),\n",
+      "                              ParameterVectorElement(θ[4]): np.float64(-2.040658007445176),\n",
+      "                              ParameterVectorElement(θ[5]): np.float64(-5.521285457274116),\n",
+      "                              ParameterVectorElement(θ[6]): np.float64(0.25988967555621034),\n",
+      "                              ParameterVectorElement(θ[7]): np.float64(0.5404535807044255)},\n",
+      "    'optimal_point': array([-3.9739141 ,  2.64006984,  1.16314307,  2.99667757, -2.04065801,\n",
+      "       -5.52128546,  0.25988968,  0.54045358]),\n",
+      "    'optimal_value': np.float64(-1.8572750063354504),\n",
       "    'optimizer_evals': None,\n",
-      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x7fad38796920>,\n",
-      "    'optimizer_time': 0.13650894165039062}\n"
+      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x7fcb9ef00ec0>,\n",
+      "    'optimizer_time': 0.19738006591796875}\n"
      ]
     }
    ],
@@ -209,9 +209,9 @@
    "source": [
     "## Updating the primitive inside VQE\n",
     "\n",
-    "To close off let's also change the estimator primitive inside the a VQE. Maybe you're satisfied with the simulation results and now want to use a shot-based simulator, or run on hardware!\n",
+    "To close off let's also change the estimator primitive inside the a VQE. Maybe you're satisfied with the simulation results and now want to use a noisy simulator, or run on hardware!\n",
     "\n",
-    "In this example we're changing to a shot-based estimator, still using Qiskit's reference primitive. However, you could replace the primitive by e.g. Qiskit Aer's estimator ([qiskit_aer.primitives.Estimator](https://qiskit.github.io/qiskit-aer/stubs/qiskit_aer.primitives.Estimator.html#qiskit_aer.primitives.Estimator)) or even a real backend ([qiskit_ibm_runtime.Estimator](https://quantum.cloud.ibm.com/docs/api/qiskit-ibm-runtime/estimator)).\n",
+    "In this example we're changing to a noisy estimator, still using Qiskit's reference primitive. However, you could replace the primitive by e.g. Qiskit Aer's estimator ([qiskit_aer.primitives.EstimatorV2](https://qiskit.github.io/qiskit-aer/stubs/qiskit_aer.primitives.EstimatorV2.html#qiskit_aer.primitives.EstimatorV2)) or even a real backend ([qiskit_ibm_runtime.EstimatorV2](https://quantum.cloud.ibm.com/docs/api/qiskit-ibm-runtime/estimator-v2)).\n",
     "\n",
     "For noisy loss functions, the SPSA optimizer typically performs well, so we also update the optimizer. See also the [noisy VQE tutorial](03_vqe_simulation_with_noise.ipynb) for more details on shot-based and noisy simulations."
    ]
@@ -227,29 +227,29 @@
      "text": [
       "{   'aux_operators_evaluated': None,\n",
       "    'cost_function_evals': 200,\n",
-      "    'eigenvalue': -1.8574199402954465,\n",
-      "    'optimal_circuit': <qiskit.circuit.library.n_local.two_local.TwoLocal object at 0x7fad3901c400>,\n",
-      "    'optimal_parameters': {   ParameterVectorElement(θ[0]): -5.113683583175044,\n",
-      "                              ParameterVectorElement(θ[1]): 4.853118586793109,\n",
-      "                              ParameterVectorElement(θ[2]): 2.166347648663523,\n",
-      "                              ParameterVectorElement(θ[3]): 2.3924391958613804,\n",
-      "                              ParameterVectorElement(θ[4]): 4.624991727523756,\n",
-      "                              ParameterVectorElement(θ[5]): 5.951561020018319,\n",
-      "                              ParameterVectorElement(θ[6]): -2.811815937510964,\n",
-      "                              ParameterVectorElement(θ[7]): 1.7438519034671542},\n",
-      "    'optimal_point': array([-5.11368358,  4.85311859,  2.16634765,  2.3924392 ,  4.62499173,\n",
-      "        5.95156102, -2.81181594,  1.7438519 ]),\n",
-      "    'optimal_value': -1.8574199402954465,\n",
+      "    'eigenvalue': np.float64(-1.8537921721352064),\n",
+      "    'optimal_circuit': <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fcb68ea5d30>,\n",
+      "    'optimal_parameters': {   ParameterVectorElement(θ[0]): np.float64(3.6515906092934514),\n",
+      "                              ParameterVectorElement(θ[1]): np.float64(1.6888163552747453),\n",
+      "                              ParameterVectorElement(θ[2]): np.float64(5.0639401144453995),\n",
+      "                              ParameterVectorElement(θ[3]): np.float64(-5.106067528958566),\n",
+      "                              ParameterVectorElement(θ[4]): np.float64(4.1999076379511),\n",
+      "                              ParameterVectorElement(θ[5]): np.float64(-1.9103241055740936),\n",
+      "                              ParameterVectorElement(θ[6]): np.float64(-3.7977146497291088),\n",
+      "                              ParameterVectorElement(θ[7]): np.float64(2.0962115930756493)},\n",
+      "    'optimal_point': array([ 3.65159061,  1.68881636,  5.06394011, -5.10606753,  4.19990764,\n",
+      "       -1.91032411, -3.79771465,  2.09621159]),\n",
+      "    'optimal_value': np.float64(-1.8537921721352064),\n",
       "    'optimizer_evals': None,\n",
-      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x7fad40303be0>,\n",
-      "    'optimizer_time': 0.6039888858795166}\n"
+      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x7fcb691356d0>,\n",
+      "    'optimizer_time': 0.4407474994659424}\n"
      ]
     }
    ],
    "source": [
     "from qiskit_algorithms.optimizers import SPSA\n",
     "\n",
-    "estimator = Estimator(options={\"shots\": 1000})\n",
+    "estimator = StatevectorEstimator(default_precision=1e-2)\n",
     "\n",
     "vqe.estimator = estimator\n",
     "vqe.optimizer = SPSA(maxiter=100)\n",
@@ -283,7 +283,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.0</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td colspan='2'>Mon Feb 19 11:17:24 2024 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>2.0.2</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.4.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.13.3</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td colspan='2'>Mon Jun 16 16:39:23 2025 CEST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -295,7 +295,7 @@
     {
      "data": {
       "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -330,7 +330,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.3"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/tutorials/02_vqe_advanced_options.ipynb b/docs/tutorials/02_vqe_advanced_options.ipynb
index b5fb4b3d..8a5161fc 100644
--- a/docs/tutorials/02_vqe_advanced_options.ipynb
+++ b/docs/tutorials/02_vqe_advanced_options.ipynb
@@ -72,9 +72,9 @@
    },
    "outputs": [],
    "source": [
-    "from qiskit.primitives import Estimator\n",
+    "from qiskit.primitives import StatevectorEstimator\n",
     "\n",
-    "estimator = Estimator()"
+    "estimator = StatevectorEstimator()"
    ]
   },
   {
@@ -102,7 +102,7 @@
    "source": [
     "import numpy as np\n",
     "\n",
-    "from qiskit.circuit.library import TwoLocal\n",
+    "from qiskit.circuit.library import n_local\n",
     "\n",
     "from qiskit_algorithms import VQE\n",
     "from qiskit_algorithms.optimizers import COBYLA, L_BFGS_B, SLSQP\n",
@@ -116,7 +116,7 @@
     "for i, optimizer in enumerate(optimizers):\n",
     "    print(\"\\rOptimizer: {}        \".format(type(optimizer).__name__), end=\"\")\n",
     "    algorithm_globals.random_seed = 50\n",
-    "    ansatz = TwoLocal(rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
+    "    ansatz = n_local(2, rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
     "\n",
     "    counts = []\n",
     "    values = []\n",
@@ -151,7 +151,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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Xb/j+SIn3KHXP7XbbbaexY8dq3rx59rV58+ZpwIABdphdvXq11q9fr/vuu6/N+2gF0Gzfx/auffnll6qrq9PAgQPbvF5jY2Ob13LydX799dfaZptt2ty34et++umnbV5zyy23bPdrdML6useMGZN23efzaeTIkW2+Z5tuuql8Pp+j554yZYpcLpf9vTNNU4888oi9t9yydOlSTZs2TRtvvLEqKytVW1urvfbaS1Jiq0sqj8eTNkuis69ryJAhbVrIt9pqK/vzmbrpppv0ySefaNiwYdppp510zTXXdBl+V69erebm5jbvr7WWeDyu7777Lu36Fltskfb3yspKDR48uFvnxA8dOrTN5Prq6moNGzaszTVJbfa9d+TXv/61XnzxRT300EMaNWqUfT3Tn5H2ThOZOnWqdtttN5111lnaZJNNdMIJJ+jhhx8m4AMoCuypB4AS0dHEaDM5qMv6j9Nf/epXbapUlg0DZXcnQp966qm6+eab9fjjj+vEE0/UQw89pMMOO8z+j/lccHrsVVfvj2Xq1Km64YYbtGbNGlVVVenJJ5/UiSeeaHcmWO/jySef3GbvvcXaP27pzvsYj8c1cODAtI6HVLW1tWl/d/p1Onnd8ePH69Zbb2338xsGtJ6Qyfs2ZMgQ7bHHHnr44Yd1xRVX6K233tLSpUv1u9/9zr4nFotp//3317p163TZZZdp7Nixqqio0PLlyzVt2rQ2Ac7v9+f0lIKO/q3GYrE2144//njtsccemj9/vp577jndfPPN+t3vfqfHHntMBx98cM7W1BM6+jfYnX+bjz/+uH73u9/puuuu00EHHZT2uUx/Rtr7d1VWVqZXXnlFCxYs0FNPPaVnnnlG8+bN0z777KPnnnuuVyfyA0CmCPUA0EdY7bher1f77bdfVs8xfPhwSYmqf2q3QDQa1bffftsmzG6zzTaaMGGCHnzwQQ0dOlRLly7V//7v/3b5OqNGjdKzzz6rdevWdVitHz58uOLxuL788ku7IiolhtitX7/eXmumpk6dqpkzZ+rRRx/VJptsovr6+rRW4traWlVVVSkWi+XkfdzQhtdGjRql559/XrvttlvOjt0aNWqUPvnkky7v+fDDD7Xvvvvm7Lxw6+tevHhxWnt4OBzWN998k/X7aZk6darOP/98LV68WPPmzVN5ebkOP/xw+/Mff/yxvvjiC82dO1ennnqqff3f//53t153+PDhev7559XQ0JBWrV+0aJH9eam1e2bDSf8dVfIHDx6s888/X+eff75WrVqln/zkJ7rhhhs6DPW1tbUqLy/X4sWL23xu0aJFcrlcbX4Z8+WXX6b9LDc2NmrFihU65JBD7Gv5Pi/+iy++0Gmnnaajjjqq3Wn0ufoZcblc2nfffbXvvvvq1ltv1Y033qgrr7xSCxYs6Pa/TQDoSbTfA0AfMXDgQE2ePFn33nuvVqxY0ebzq1ev7vI5dthhB9XU1Oj+++9XNBq1rz/44IMdttCecsopeu6553T77berpqbGUZXx2GOPlWmamjlzZpvPWVU9K3TcfvvtaZ+3KsuHHnpol6/Tnq222krjx4/XvHnzNG/ePA0ePFh77rmn/Xm3261jjz1Wjz76aLvB2Mn7OGTIEG2zzTZ64IEH1NjYaF9/+eWX9fHHH6fde/zxxysWi+m6665r8zzRaLRNQHTi2GOP1YcfftjmhACp9f09/vjjtXz5ct1///1t7mlpabFPQ8jEfvvtJ5/Ppz/84Q9p1dnZs2errq4u6++Z5dhjj5Xb7dbf/vY3PfLIIzrssMPsfddSa6U49bVN09Qdd9zRrdc95JBDFIvFdOedd6Zdv+2222QYhv1vvl+/fhowYIBeeeWVtPvuuuuutL/HYrE2WwEGDhyoIUOGdHqcoNvt1gEHHKAnnngirX1+5cqVeuihh7T77runbUWQpPvuu0+RSMT++913361oNJr2c1pRUZHVv7NcaGxs1NFHH61NN91Uc+fObfcXDLn4GVm3bl2ba9tvv70kdXmEIwDkG5V6AChwTz/9tF3xSzVp0qS0aqcTf/zjH7X77rtr/PjxOvvsszVy5EitXLlSb775ppYtW6YPP/yw08f7fD5dc801+sUvfqF99tlHxx9/vL799lvNmTNHo0aNavc/uH/605/q0ksv1fz583XeeeelDeXqyN57761TTjlFf/jDH/Tll1/qoIMOUjwe16uvvqq9995bF1xwgbbbbjuddtppuu+++7R+/XrttddeevvttzV37lwdddRR7c4dcGrq1Km66qqrFAgEdOaZZ7Zpwf7tb3+rBQsWaOedd9bZZ5+trbfeWuvWrdP777+v559/vt2AsKEbb7xRRx55pHbbbTedfvrp+vHHH3XnnXdqm222SQv6e+21l8455xzNmjVLCxcu1AEHHCCv16svv/xSjzzyiO644w4dd9xxGX19l1xyif7xj39oypQpOuOMMzRx4kStW7dOTz75pO655x5tt912OuWUU/Twww/r3HPP1YIFC7TbbrspFotp0aJFevjhh+1zvjNRW1urGTNmaObMmTrooIN0xBFHaPHixbrrrru04447djlAsSsDBw7U3nvvrVtvvVUNDQ32cEPL2LFjNWrUKP3qV7/S8uXL1a9fPz366KOO93R35PDDD9fee++tK6+8Ut9++6222247Pffcc3riiSd00UUXpe3/Puuss/Tb3/5WZ511lnbYYQe98sor+uKLL9Ker6GhQUOHDtVxxx2n7bbbTpWVlXr++ef1zjvv6Pe//32na7n++uvt89bPP/98eTwe3XvvvQqFQrrpppva3B8Oh7Xvvvvq+OOPt78Xu+++u4444gj7nokTJ+ruu+/W9ddfr9GjR2vgwIH2jImeNnPmTH322Wf69a9/rSeeeCLtc6NGjdKuu+6ak5+Ra6+9Vq+88ooOPfRQDR8+XKtWrdJdd92loUOHavfdd+/JLxEAuq/3B+4DAJzo7Eg7pRwDZR2TdfPNN7d5Dm1wTJZpmubXX39tnnrqqeagQYNMr9drbrrppuZhhx1m/uMf/2jz2u+88067a/vDH/5gDh8+3PT7/eZOO+1kvv766+bEiRPNgw46qN37DznkEFOS+cYbbzj++qPRqHnzzTebY8eONX0+n1lbW2sefPDB5nvvvWffE4lEzJkzZ5qbb7656fV6zWHDhpkzZsxIO7LPNBPHWB166KFtXmOvvfZq96iuL7/80n6fX3vttXbXt3LlSvPnP/+5OWzYMNPr9ZqDBg0y9913X/O+++6z77GO/HrkkUfafY6///3v5tixY02/329us8025pNPPmkee+yx5tixY9vce99995kTJ040y8rKzKqqKnP8+PHmpZdean7//fdZfZ1r1641L7jgAnPTTTc1fT6fOXToUPO0005LO6YvHA6bv/vd78xx48aZfr/f3GijjcyJEyeaM2fONOvq6tr9miztHWlnufPOO82xY8eaXq/X3GSTTczzzjvP/PHHH9usedy4cZ2+Rnvuv/9+U5JZVVVltrS0tPn8Z599Zu63335mZWWlOWDAAPPss882P/zwwzZHq5122mlmRUVFu6+x4ZF2pmmaDQ0N5sUXX2wOGTLE9Hq95hZbbGHefPPN9hGBlubmZvPMM880q6urzaqqKvP44483V61alfazGgqFzEsuucTcbrvtzKqqKrOiosLcbrvtzLvuusvRe/D++++bBx54oFlZWWmWl5ebe++9d5ufPetn/OWXXzZ/9rOfmRtttJFZWVlpnnTSSebatWvT7v3hhx/MQw891Kyqqko7urKjI+3a+7519G9TUtrRfRseaWcdLdjex2mnnZb2XN35GXnhhRfMI4880hwyZIjp8/nMIUOGmCeeeKL5xRdftPseA0AhMUwzw8k5AABsIB6Pq7a2Vsccc0y77dpHH320Pv7443b3kCPd9ttvr9ra2m7v8wY6M2fOHJ1++ul65513Mu64AAAUFvbUAwAyEgwG20yrfuCBB7Ru3TpNnjy5zf0rVqzQU089pVNOOaWXVlgcIpFI2lwCSXrppZf04Ycftvs+AgAAtIc99QCAjLz11lu6+OKLNWXKFNXU1Oj999/X7Nmztc0226SdP//NN9/o9ddf15/+9Cd5vV6dc845eVx14Vm+fLn2228/nXzyyRoyZIgWLVqke+65R4MGDdK5556b7+UBAIAiQagHAGRkxIgRGjZsmP7whz/YR86deuqp+u1vfyufz2ff9/LLL+v000/XZpttprlz52rQoEF5XHXh2WijjTRx4kT96U9/0urVq1VRUaFDDz1Uv/3tb1VTU5Pv5QEAgCLBnnoAAAAAAIoUe+oBAAAAAChShHoAAAAAAIoUe+q7EI/H9f3336uqqkqGYeR7OQAAAACAEmeaphoaGjRkyBC5XJ3X4gn1Xfj+++81bNiwfC8DAAAAANDHfPfddxo6dGin9xDqu1BVVSUp8Wb269cvz6sBAAAAAJS6+vp6DRs2zM6jnSHUd8Fque/Xrx+hHgAAAADQa5xsAWdQHgAAAAAARYpQDwAAAABAkSLUAwAAAABQpNhTDwAAAABFIBaLKRKJ5HsZyAG32y2Px5OTY9MJ9QAAAABQ4BobG7Vs2TKZppnvpSBHysvLNXjwYPl8vm49D6EeAAAAAApYLBbTsmXLVF5ertra2pxUd5E/pmkqHA5r9erV+uabb7TFFlvI5cp+ZzyhHgAAAAAKWCQSkWmaqq2tVVlZWb6XgxwoKyuT1+vVkiVLFA6HFQgEsn4uBuUBAAAAQBGgQl9aulOdT3uenDwLAAAAAADodYR6AAAAAACKFKEeAAAAAIAiRagHAAAAAPSYH374Qb/4xS80cuRI+f1+DRs2TIcffrheeOEF+5433nhDhxxyiDbaaCMFAgGNHz9et956q2KxWNpzGYZhf3g8Hm222Wb65S9/qVAopJdffller1evvfZa2mOampo0cuRI/epXv5IkTZ48WRdddFGX637zzTfldrt16KGHdv9N6EGEegAAAABAj/j22281ceJEvfjii7r55pv18ccf65lnntHee++tn//855Kk+fPna6+99tLQoUO1YMECLVq0SP/zP/+j66+/XieccIJM00x7zr/85S9asWKFvvnmG911113661//quuvv1577bWXfvGLX2jatGlqamqy77/00ktVVlam66+/PqO1z549W7/4xS/0yiuv6Pvvv+/+m9FDONIOAAAAAIqIaZpqicS6vrEHlHndGU3hP//882UYht5++21VVFTY18eNG6czzjhDTU1NOvvss3XEEUfovvvusz9/1llnaZNNNtERRxyhhx9+WFOnTrU/179/fw0aNEiSNGzYMB155JF6//33JUk33nijnnnmGV122WW68847tWDBAv3pT3/SG2+8kdGxcY2NjZo3b57effdd/fDDD5ozZ46uuOIKx4/vTYR6AAAAACgiLZGYtr7q2by89mfXHqhyn7MYuW7dOj3zzDO64YYb0gK9pX///po/f77Wrl1rt8anOvzww7Xlllvqb3/7W1qoT/XFF1/oxRdf1LRp0yRJgUBADzzwgCZNmqT9999fF110ka644gpNnDjR+Rcp6eGHH9bYsWM1ZswYnXzyybrooos0Y8aMgjxWkPZ7AAAAAEDOffXVVzJNU2PHju3wni+++EKStNVWW7X7+bFjx9r3WE488URVVlYqEAhozJgxGjdunGbMmGF/focddtCMGTN0zDHHqKamRldeeWXGa589e7ZOPvlkSdJBBx2kuro6vfzyyxk/T2+gUg8AAAAARaTM69Zn1x6Yt9d2asO98Lm697bbbtN+++2nWCymr776Sr/85S91yimn6O9//7t9z29+8xtde+21uvzyy+XxZBZ7Fy9erLffflvz58+XJHk8Hk2dOlWzZ8/W5MmTM3qu3kCoBwAAAIAiYhiG4xb4fNpiiy1kGIYWLVrU4T1bbrmlJOnzzz/XpEmT2nz+888/19Zbb512bdCgQRo9erQkacyYMWpoaNCJJ56o66+/3r5uBflMA72UqNJHo1ENGTLEvmaapvx+v+68805VV1dn/Jw9ifZ7AAAAAEDObbzxxjrwwAP1xz/+MW0avWX9+vU64IADtPHGG+v3v/99m88/+eST+vLLL3XiiSd2+jpud6J7oKWlpdtrjkajeuCBB/T73/9eCxcutD8+/PBDDRkyRH/729+6/Rq5Vvi/3gEAAAAAFKU//vGP2m233bTTTjvp2muv1bbbbqtoNKp///vfuvvuu/X555/r3nvv1QknnKCf/exnuuCCC9SvXz+98MILuuSSS3Tcccfp+OOPT3vO9evX64cfflA8HteXX36pa6+9VltuuWWH+/Lbs3r1ai1cuDDt2uDBg/Xmm2/qxx9/1JlnntmmIn/sscdq9uzZOvfcc7N+P3oCoR4AAAAA0CNGjhyp999/XzfccIOmT5+uFStWqLa2VhMnTtTdd98tSTruuOO0YMEC3XDDDdpjjz0UDAa1xRZb6Morr9RFF13UZuL86aefLimxDWHQoEHac889deONN2bUav/QQw/poYceSrt23XXX6T//+Y/222+/dlvsjz32WN1000366KOPtO2222b6VvQYw8xkIkEfVF9fr+rqatXV1alfv375Xg4AAACAPiYYDOqbb77R5ptvntFZ6yhsnX1fM8mh7KkHAAAAAKBI0X5fIuYvuEur1i/XJtUBVRbBJMxCMKD/cG239QkyXPxuCwAAAEBxIv2ViL9/eb8+80el+nyvpLjMjUX1k+1OzfcyAAAAACArhPoSsXFsI41uWaOA1y2vm8pzV74yQ2pwGVq+dpF+ku/FAAAAAECWCPUlIlZxkz74bKVuOHobnbTz8Hwvp+D9z19304vxerVE256XCQAAAADFgpJuiQh43ZKkYCSe55UUB7/LK0kKRVryvBIAAAAAyB6hvkQEvIlvZTASy/NKikOZyydJCkaDeV4JAAAAAGSPUF8irEp9iFDviN+dDPUxQj0AAACA4kWoLxF2+32U9nsnAm6/JCkUC+V5JQAAAACQPUJ9iQh4aL/PRMAdkCQFCfUAAAAAihihvkT47UF5hHon/J4ySVIwFs7zSgAAAIDSNG3aNB111FEZP27EiBEyDEOGYcjtdmvIkCE688wz9eOPP9r3vPTSS/Y9qR+//vWv7XtM09T999+vXXfdVf369VNlZaXGjRun//mf/9FXX31l39fc3KwZM2Zo1KhRCgQCqq2t1V577aUnnnjC0XonT56ctoZNNtlEU6ZM0ZIlSzL+2rNBqC8RfrtST/u9EwFvolIfikfyvBIAAAAAG7r22mu1YsUKLV26VA8++KBeeeUVXXjhhW3uW7x4sVasWGF/XH755ZISgf6nP/2pLrzwQh1yyCF67rnn9Nlnn2n27NkKBAK6/vrr7ec499xz9dhjj+l///d/tWjRIj3zzDM67rjjtHbtWsfrPfvss7VixQp9//33euKJJ/Tdd9/p5JNP7v4b4QDn1JeIAJX6jAQ85ZKkIKEeAAAAxcY0pUhzfl7bWy4ZRo+/TFVVlQYNGiRJ2nTTTXXaaafpb3/7W5v7Bg4cqP79+7e5Pm/ePP3973/XE088oSOOOMK+vtlmm2mXXXaRaZr2tSeffFJ33HGHDjnkEEmJToGJEydmtN7y8nJ7vYMHD9YFF1ygc845J6PnyBahvkQwKC8zfm8y1JvRPK8EAAAAyFCkWbpxSH5e+4rvJV9Fr77k8uXL9c9//lM777yz48f87W9/05gxY9ICfSoj5RcTgwYN0r/+9S8dc8wxqqqq6vZ6161bp4cffjij9XYH7fclgnPqM1PmrZQkBU3eLwAAAKDQXHbZZaqsrFRZWZmGDh0qwzB06623trlv6NChqqystD+slvkvvvhCY8aMSbv3oosusu8bOnSoff2+++7TG2+8oZqaGu244466+OKL9frrr2e03rvuukuVlZWqqKhQTU2NFi9erD//+c9ZfOWZo1JfIgIezqnPhD/528UQoR4AAADFxlueqJjn67V7wSWXXKJp06bJNE199913uuKKK3TooYfqlVdekdvttu979dVX06rrG220UYfPeeWVV+qCCy7QY489phtvvNG+vueee+q///2v3nrrLb3xxht64YUXdMcdd2jmzJn6zW9+42i9J510kq688kpJ0sqVK3XjjTfqgAMO0HvvvZeT6n9nCPUlonVPPe33Tvh9iR+soMwu7gQAAAAKjGH0egt8bxswYIBGjx4tSdpiiy10++23a9ddd9WCBQu033772fdtvvnm7e6p32KLLbR48eK0a7W1taqtrdXAgQPb3O/1erXHHntojz320GWXXabrr79e1157rS677DL5fL4u11tdXW2vd/To0Zo9e7YGDx6sefPm6ayzzsrkS89Y0bTf33DDDZo0aZLKy8vb/aa157HHHtMBBxygmpoaGYahhQsX9uga88luv49SeXaizN9PkhQUvwQBAAAACp1VnW9paXF0/4knnqjFixc7PpZuQ1tvvbWi0aiCwWBWj890vd1RNJX6cDisKVOmaNddd9Xs2bMdPaapqUm77767jj/+eJ199tk9vML8Yvp9ZqxKfSjP6wAAAABKWV1dXZviak1NjYYNG9bp4xoaGvTDDz/Y7feXXnqpamtrNWnSJEeve8IJJ+ixxx7TCSecoBkzZujAAw/UJptsoiVLlmjevHlpLfyTJ0/WiSeeqB122EE1NTX67LPPdMUVV2jvvfdWv379HL1ec3OzfvjhB0mJ9vvrrrtOgUBABxxwgKPHd0fRhPqZM2dKkubMmeP4Maeccook6dtvv+2BFRWW1kF5VJ6dCAT6S5KChhJHgvTCsRwAAABAX/PSSy9pwoQJadfOPPNM/elPf+r0cVdddZWuuuoqSYm2+R133FHPPfecampqHL2uYRiaN2+e7r//fv3lL3/RTTfdpEgkoqFDh2rfffdNG7p34IEHau7cubriiivU3NysIUOG6LDDDrNf34n7779f999/v6TEvv5tt91W//rXv9oM6+sJRRPqe0soFFIo1Fq/ra+vz+NqnPNbg/Jov3ckYLXfG4bMaEiGN5DnFQEAAAClZc6cORkVZS1OirKTJ09OO2u+PS6XS+ecc06X58XPmDFDM2bMyGSJaV566aWsH5sLRbOnvrfMmjVL1dXV9kdXbSGFInVQXlf/uCH5A4mpmHHDUDRUHL+4AQAAAIAN5TXUX3755TIMo9OPRYsW9eqaZsyYobq6Ovvju+++69XXz5bVfi9JoSgt+F0p87UeK9ESXJ+/hQAAAAB9yIMPPph2rnzqx7hx4/K9vDZeffXVDtdbWVmZ7+VJynP7/fTp0zVt2rRO7xk5cmTvLCbJ7/fL7/f36mvmglWpl6RQJJ72d7TlcXnkMk3FDUOhUF2+lwMAAAD0CUcccYR23nnndj/n9Xp7eTVd22GHHQr+FLW8hnrrnEB0n9ftkttlKBY3FYzGVK3C+4EoJIZhyC+pRVKQUA8AAAD0iqqqKlVVVXV9Y4EoKyuzz58vVEWzp37p0qVauHChli5dqlgspoULF2rhwoVqbGy07xk7dqzmz59v/33dunVauHChPvvsM0nS4sWLtXDhQvuogVIT8FgT8BmW50SZmZh4H2RPPQAAAIAiVTSh/qqrrtKECRN09dVXq7GxURMmTNCECRP07rvv2vcsXrxYdXWtVdcnn3xSEyZM0KGHHiopcVbhhAkTdM899/T6+nuDP2VYHrrmVyLUh8INeV4JAAAAAGSnaI60c3IcwoZT36dNm9blnv1SQqU+MwHDJSmmYLixy3sBAAAAoBAVTaUeXWs91o5Q70RAyfeLSj0AAACAIkWoLyF2+z1H2jniNxLvVyjSnOeVAAAAAEB2CPUlxDqrnkq9MwFXYvdJS6QpzysBAAAAgOwQ6ktIwEP7fSasUB+KtOR5JQAAAEBpWr16tc477zxtttlm8vv9GjRokA488EC9/vrrkqQRI0bo9ttv7/Dx8+fP1y677KLq6mpVVVVp3Lhxuuiii9LuaWlp0dVXX60tt9xSfr9fAwYM0JQpU/Tpp5+m3XfNNdfIMAwZhiGPx6MRI0bo4osvTjtRrRgVzaA8dM2q1IeYfu+I3+WTJAWjhHoAAACgJxx77LEKh8OaO3euRo4cqZUrV+qFF17Q2rVru3zsCy+8oKlTp+qGG27QEUccIcMw9Nlnn+nf//63fU8oFNJ+++2npUuX6ve//7123nlnrVy5UrNmzdLOO++s559/Xrvssot9/7hx4/T8888rGo3q9ddf1xlnnKHm5mbde++9PfL19wZCfQmxB+VFqdQ7EXAT6gEAAFB8TNNUS57+G7bMUybDMBzdu379er366qt66aWXtNdee0mShg8frp122snR4//5z39qt9120yWXXGJf23LLLXXUUUfZf7/99tv15ptv6oMPPtB2221nv8ajjz6qnXfeWWeeeaY++eQTe80ej0eDBg2SJE2dOlUvvPCCnnzySUI9CgPT7zMTSFbqQ7FQnlcCAAAAONcSbdHOD+2cl9f+z0//o3JvuaN7KysrVVlZqccff1y77LKL/H5/Rq81aNAgPfTQQ/rkk0+0zTbbtHvPQw89pP33398O9BaXy6WLL75YJ510kj788ENtv/327T6+rKxM4XA4o3UVGvbUlxDa7zPjdyf+TyVIqAcAAAByzuPxaM6cOZo7d6769++v3XbbTVdccYU++ugjR4//xS9+oR133FHjx4/XiBEjdMIJJ+jPf/6zQqHW/37/4osvtNVWW7X7eOv6F1980e7n33vvPT300EPaZ599MvzKCguV+hLi99B+n4mAp0wSoR4AAADFpcxTpv/89D95e+1MHHvssTr00EP16quv6q233tLTTz+tm266SX/60580bdq0Th9bUVGhp556Sl9//bUWLFigt956S9OnT9cdd9yhN998U+XliY4B0zQ7fR6fz2f/+eOPP1ZlZaVisZjC4bAOPfRQ3XnnnRl9TYWGUF9CWtvvqdQ7EfAEJEmheCTPKwEAAACcMwzDcQt8IQgEAtp///21//776ze/+Y3OOussXX311V2GesuoUaM0atQonXXWWbryyiu15ZZbat68eTr99NO1xRZb6PPPP2/3cdb1Lbfc0r42ZswYPfnkk/J4PBoyZEha4C9WtN+XEM6pz0zAk/g/wiChHgAAAOg1W2+9tZqamrJ67IgRI1ReXm4//sQTT9Tzzz+vDz/8MO2+eDyu2267TTvssIO23npr+7rP59Po0aM1YsSIkgj0EpX6kkKlPjN+rxXqo3leCQAAAFB61q5dqylTpuiMM87Qtttuq6qqKr377ru66aabdOSRR9r3LV++XAsXLkx77PDhw3XHHXeoublZhxxyiIYPH67169frD3/4gyKRiPbff39J0sUXX6wnnnhChx9+eNqRdjfeeKO+/PJLvfHGG735JecFob6EBDzJSj176h0JeCskSSGTUA8AAADkWmVlpXbeeWfddttt+vrrrxWJRDRs2DCdffbZuuKKK+z7brnlFt1yyy1pj/3rX/+qvfbaS3/84x916qmnauXKldpoo400YcIEPffccxozZoykRGv/Cy+8oFmzZmnGjBlasmSJotGoRo8erU8++URDhw7t1a85Hwyzq6kCfVx9fb2qq6tVV1enfv365Xs5nfr720t1+WMfa7+tBupPp+2Y7+UUvOfe+YOmf3a/fhI1NPdMZxM4AQAAgN4WDAb1zTffaPPNN1cgEMj3cgre008/raOPPlq33HKLLrjggnwvp0OdfV8zyaHsqS8hfntPPe33TgR8lZKkkHi/AAAAgFJx8MEH6+mnn9a6deu0Zs2afC+nx9F+X0IC1pF2DMpzxO+rkiSFTEI9AAAAUEr23ntv7b333vleRq+gUl9C7EF57Kl3JOBLtLG0GHleCAAAAABkiVBfQmi/z0zAXy1JCklSnPcMAAAAQPEh1JeQ1iPtqNQ7EQgkQ71hSNFgnlcDAAAAdI4Z56UlV99PQn0Jad1TT9XZCX+yUt/iMqRIS55XAwAAALTP7U78d344HM7zSpBLzc3NkiSv19ut52FQXgkJJNvvQ1TqHQl4yyVJUcNQNNQgT0VNnlcEAAAAtOXxeFReXq7Vq1fL6/XK5aI2W8xM01Rzc7NWrVql/v3727+0yRahvoQwKC8zAU/rWZChUB0/DAAAAChIhmFo8ODB+uabb7RkyZJ8Lwc50r9/fw0aNKjbz0OOKSFWqI/ETMXiptwuxrp3xu/2238OhtarIo9rAQAAADrj8/m0xRZb0IJfIrxeb7cr9BZCfQmx2u8lKRSNqdzHt7czhmHIb0ohQwqFGvK9HAAAAKBTLpdLgUCg6xvRp7AZo4RYg/IkhuU5FVCimyEYqsvzSgAAAAAgc4T6EuJyGfK5rbPq2VfvhN8K9eHGPK8EAAAAADJHqC8xfi+hPhMBwzoxoCnPKwEAAACAzBHqS4w9AZ/2e0cCSrxfLVTqAQAAABQhQn2JsYblcaydMwFXYphgKNKc55UAAAAAQOYI9SXGGpZH+70zASMR6oO03wMAAAAoQoT6EmPtqQ/Rfu+I3+WVJAWjLXleCQAAAABkjlBfYqjUZybgToT6EKEeAAAAQBEi1JcYe1Aee+odCbj8kqRgNJjnlQAAAABA5gj1JcYelEf7vSN+TzLUx0J5XgkAAAAAZI5QX2L8XtrvMxFwByRJIUI9AAAAgCJEqC8xrXvqqdQ7EfAkQn0wHs7zSgAAAAAgc4T6EtPafk+l3gm/p0wSoR4AAABAcSLUlxgG5WWmzFsuSQrGo3leCQAAAABkjlBfYgKcU58RfzLUhwj1AAAAAIoQob7EWHvqQ1TqHfF7KyVJQZP3CwAAAEDxIdSXGLv9nkq9I2U+Qj0AAACA4kWoLzEMysuMPxnqQ+KXIAAAAACKD6G+xHBOfWb8vipJUlBmnlcCAAAAAJkj1JcY2u8zU+avliQFDUkxhuUBAAAAKC6E+hIT8CTb7xmU54jf30+SFDQMKdqS59UAAAAAQGYI9SXGT6U+IwFfItSHDEOKEOoBAAAAFBdCfYmxKvUh9tQ7EvCWSUpW6iPNeV4NAAAAAGSGUF9iAgzKy4jf7ZckhVwuxcNNeV4NAAAAAGSGUF9i7FAfpf3eiTJPmf3nUKgujysBAAAAgMwR6ksM59RnxqrUS1Io1JDHlQAAAABA5gj1JSa1/d40OXu9K26XW57k2xQk1AMAAAAoMoT6EhPwJEJ93JQiMUK9E2UyJEnBcH2eVwIAAAAAmSHUlxi/t/Vbyln1zviN5IkB4cY8rwQAAAAAMkOoLzF+j0tGovDMvnqH/Mkfg2CY9nsAAAAAxYVQX2IMw5DfPqueCfhOlBkeSVIwwpF2AAAAAIoLob4EWcPyQrTfO+J3JUJ9KNKc55UAAAAAQGYI9SXIGpYXpFLviBXqW9oJ9ZwgAAAAAKCQEepLEGfVZ6bM5ZUkhaItadfXNYW1y6wXNPOfn+ZjWQAAAADQJUJ9CWo9q55KvRN+l1+SFNwg1H++ol4r60N6efHqfCwLAAAAALpEqC9BfjvUU6l3IuD2SZKCsVDa9Ugs8UuRSJxfjgAAAAAoTIT6EhRITr/nnHpnAu5EpT7UJtQn9tNHouyrBwAAAFCYiibU33DDDZo0aZLKy8vVv3//Lu+PRCK67LLLNH78eFVUVGjIkCE69dRT9f333/f8YvPMT/t9RvyegCQpGAunXbcr9THeRwAAAACFqWhCfTgc1pQpU3Teeec5ur+5uVnvv/++fvOb3+j999/XY489psWLF+uII47o4ZXmn12pp/3ekYCnTJIUjLcf6sOEegAAAAAFypPvBTg1c+ZMSdKcOXMc3V9dXa1///vfadfuvPNO7bTTTlq6dKk222yzXC+xYATYU5+RgKdckhSKR9Kuh6OJMB+N0X4PAAAAoDAVTajPhbq6OhmG0Wn7figUUijUure6vr6+F1aWW9aRdqEoFWYn/N5EqG+JR9OuR+PJPfVU6gEAAAAUqKJpv++uYDCoyy67TCeeeKL69evX4X2zZs1SdXW1/TFs2LBeXGVuUKnPTMBbIUkKmemh3grz0bgp06RaDwAAAKDw5DXUX3755TIMo9OPRYsWdft1IpGIjj/+eJmmqbvvvrvTe2fMmKG6ujr747vvvuv26/c2Qn1mAj4r1Ke/X+GUTocILfgAAAAAClBe2++nT5+uadOmdXrPyJEju/UaVqBfsmSJXnzxxU6r9JLk9/vl9/u79Zr51jooj7ZxJwK+SklSi5n+fqUG+UgsLp+nzzS2AAAAACgSeQ31tbW1qq2t7bHntwL9l19+qQULFqimpqbHXquQ+KnUZyTgq5IkhRSXTFMyDEnpe+nZVw8AAACgEBVN6XHp0qVauHChli5dqlgspoULF2rhwoVqbGy07xk7dqzmz58vKRHojzvuOL377rt68MEHFYvF9MMPP+iHH35QOBzu6GVKgt1+z6A8R/y+RPdGyDCkWOsE/GhKkOdYOwAAAACFqGim31911VWaO3eu/fcJEyZIkhYsWKDJkydLkhYvXqy6ujpJ0vLly/Xkk09Kkrbffvu050p9TCmyp99TqXck4K+WJLUYhhRpljw+SVI4pf2eY+0AAAAAFKKiCfVz5szp8oz61AnlI0aM6LMTywMeKvWZsAfluQwp0iKV9ZdE+z0AAACAwlc07fdwjun3mfF7ApKkoGFI0Rb7OqEeAAAAQKEj1Jcg2u8zE0gN9ZGOQn3f7PoAAAAAUNgI9SWotVJPddmJgDsR6kOGITPcbF/f8Eg7AAAAACg0hPoSZFXqg1Eq9U74PX5JUtwwFAnX29dpvwcAAABQ6Aj1JcjvYU99JsrcZfafg6EG+8+pQT4cpf0eAAAAQOEh1Jcgu1JP+70jHpdHrmRmD4VaK/WpQT4a570EAAAAUHgI9SWISn1mDMNQwDAkScFw+5V62u8BAAAAFCJCfQmyBuWFonGZJm3jTgSU/EVISqhPrc7Tfg8AAACgEBHqS5DVfi8lgj265jesYwCb7GuRKNPvAQAAABQ2Qn0Jsir1Ei34TgUMjySpJdJ6pF04Jcizpx4AAABAISLUlyCv2yW3K7lHnGF5jgRciVCfVqlP3VNP+z0AAACAAkSoL1EBjzUBn0q9E36XV5IUjLTY16Kx1iAfpv0eAAAAQAEi1JcoqwU/GCXUOxFw+SRJwWhrqE+t1EcJ9QAAAAAKEKG+RNkT8Gm/dyTgToT6UCxoXwunHWlH+z0AAACAwkOoL1F+L+33mQi4/ZKkYDRkX0ut1NN+DwAAAKAQEepLVMBjtd8TRp3wewKSpGA8NdRzpB0AAACAwkaoL1EBKvUZCbgToT4UC9vX0vfU034PAAAAoPAQ6kuUPSiPUO9IwFsuSQrG2w/1VOoBAAAAFCJCfYliUF5m/J4ySVIwHrWvRTjSDgAAAECBI9SXKLv9niPtHLEr9WYi1MfipmLx1lBP+z0AAACAQkSoL1F+D+33mQh4KyVJwXji/dqw3Z72ewAAAACFiFBfoloH5RFGnfD7KiRJISVCfTSeXpmn/R4AAABAISLUlygq9ZkJ+KokSUEzLpmmItENK/W03wMAAAAoPJ58LwA9o3X6PRVmJwL+ZKh3GVI0qEjMSPt8lEo9AAAAgAJEpb5EMSgvMwFvP0lSyDCkSEubdnv21AMAAAAoRIT6EsU59ZkJJPfUB5OhfsN2+zDt9wAAAAAKEKG+RAU8iW8t59Q74/f4JUlBwyVFWtq022+4xx4AAAAACgF76ksUlfrMBNwBSValvllhMz3ER+OEegAAAACFh1BfouxQz556RwKeRKgPuZLt9wbt9wAAAAAKH+33JcoalEf7vTN+t9V+n6jUbzgYj/Z7AAAAAIWIUF+i/FTqM2K130cNQ9FwY5sQT/s9AAAAgEJEqC9RAQ/n1GfCar+XpFCoXpF4erv9htPwAQAAAKAQEOpLlH1OPYPyHLHa7yUpGG6wK/WGkbgWpv0eAAAAQAEi1Jeo1un3hFEnDMNQQIkEHww12nvqy5Pv44Z77AEAAACgEBDqS5QV6kNU6h3zG9Z71qiwFer9iQMionHa7wEAAAAUHkJ9ibLb7xmU55gV6lsiTfYe+gpfslJP+z0AAACAAkSoL1F+j9U2bipGldmRMiNRlQ9FmhRNVurLfIlrYdrvAQAAABQgQn2Jsir1EsPynPK7vJKkYMo59eXJSj3t9wAAAAAKEaG+RFlH2kmEeqcCVqiPtiicbL+3Qn0sTscDAAAAgMJDqC9RLpchn9vaV0/ruBMBt0+SFIoG21TqJSbgAwAAACg8hPoS5ues+oz4XYlQH4wF7cF4Fck99RKhHgAAAEDhIdSXsNaz6gn1TgQ8AUlSMBZSJNlqX5ZSqY/GaL8HAAAAUFgI9SXMPtYuQoXZiYA7EepDsbBdlfd73HK7DElU6gEAAAAUHkJ9CbOG5YWo1DviT1bqW2Jhu/3e6zHkSYZ6jrUDAAAAUGgI9SXMar8PMSjPkYC3XJIUikfsqrzP7bIHDkZovwcAAABQYAj1JSzAoLyMBDzJUG9G7SPtvG6XvJ7E+xilUg8AAACgwBDqS5g9KC9KqHfC762QJLWYUTvAe90u2u8BAAAAFCxCfQnze6zp94RRJ8p8lZKkkBm32++9bkNe2u8BAAAAFChCfQmj/T4zfivUK65osrvB63bJR/s9AAAAgAJFqC9hrefUE0adsEJ9i2HIjLZISu6pd9N+DwAAAKAwEepLGJX6zJT5qiRJIcOQyw71hjwu2u8BAAAAFCZCfQmz99QzKM8Rv6dMkhXqg5Ikn6d1+n2EowEBAAAAFBhCfQmzKvUh2u8dCXgCkqQWV2ul3uNyyZdsv4/GeR8BAAAAFBZCfQkL2NPvqdQ7YYX6kGHIFWttv7em34dpvwcAAABQYAj1Jax1UB6h3gm/2y9JChqGPFao97jkcdN+DwAAAKAwEepLWOugPMKoE1alPhHqQ5Ikn5v2ewAAAACFi1BfwvxeBuVlIuBubb93x1KPtKP9HgAAAEBhItSXMNrvM2O134dcLrnjyUF5boP2ewAAAAAFi1BfwgIe2u8zUZY80k6SXMlQ73O75E2230divI8AAAAACguhvoRRqc+MVamXJMNslpRov/clK/XROO33AAAAAApL0YT6G264QZMmTVJ5ebn69+/v6DHXXHONxo4dq4qKCm200Ubab7/99J///KdnF1pArFAfpm3cEbfLLY8SVXlDVqhPOdKO9xEAAABAgSmaUB8OhzVlyhSdd955jh+z5ZZb6s4779THH3+s1157TSNGjNABBxyg1atX9+BKC0fr9Hsq9U6VGYlfhLgUlJSo1HtovwcAAABQoDz5XoBTM2fOlCTNmTPH8WN++tOfpv391ltv1ezZs/XRRx9p3333zeXyCpLdfk+F2TG/4VaDGZXUOv3ear8n1AMAAAAoNEUT6rsrHA7rvvvuU3V1tbbbbrsO7wuFQgqFQvbf6+vre2N5PSLgYU99pgIurxQPyWUk/g2ktt9HONIOAAAAQIEpmvb7bP3f//2fKisrFQgEdNttt+nf//63BgwY0OH9s2bNUnV1tf0xbNiwXlxtbqW235smgdSJgMsrSXK5wpIkr4f2ewAAAACFK6+h/vLLL5dhGJ1+LFq0qFuvsffee2vhwoV64403dNBBB+n444/XqlWrOrx/xowZqqursz++++67br1+PvmT7fdxkyqzU/5kqDeMRKhPHGlH+z0AAACAwpTX9vvp06dr2rRpnd4zcuTIbr1GRUWFRo8erdGjR2uXXXbRFltsodmzZ2vGjBnt3u/3++X3+9v9XLGxKvWSFIzG5POUfGNGtwWSx9oZRlTSBkfa8YsRAAAAAAUmr6G+trZWtbW1vfqa8Xg8bc98KfO5XTIMyTQTLfj9At58L6ngBdw+SZLhisgwJLfLkDfZfh+mUg8AAACgwBRN6Xbp0qVauHChli5dqlgspoULF2rhwoVqbGy07xk7dqzmz58vSWpqatIVV1yht956S0uWLNF7772nM844Q8uXL9eUKVPy9WX0KsMw5E9W50MRAqkTfndAkmS6InbbvYf2ewAAAAAFqmim31911VWaO3eu/fcJEyZIkhYsWKDJkydLkhYvXqy6ujpJktvt1qJFizR37lytWbNGNTU12nHHHfXqq69q3Lhxvb7+fAl43QpG4kzAdyjgKZMkmUbMbrv3Mf0eAAAAQIEqmlA/Z86cLs+oT53wHggE9Nhjj/Xwqgpf4li7iIJU6h2xQn3MiNlt914P0+8BAAAAFKaiab9Hduxj7aJU6p3wexOhPu6K2e33TL8HAAAAUKgI9SUukDzWjvZ7ZwLeCklS3Ii37ql30X4PAAAAoDAR6kuc3w71VJmdsEJ9xNXafu9Ltt9HqdQDAAAAKDCE+hIXSE6/p1LvTMBXKUmKGFLAlQjxVsU+TKUeAAAAQIEh1Jc42u8z4/cmQn3IMFThjkpKbb+nUg8AAACgsBDqS5w1KC8UJZA6EfAl2u+DLpcq3WFJre33hHoAAAAAhYZQX+Ko1GfGOtIuaBiqUCLUW+33UdrvAQAAABQYQn2JS5xTT6XeqYA7ICkZ6l0RSal76nkPAQAAABQWQn2Js8+pp1LviN/jl5TcU++yKvW03wMAAAAoTIT6Ekf7fWbSKvVGSFJrpT5CtwMAAACAAkOoL3GcU5+ZgCcZ6l2Gyoz0PfWROHvqAQAAABQWQn2Jo/0+M1alPmS0hnpPSvu9aRLsAQAAABQOQn2J8ycH5QVpHXfE2lMfNAyVKdF+70tW6k1TilGtBwAAAFBACPUljkp9ZlL31AeUvqdekqKEegAAAAAFhFBf4qwj7Qj1zlh76k3DkFdBSemhnmPtAAAAABQSQn2Js6bfhxiU54hVqZckl1oktR5pJzEBHwAAAEBhIdSXOLv9Pkql3gmPyyMj2WFvmIlQbxiGPC5rWB7t9wAAAAAKB6G+xHFOfWYMw5BPiffMSLbfSynH2tF+DwAAAKCAEOpLXOugPMKoU97kj0XcTA31rcfaAQAAAEChINSXOD+D8jLmMT3JP7XY11or9bTfAwAAACgchPoSZw/KY8CbYx4lQn3cDNvXaL8HAAAAUIgI9SWOc+oz50qG+ljqnnoP7fcAAAAACg+hvsSlVupNk9ZxJ9ymV5IUS63Uu2i/BwAAAFB4CPUlzgr1Ei34ThlKhPqoGbGv0X4PAAAAoBAR6ktcwNP6LaYF3yHTJ0mKpIZ62u8BAAAAFCBCfYnzuF3yuBKBlGPtHGov1DP9HgAAAEABItT3AVYLPpV6Z8xkqA8ral9r3VPPL0YAAAAAFA5CfR/gT7bgB6OEeificb8kKWymhHra7wEAAAAUIEJ9H9BaqSeQOhEzk6FeMSl5YgDt9wAAAAAKEaG+D/BzVn1GoslKfdCQFEsca+eh/R4AAABAASLU9wEBD3vqMxGOByRJIcOQIs2SJB/t9wAAAAAKEKG+DwjYlXoCqRPReGJQXtAwpEiLJNrvAQAAABQmQn0fYO2pDzEoz5F4zCOpo1DPL0YAAAAAFA5CfR/AkXaZicW8ktLb773uZPt9lFAPAAAAoHAQ6vsA2u8zE7Uq9S4q9QAAAAAKG6G+D2BQXmZiseT7lVapT4b6OHvqAQAAABQOQn0f4Lf31FNl7oppmopGU9vvN6jU8x4CAAAAKCCE+j4gwDn1jkVipsx4ItQHDVfbPfW03wMAAAAoIIT6PqB1UB6BtCvReFwyO55+H+ZIOwAAAAAFhFDfB9h76jnSrkuRqCkzeU59qJ1BeVEq9QAAAAAKCKG+D6D93rlwLC6ZVvt9O0faEeoBAAAAFBBCfR9gtd+HaL/vUiQWt/fURw1DkXCTpNQj7Wi/BwAAAFA4CPV9QKaV+tUNIS1YtErxPnh8WySlUi9JoXCDJM6pBwAAAFCYCPV9gD/DPfVXzP9Yp895R69+taYnl1WQIjHTHpQnScGIVamn/R4AAABA4SHU9wGtlXpngfSjZeslSV+ubOipJRWsRGg35Ikn3rMQ7fcAAAAAChihvg/w20fadV2pbwhGtLI+JEn6oS7Yo+sqRFYl3m1a3Q3WoDzrSDsq9QAAAAAKB6G+D7CPtHMQ6r9e3WT/+Yf6PhzqZb1niffAar/nSDsAAAAAhYRQ3wdk0n7/9apG+899sVIfjiba693JffWhDSr1tN8DAAAAKCSE+j7APtLOwaC8r1e3hvoVfTDUR+OJX3y4kqE+GLMq9Uy/BwAAAFB4CPV9QMDeU991IP0qpVK/sj7Y5461a22/90mSgtHEfAGm3wMAAAAoRIT6PiCTc+pTK/XRuKk1TaEeW1chstrvXUqcVR+MJUO9h/Z7AAAAAIWHUN8HWIPyonGz00FvkVhcS9Ym9pD7ku3mfW1fvVWJdxmJSn0oHpYkeV203wMAAAAoPIT6PsBqv5ekULTjULpkbbOicVPlPre2Glwlqfv76oORmP6bUv0vdK2hPiBJCsYjkiSvh/Z7AAAAAIWHUN8H+D2t3+bOWvCt1vtRtZUaXF0mqfuV+ivmf6x9fv+y3lvyY7eep7dEY1b7/Qahnun3AAAAAAoQob4PcLkM+ZLBPthJpd4akjd6YKUGVSdCbXcr9R9+t16S9M2apm49T28JW5V6VzLUm1EpHre3I1CpBwAAAFBIPPleAHpHwONSOBp3WKmvkMfeU9+S9WuapqllPyYe3xyOZv08vcluv3eVSaYUMgwpGpSH6fcAAAAAChCV+j6i9Vi7TkL9qtT2++5X6tc0hu09/M3hrifvFwL7SDtXuSQpaLikSEta+71p0oIPAAAAoDAQ6vuIrs6qN01TX69OtMiPHlipQf0Sof6H+uxD/fL1rVX+4gn1icDusdrvDUOKNNuhXkqcIgAAAAAAhYD2+z7COqs+1EGlflVDSI2hqNwuQ8NrKuRPHoP3Q11QpmnKMIyMX3PZj832n1uKrP3e5/ZLESnkMhKV+nIj7Z7UkA8AAAAA+UIy6SOskB6Mth/qrSF5wzcul8/j0sB+fkmJI/DWN0eyek1rP71UTJX6ZKh3Jb7+9ir1kSiVegAAAACFgVDfR1iV+o7a760heSNrK5P3u1VT4ZOU/b765SmhvqVoQn0isPvcqe33LfK4Uir1cYblAQAAACgMRRPqb7jhBk2aNEnl5eXq379/xo8/99xzZRiGbr/99pyvrRh0NSjPqtSPGlhhX7OOtfuhPrsJ+Knt98VSqQ9HrUp94hcaVqXeMAyOtQMAAABQcIom1IfDYU2ZMkXnnXdexo+dP3++3nrrLQ0ZMqQHVlYc7Pb7Lir1o5OVekndnoCfNiivk6n7hcQK7P5kpT6UrNRLaj3WjvZ7AAAAAAWiaAblzZw5U5I0Z86cjB63fPly/eIXv9Czzz6rQw89tAdWVhxa2+/bD9dfr0pMvh81sDXU25X6LEJ96hn1UvEMyosm2+8DnmT7vas11Cf21ccUplIPAAAAoEAUTajPRjwe1ymnnKJLLrlE48aNc/SYUCikUChk/72+vr6nlter7Pb7dgblNQQj9tF1o9Iq9WWSsqvU/9gcSWu5L5b2e6tSH3CnH2knyR6WF2VPPQAAAIACUTTt99n43e9+J4/HowsvvNDxY2bNmqXq6mr7Y9iwYT24wt7T2aC8/ybPp6+t8qu6zGtft8+qzyLUpw7Jk4pnUF7Ybr9PTv83Uiv1tN8DAAAAKCx5DfWXX365DMPo9GPRokVZPfd7772nO+64Q3PmzMnojPUZM2aorq7O/vjuu++yev1CE0juqW/vnHp7SF5tRdr11j31mQ/Ks4bk+T2Jf2JNRdJ+b1XqyzwdV+ppvwcAAABQKPLafj99+nRNmzat03tGjhyZ1XO/+uqrWrVqlTbbbDP7WiwW0/Tp03X77bfr22+/bfdxfr9ffr8/q9csZFb7fSjaNpDaQ/JS9tNLrXvqV9QFZZpmRr8csYbkjR5YqU+/ry+i9vtEFb411LvaVOqjhHoAAAAABSKvob62tla1tbU98tynnHKK9ttvv7RrBx54oE455RSdfvrpPfKahayzQXlWqE/dTy+1hvrmcEwNoaj6BbxtHtsRa0jeFslQXyzt91alvtyX+NrDLkPxcJNcaq3UW8EfAAAAAPKtaAblLV26VOvWrdPSpUsVi8W0cOFCSdLo0aNVWZkIo2PHjtWsWbN09NFHq6amRjU1NWnP4fV6NWjQII0ZM6a3l593nZ1T39p+nx7qy30e9Qt4VB+M6oe6YIahPtGyvsUmVZKkaNxUOBqXz1PYYxxa2+/L7GvBaJPKlRrqqdQDAAAAKAxFE+qvuuoqzZ071/77hAkTJEkLFizQ5MmTJUmLFy9WXV1dPpZX8Pze9s+pj8TiWrI2EcA3bL+XEhPw64MNWlEX1JbJgO6EValPfUxLOFYEoT69/V6SQuHmZKhPtN+zpx4AAABAoSiaUD9nzpwuz6g3zc7bojvaR98XBJJhesMj7ZasbVY0bqrc57YH46UaVB3Q4pUNWpnhBHxr+v3mA8rlcRmKxk01R6KqlvNqfz7YR9p5PfIaLkXMuEKRxOkA9pF2tN8DAAAAKBCFXTZFznTUfp+6n769QXiDU4blOVXXElFDKDHtftP+5Sr3JV67GIblWZV6r9ulgJH4BURLNNHJYHUZ0H4PAAAAoFAQ6vuIQAft962hvqLNY6TWYXk/1Ds/1s7aT19T4VOZz61yX6IhpBiG5VmB3et2ye9KhPpQMtR7XLTfAwAAACgsRdN+j+6xzovfsFLf0ZA8SzaVems//dCNEsPmiqtS3xrqA26fFJWCoUapeZ36q0H91SCjZZ3U3P4vQfo8w5DKNsr3KgAAAIA+g1DfR3R0Tv3XqxP7xdsbkidJg6oTwfyHDEK9tZ9+02SoL7NDfTSDFedHJGqFeiMR6iUF130l3bS5bpOkgKQXkh9o309Ok474Q75XAQAAAPQJtN/3Ee2dU2+apr62KvUdhPruVerLJbVW6ouh/T6csqfe76+WJIXamTWATvz3pXyvAAAAAOgzqNT3Ee0NylvVEFJjKCq3y9DwmvJ2H2ftqa9riag5HLX3x3dm+frEHvShdqU+8ZimIgj10XhK+70v8YuOlilzpBEH6tJ/fKhH3l+mSw4co/P3Gp3HVRao1Yuku3eVQvX5XgkAAADQZxDq+4iAp+2gPKtKv9nG5fInP7+hKr9HFT63msIx/VAX1MgO9t6nsir1m/ZP7qn3WpX64mm/97ldCrgTv9AIxcOSyyWP1yNTLkXjhuSiyaWNQKKzQcF6yTQT++sBAAAA9CiSSR9ht99HYzLNRIv5V11MvpckwzBaJ+A7bMHvqP2+OAblJdvvPYYCnmSoj4UkJYJ+4h6m37cr0C/xv2ZMijTndy0AAABAH0Go7yP8yWq5abYeydbVfnrL4OSwPCf76huCEdW1RCS1NyivsEO9aZr2e+NxueR3+yVJLdGW5DWOtOuUr1Iykv+XEqQFHwAAAOgNhPo+wqrUS60t+K2V+s5D/Sb9rLPquw71y9cnAnD/cq8q/YndHfagvEhhh/po3LT/7HO72lTqvcljASNRs+2DkWi391cl/hxqyO9aAAAAgD6CUN9H+Nwue4tzKJoI11+v6vw4O0vrBPyWLl9n+QZn1Euyh+sV+pF20VhrWPd6DHtPfTCa+GWGN9l+bw3TQzuSJwYwLA8AAADoHYT6PsIwDHtYXigSV2MoalfeRw3oPNRnsqd+wyF5UvHsqU9tq/e6XfJ7Eu33wVgy1Cfb79lT3wlrX32wLr/rAAAAAPoIQn0fknpWvbWffkClX9Xl3k4fZ1XqnbTfL/vROs6u9Yi8YjmnPjWse1ytlfpQNL39Pkz7fcf8yVBPpR4AAADoFYT6PqT1rPq4vk7upx89sOPJ95ZMKvXWnvrUSn2Z3X5fHKHe6zaSnQ3J9vsY7feO2ZV6Qj0AAADQGwj1fYgd6qMxfbXK2ZA8qXX6/ZrGsL0fvyPL2t1TXySV+mQF3grv1vR7a0+9z037fZeo1AMAAAC9ilDfh/g9Ke33dqW+61C/UblXvuRjV9WHOr13+QZn1EutR9o1FfigvEjcqtQnvtYyT+IXE9b0e4+b9vsuUakHAAAAehWhvg9Jb79PTL53Uqk3DCNlAn7HLfjN4ajWNoUltZ5RL0nl3iKp1MfSQ/2GlXrrOpX6TlCpBwAAAHoVob4PsQblNYWi+nZNMtQ7qNRL0qB+XR9rZ1XpqwIeVZe1Dt8rL5Y99ckKvNVmbx9pZ++pT1xnT30nqNQDAAAAvcqT7wWg9/iTR9otXtmgaNxUuc+twcmw3pXBDoblLWtnSJ7U2n5f6OfUW0faWW321qC8L378Qif/62Stb46ofHijvnR5dPK/qvK2zkLmalyl4yvKdRiVegAAAKBXZBXqm5qaVFHR9dR0FBarUv/p94nANbK2Qq7k2etdGZQcltdZ+/2ydvbTSymD8iIFXqlPmX4vSZtWbipJaom26MPVH0qS3OVSi6QPV+dliUWhrn+1DuOcegAAAKBXZBXqN9lkEx1//PE644wztPvuu+d6Tegh1p76T5cnAtdoB/vpLU4q9cvbmXwvtYb6SMxUJBa396YXmmgsffr9iOoR+sfh/9CyxmWSpE+W1+l/X/hKwzYu068P2zpv6yxUyxqW6ZZ3b1GTy2BPPQAAANBLsgr1/+///T/NmTNH++yzj0aMGKEzzjhDp556qoYMGZLr9SGHAsn2e2uYnZMheRbrrPoV9Z1V6psltRfqW/+ZNYdjqi4rzFBvVeqtSf+SNGbjMRqz8RhJUll4jaKNfvkqqrTvZnvmZY2F7L/r/6tb3r1FLYbBnnoAAACgl2SVro466ig9/vjjWr58uc4991w99NBDGj58uA477DA99thjikYLe+90X2W131ucDsmTWgfl/dDJoLz2zqiXEiHZk2zzL+QJ+OENpt9vyMP0+05ZRwC2uFxU6gEAAIBe0q2SaW1trX75y1/qo48+0q233qrnn39exx13nIYMGaKrrrpKzc3NuVoncsBqv7c4OaPeYrXfr2oIdRhql69vf0+9VBzD8qyvy9PBnAFrr32YUN8uK9RHDEPRUEOeVwMAAAD0Dd0K9StXrtRNN92krbfeWpdffrmOO+44vfDCC/r973+vxx57TEcddVSOlolc8KeEepchDa9pG747UlPpl8dlyDSl1Q2hNp8PRmL29Q2n30ut++oL+Vi79trvU1kVfGvvPdKVeVu/7y3xiBTpeKsGAAAAgNzIak/9Y489pr/85S969tlntfXWW+v888/XySefrP79+9v3TJo0SVtttVWu1okcSG2/H15TYR9x54TbZWiTfgEtX9+iH+qDGrJBcP8+WaWv8LnVv9zb5vGJffWhgp6AH9lgUN6GrLBP+337fC6fXIZLcTOuFpdLVaF6yevsyEQAAAAA2ckq1J9++uk64YQT9Prrr2vHHXds954hQ4boyiuv7NbikFuBlBA/qjbzIwkHVSdDfTsT8K399JtuVCbDaNu+XpbsEmgKFX77vdVmvyGrLZ/2+/YZhqEyT5maIk2tw/IqB+Z7WQAAAEBJyyrUr1ixQuXlnbdul5WV6eqrr85qUegZqXvqM5l8b7En4HcS6tvbTy+lnFVfyO330c4H5XkZlNclO9S7DCnEWfUAAABAT8sq1EejUdXXt51ubRiG/H6/fD5ftxeG3Ettv89k8r1lcCcT8Jevb/84O0tZUeypd9Z+z576jtkT8DnWDgAAAOgVWYX6/v37t9tibRk6dKimTZumq6++Wi5XYZ5J3hf1RqW+vSF5UsqgvALeUx922H4fjZuKx025OpiS35dxrB0AAADQu7IK9XPmzNGVV16padOmaaeddpIkvf3225o7d65+/etfa/Xq1brlllvk9/t1xRVX5HTByF5qpX50FqF+cHUisLW3p355l+33iX9qLQV8pF20i0q9N2UqfiQel9/lfNBgX0GlHgAAAOhdWYX6uXPn6ve//72OP/54+9rhhx+u8ePH695779ULL7ygzTbbTDfccAOhvoBYlfoBlX5VtzOhviuOKvVF3X7f+Z56X8r1aMyUP6ufntJmhfpmw6BSDwAAAPSCrHrj33jjDU2YMKHN9QkTJujNN9+UJO2+++5aunRp91aHnNp+WH/tMnJj/WzPzbN6/OBkqF9ZH1Q83rqvPByNa2VDIuh3tKe+ohgG5Tk8pz71XqRLa7+nUg8AAAD0uKxC/bBhwzR79uw212fPnq1hw4ZJktauXauNNtqoe6tDTpX7PPr7z3bVz/YcldXja6v8chmJPeVrmkL29RV1LTLNRHt/TUX7QxLLku33hVypt/bUezrYK+92GbJGSXCsXfvS2u+p1AMAAAA9LqsG4ltuuUVTpkzR008/bZ9T/+6772rRokX6xz/+IUl65513NHXq1NytFHnndbtUW+XXyvqQfqgLamBVonKfOiSvowGK5SXQfm99LhyN25Pyka61Us+eegAAAKA3ZBXqjzjiCC1evFj33nuvFi9eLEk6+OCD9fjjj2vEiBGSpPPOOy9ni0ThGNQvoJX1Ia2oC2rboYlrXQ3Jk1LOqY8U/qC8jtrvpcS++nA0riiV+nalV+o5px4AAADoaRmH+kgkooMOOkj33HOPZs2a1RNrQgEbVB3Qh8vq0ibgL/sxcUZ9R0PyJKnMW/iV+q6OtEv9HHvq29ca6tlTDwAAAPSGjPfUe71effTRRz2xFhQB+1i7+tRQb1XqOw711pF2zaHCDfWRLo60kyRP8nPhKO337Sn3Jro1WlyGFGrI82oAAACA0pfVoLyTTz653UF5KH3WsXZplfr1ztvvmwu4/T4STQ7K6yTUW8faUalvH4PyAAAAgN6V1Z76aDSqP//5z3r++ec1ceJEVVRUpH3+1ltvzcniUHgG22fVt9jXlqcMyutIMZxTH40nj7Rz0H5v3Yt0aUfaNRPqAQAAgJ6WVaj/5JNP9JOf/ESS9MUXX6R9rqPp5ygNg/qlV+qjsbjdij+s0/b7wj+nPkz7fbdRqQcAAAB6V1ahfsGCBbleB4qEtad+RV1QpmlqRV1Qsbgpn9ulAZX+Dh9XFEfaRZ0daSfRft+RtCPtokEpGpY8vjyvCgAAAChdWe2pt3z11Vd69tln1dKSaL82TaqXpW5gv0RwD0XjWt8caT2jfqMyuVwdd2lYg/IKuVLv5Jx6H+33nUqr1EtU6wEAAIAellWoX7t2rfbdd19tueWWOuSQQ7RixQpJ0plnnqnp06fndIEoLAGvWzUVicrrirqglq/vevK91FqpD8cK94z3iKMj7Wi/70xrpT7x/VaQs+oBAACAnpRVqL/44ovl9Xq1dOlSlZe3TjyfOnWqnnnmmZwtDoXJnoBf39J6Rn0nQ/Kk1kF5ktQcKcxqvbMj7TinvjNpg/IkKvUAAABAD8tqT/1zzz2nZ599VkOHDk27vsUWW2jJkiU5WRgK1+DqgD79vj5RqXdwRr2UOArO7TIUi5tqCcfUL+DtjaVmxEn7PXvqO9em/T5IqAcAAAB6UlaV+qamprQKvWXdunXy+zselobSkHpW/bIfuz6jXkqcilDuLexheVZQ93k6br+3zqmPxmi/b48V6oOGFJeo1AMAAAA9LKtQv8cee+iBBx6w/24YhuLxuG666SbtvffeOVscClPqBPxl65Pt911U6qXUs+qjPbe4bsik/T5Mpb5dVqiXpKBhUKkHAAAAelhW7fc33XST9t13X7377rsKh8O69NJL9emnn2rdunV6/fXXc71GFJhNkmfVL/+xRSvWJ86o76r9Xir8Y+2soO5x0X6frYAnYP+52TBUTqUeAAAA6FFZVeq32WYbffHFF9p999115JFHqqmpScccc4w++OADjRo1KtdrRIEZnGy//2R5naJxUx6XoYFVgS4eJZUlj7Ur1FAfzaD9nlDfPpfhSj+rnko9AAAA0KOyqtRLUnV1ta688spcrgVFwtpT3xBKtNEP6V8mdydn1FusSn1LEbfft1bq2VPfkTJPmVqiLWoxXOypBwAAAHpY1qF+/fr1evvtt7Vq1SrF4+lVy1NPPbXbC0PhGtQvvSrvpPVeKp72e4606560Sj2hHgAAAOhRWYX6f/7znzrppJPU2Niofv36yTBaq7SGYRDqS1yF36N+AY/qg4mKe1dn1FvKCnj6vWmadlC3gnt72FPftbRj7Wi/BwAAAHpUVnvqp0+frjPOOEONjY1av369fvzxR/tj3bp1uV4jCpA1AV/q+jg7S4U/8TuklgIM9bG4KTPZUe/rpFLv83CkXVdaK/W03wMAAAA9LatQv3z5cl144YXtnlWPvsHaVy85O85OSj3SrvBCfTTeGtI731PPkXZdoVIPAAAA9J6sQv2BBx6od999N9drQREZnBLqHe+pt9rvI4U3KC81pHe6p95F+31X0kI9lXoAAACgR2W1p/7QQw/VJZdcos8++0zjx4+X1+tN+/wRRxyRk8WhcA3KJtTb0+8Lr1IfiaaG+k6OtEu230eitN93JK39nko9AAAA0KOyCvVnn322JOnaa69t8znDMBSLFV5oQ25ZlXq3y2gzDb8jhXxOvXVEncdlpA1+3JAV+CNxKvUdoVIPAAAA9J6sQv2GR9ih77EG5Q3qF5Cnk3b1VAVdqXdwnJ2U2n5Ppb4jaUfaRZqlWERye7t4FAAAAIBsZLSn/pBDDlFdXZ3999/+9rdav369/fe1a9dq6623ztniULh2Hrmxjtp+iH65/5aOH2MNymsKF96e+tZQ33GVXpK8dvs9v9jqSFqlXpJCDXlcDQAAAFDaMgr1zz77rEKhkP33G2+8Me0Iu2g0qsWLF+dudSluuOEGTZo0SeXl5erfv7+jx0ybNk2GYaR9HHTQQT2yvr7G73Hr9hMm6NiJQx0/pryAp99blXdrz3xHfMnQH6VbpUN2qLeq88G6Tu4GAAAA0B0Ztd+bptnp33tSOBzWlClTtOuuu2r27NmOH3fQQQfpL3/5i/13v9/fE8uDA6XQfm99Pkz7fYesUN/s8SUusK8eAAAA6DFZ7anPh5kzZ0qS5syZk9Hj/H6/Bg0a1AMrQqbKvNagvMJrv7eOtPN00X5vzQ+g/b5j5d5ySamVekI9AAAA0FMyar+3Wtg3vFbIXnrpJQ0cOFBjxozReeedp7Vr13Z6fygUUn19fdoHcqOgK/VRZ5V6q/2ec+o71tp+n/h+U6kHAAAAek7G7ffTpk2zW9iDwaDOPfdcVVRUSFLafvtCcNBBB+mYY47R5ptvrq+//lpXXHGFDj74YL355ptyW4FjA7NmzbK7ApBbFf7knvpI4YX6aDy5p95h+30kTvt9R9LOqZeo1AMAAAA9KKNQf9ppp6X9/eSTT25zz6mnnur4+S6//HL97ne/6/Sezz//XGPHjnX8nKlOOOEE+8/jx4/Xtttuq1GjRumll17Svvvu2+5jZsyYoV/+8pf23+vr6zVs2LCsXh/pCvmc+rDTI+1ov++Svaee6fcAAABAj8so1KcOnMuF6dOna9q0aZ3eM3LkyJy93siRIzVgwAB99dVXHYZ6v9/PML0eUu5NVOrD0bhicVNuV+Fs3Whtv+/iSDva77vUeqRd8kKI6fcAAABAT8nroLza2lrV1tb22ustW7ZMa9eu1eDBg3vtNdHKOqdeSgzLqwp487iadNaRdp4u99QnK/WE+g7ZoV7J94j2ewAAAKDHZDQoL5+WLl2qhQsXaunSpYrFYlq4cKEWLlyoxsZG+56xY8dq/vz5kqTGxkZdcskleuutt/Ttt9/qhRde0JFHHqnRo0frwAMPzNeX0af5PS5ZxflCG5ZnhXTHe+o50q5D5Z7k9HszGeoZlAcAAAD0mKI50u6qq67S3Llz7b9PmDBBkrRgwQJNnjxZkrR48WLV1SVafd1utz766CPNnTtX69ev15AhQ3TAAQfouuuuo70+TwzDULnPo8ZQVE0FGuq7ar/30H7fpTJvolIfNKMyJRlU6gEAAIAeUzShfs6cOV2eUW+ardXTsrIyPfvssz28KmSqzOdWYyhacGfVW5X3ro+0o/2+K1b7vSkpaBgqo1IPAAAA9Jiiab9HaSjUs+rtSr3HWft9lPb7DgXcAfvPLYbBnnoAAACgBxHq0avKkhPwC+1YOzvUdzGR3wr9YSr1HXK73PK7E1tcWlwGe+oBAACAHkSoR6+yKvWFFuqdnlNvhX7a7zvXeqydi0o9AAAA0IMI9ehVFf7EGIeWSGHtqbfa6Z2238dNKRanBb8jraGeSj0AAADQkwj16FWF3n7f5ZF2KaGfan3H7FDvMqRwoxQvrO83AAAAUCoI9ehVhTooL+z0SLuUPfeE+o6lVeolqvUAAABADyHUo1eV+RLt9wVXqY8mWuk9XVXq3amVetrvO2KHeo8vcYF99QAAAECPINSjVxXqoLyIw0F5bpchd7JaH6VS3yE71PvKExeo1AMAAAA9glCPXtXafl9gg/Li1p76ztvvpdYWfY6165gV6pu9yTPrQw15XA0AAABQugj16FVlyVDfVGCV+nCy/b6rSr0keV2Je2i/75hdqbdCPe33AAAAQI8g1KNXlXsLc1Ce0/Z7qXUCPoPyOtZmTz3t9wAAAECPINSjV5Xbg/IKq/0+4nD6feo9hPqOlXmTod6d+H4rWJfH1QAAAACli1CPXlVWsIPynLffe2i/75JdqbdCPZV6AAAAoEcQ6tGr7EF5kUIL9c7b732033ep3JOYet/iSny/2VMPAAAA9AxCPXpVeaGeU2+Feo+DPfVW+32UUN8Ru1JvJLczUKkHAAAAegShHr2q9Ui7wgz1zo60S1bq47Tfd6Q11CcvUKkHAAAAegShHr2q3N5TX1iD8sLJ/fHWfvnOeKxQT6W+Q3aoV/I9olIPAAAA9AhCPXpVoQ7Ki2bQfu9j+n2X7FBvJt8jKvUAAABAjyDUo1dZe+pD0bhiBdS+ntmRdrTfd6U11Cc7MqjUAwAAAD2CUI9eZbXfS4U1Ad86ns7nYPq9l/b7LtmhPp4M9VTqAQAAgB5BqEev8ntcsgaiN4cy21f/3bpmzf9gWY9U+MNR50faeWm/71JrqA8nLlCpBwAAAHqEJ98LQN9iGIbKvW41hWMZ76u/5slP9cKiVdqo3KfJYwbmdF1WQPdk0n5PqO+QHepjQZmSjFCDFI9LDgYRAgAAAHCO/8JGryvL8qz6JeuaJUkr64M5X1M0nkX7fYw99R0p8yZCfcyMKyJJMqVwYz6XBAAAAJQkQj16nX1WfSSz9vu1jSFJUlMo93vxIxm033tov++SVamXpBaPL/EHWvABAACAnCPUo9eVZ3GsXSQW14/NiZpvU4Z78Z0IZ3SkHe33XfG6vPK4Eh0ZLYF+iYsMywMAAAByjlCPXpdNqP+xKWz/uTGc+1Cf1ZF2tN93yqrWN/srExeo1AMAAAA5R6hHr7POqm/JINSvaWwN9bmu1MfipqyB+l4Hg9wYlOeMPSzPX5G4QKUeAAAAyDlCPXpdWRaV+jXJ/fRS7vfUp4ZzJ+33HGnnTLmnXJLU4kv8L5V6AAAAIPcI9eh1re33zivua5taQ31jjiv1aaGe9vucsSv1yUn4CtblcTUAAABAaSLUo9fZ0+8zqdQ39Fz7fWo4p/0+d1pDfSBxgUo9AAAAkHOEevS6Mm/ynPpIBqG+KbX9vmcq9R6XIZer60o9R9o5Y4d6jzdxgT31AAAAQM4R6tHr7Pb7DMJ5WqU+gwq/E+HkGfUeB633UuqRdrTfd8YO9e5kqKdSDwAAAOQcoR69LptBeWt7sFIfTY6+t9rqu2Ltuw9Tqe9Ua6hPdGZQqQcAAAByj1CPXmdX6jNov1+bcqRdTw3K8zkN9ckJ+VFCfafsUO9KfL+p1AMAAAC5R6hHr8tqUF5jeqXeNHPX+m613zuu1Ltov3eiNdQntzVQqQcAAAByjlCPXlfuSw7Kc3iknWmaaZX6uCkFI7mrkluVeq/H2Z566z4G5XWuLHmUXYuRfF9DDXlcDQAAAFCaCPXodZlW6uuD0Tb713PZgm9V3J0cZydxpJ1TdqVeyY6GEOfUAwAAALlGqEevy3RQ3tpk632l36OK5GNzOSzP2hvvfFAe7fdOtIb65C8/aL8HAAAAco5Qj17X2n7vLNSvSbbe11T6VOFPPDaXlfpwpu33nFPviBXqm83k9yrUIOVwFgIAAAAAQj3ywG6/dzj93qrUD6j0q9Kf2S8EnLDb7zOs1FsD9tA+u1IfT4Z6MyaFm/K4IgAAAKD0EOrR68q8Vvu9s2r7mqZkpb6itVKfy/b7SJbt99b59mifHepjIcngWDsAAACgJxDq0eusSn0wElfMQTBe05Cs1Ff5VeFPPDa3g/KsUE/7fS7ZoT7aIgX6JS6yrx4AAADIKUI9ep21p15y1oK/tikZ6it8dvt9biv12bXfR2i/71S5p1xSMtT7k6GeSj0AAACQU4R69LqA1yXr6HInLfhrGhLt9wOq/PYvBHqmUp9hqKf9vlNU6gEAAICeR6hHrzMMw95X7+SseqtSX1PhT9lTn8tBeYlQ78v4SDsq9Z1JC/V2pZ6z6gEAAIBcItQjL8ozOKs+9Ui7yuSe+iaHQ/acsKbYZ7ynnvb7TlmhPhKPKOqrSlykUg8AAADkFKEeeZHJWfVrUo6064lz6q099Z6MK/W033emzFtm/7klUJH4A3vqAQAAgJwi1CMv7LPquwj1oWhMDcFEgB9Q2ToorzmHoT6a9Z76uEyTYN8Rn8snl5F4r1q8iaF5VOoBAACA3CLUIy/KfM7Oql+bbL33uAxVl3lTKvU9sac+s/Z705SjI/n6KsMwWvfV+5JVeyr1AAAAQE4R6pEXdqW+iyPt1qbspzcMI2VQXg731Gd5pJ1EC35X7FDvDSQuhBryuBoAAACg9BDqkRdlXmd76lP300vqkUF59pF2nsxDfZgJ+J1qDfW+xAXa7wEAAICcItQjL5xOv7dCfU0y1PfoOfWuzNrvpdb9+GifHerdyVBP+z0AAACQU4R65IUd6rsI59ZxdgMqEqGwsgfa7yMZtt8bhiFP8hcAtN93rjXUJ75vCnJOPQAAAJBLhHrkhT0or8s99cn2+6pEpb51T33uB+U5bb+XUo+1o1LfGSvUN7sS328q9QAAAEBuEeqRF06PtFvblByUl6zUV6Tsqc/VcXKRDI+0S9xrVeoJ9Z2xK/Wu5HvLnnoAAAAgpwj1yAtrb3xXR9q1HZSXeJxpdj0536lMj7STJJ/HqtTTft8ZO9Rbb22oPvHNAwAAAJAThHrkhfNBea1H2klSmdcta55droblWcHck0Gl3uOi/d4JO9RbF+JRKdLS4f0AAAAAMkOoR144bb/fsFJvGIYqfLndV59V+70n8ZsFjrTrnB3qzZik5G9j2FcPAAAA5AyhHnlR5uv6nPp43NS65J56K9RLqcPyclWpt0K98/Z76xcAUdrvO2WH+liL5O+XuMi+egAAACBnCPXIi3Jv19Pv17dEFIsnQvPGyUF5klSeHJaXs/b7aOI1fJlU6mm/d6TcWy5Jaom2SIFkqKdSDwAAAOQMoR550dp+33Ewt46zqy7z2oPppNyfVR+m/b7H2JX6aGqlnrPqAQAAgFwpmlB/ww03aNKkSSovL1f//v0dP+7zzz/XEUccoerqalVUVGjHHXfU0qVLe26hcKTMwaC81clQbw3Js1h76nNVqY/GE8Hck0X7fSRKqO9MWqinUg8AAADkXNGE+nA4rClTpui8885z/Jivv/5au+++u8aOHauXXnpJH330kX7zm98oEAj04ErhhHWkXWeD8tY2tt1PL7Xuqe9qcr5TWbXfW3vq4+yp70z7lXpCPQAAAJArnnwvwKmZM2dKkubMmeP4MVdeeaUOOeQQ3XTTTfa1UaNG5XppyILVft/koP1+wAaV+srknvqcD8rzZBLqjbTHon12qI+kVuob8rgiAAAAoLQUTaU+U/F4XE899ZS23HJLHXjggRo4cKB23nlnPf74450+LhQKqb6+Pu0DuWe13wcjccU7qHav6aJSn6v2+6z21CfvDdN+36l2K/W03wMAAAA5U7KhftWqVWpsbNRvf/tbHXTQQXruued09NFH65hjjtHLL7/c4eNmzZql6upq+2PYsGG9uOq+w6rUS1JLBxPw1zYl99RXpIf6XA/K69aRdrTfd6rdPfW03wMAAAA5k9dQf/nll8swjE4/Fi1alNVzx5PDz4488khdfPHF2n777XX55ZfrsMMO0z333NPh42bMmKG6ujr747vvvsvq9dG5gKc11He0N351Q7JSX7XBoDy7Up+bPfXWWfOZVeppv3eCSj0AAADQs/K6p3769OmaNm1ap/eMHDkyq+ceMGCAPB6Ptt5667TrW221lV577bUOH+f3++X3+zv8PHLD5TJU7nOrORzrcFheR5V6ez8+7fcFr/1KPUfaAQAAALmS11BfW1ur2traHnlun8+nHXfcUYsXL067/sUXX2j48OE98prIjBXqmyPth/M1HQ7KK5z2+0iM9vvOWKE+GAsq7uuXaA2iUg8AAADkTNFMv1+6dKnWrVunpUuXKhaLaeHChZKk0aNHq7KyUpI0duxYzZo1S0cffbQk6ZJLLtHUqVO15557au+999Yzzzyjf/7zn3rppZfy9FUgVVdn1Xd1pF2uBuVZwTyrI+1ov++UFeolKegLqFxiTz0AAACQQ0UT6q+66irNnTvX/vuECRMkSQsWLNDkyZMlSYsXL1ZdXWtr79FHH6177rlHs2bN0oUXXqgxY8bo0Ucf1e67796ra0f7yr0dn1XfHI7aYb+mg0p9Ls6pj8dNxZLD7jzsqc+5gCdg/7nZ40+Eeir1AAAAQM4UTaifM2dOl2fUm2bbVugzzjhDZ5xxRg+tCt3RWaXeqtL7PS47xFsqcth+H4m3hvJs2u/DtN93ymW4VOYpU0u0RS0eb+IilXoAAAAgZ0r2SDsUvnI71LcN56376f0yjPSwXeFPPC4X7fepe+KzGZRH+33X7GF57uQvZ6jUAwAAADlDqEfeWKG+vfb7NfZ+el+bz+VyUF4kmlqpd/7j4KP93jE71LuSxxjGwlIkmMcVAQAAAKWDUI+8KfMlw3m77ffJ4+wq2x4vaLffh2OKx7vX/m6FcrfLkNvlvP3eQ/u9Y3aoT+24oFoPAAAA5AShHnlT7rUq9R2339dUtK3UV/ha99g3R7o3LM86o96TQaCXUo+0o1LfFTvUx0KSrypxkX31AAAAQE4Q6pE3nQ3Ks9vvq9pW6gNel6wM3t0W/GgWx9lJrUP12FPfNTvUR1ukQL/ExVBdJ48AAAAA4BShHnlT3tn0+6ZEqG+vUm8YRs7Oqrcq7V5PpqHeqtTTft+VtFDvt0J9Qx5XBAAAAJQOQj3yxgrm7Q7Ka0i039e2U6mXUs6qD+Wm/T6T4+wS91t76qnUd6XdSj3t9wAAAEBOEOqRN2XJPfXt7Ytf22TtqW8/1OeuUp+otGcy+T5xP9PvnWq/Uk+oBwAAAHKBUI+8aT3Srr1Bedae+rbt91LKBPxctd9nGOp9Huucetrvu0KlHgAAAOg5hHrkTUeD8qKxuH5stvbUd9R+n3hsUzu/EMhEJMv2e4+L9nunqNQDAAAAPYdQj7wpTx5Nt2GoX9cclmlKhiFtVO5t97HWsXa03xc+KvUAAABAzyHUI29a2+/TQ/3aZOv9xuU+eToI2zlrv49m137vpf3eMSvUN0ebUyr1HGkHAAAA5AKhHnljt99H0oO5FeprKtvfTy9JFcn2+8ZuTr+3Ku0Zn1Pvso60o1LflTJvaqW+OnGRSj0AAACQE4R65I19Tv0GwXxNY2Ly/YDK9vfTS7mr1Ft74j0ZH2lnpD0eHWNPPQAAANBzCPXIm3Jv+3vqrVBf00mor7T343cv1Eez3VPvoVLvVHqor0pcpFIPAAAA5AShHnljtd+3RGKKx1v3ptvH2XXafm8NystN+33GR9q52VPvVLmnXNIGg/Ko1AMAAAA5QahH3lj74iUpGG0N52sdtN9X5vicep8nwyPtmH7vmF2pj6S031OpBwAAAHKCUI+8CXhaQ31qC77dfl/hpFLf3T312R5plzynPkqo70q7R9pRqQcAAAByglCPvHG5DJV52x5rt7bJar/vbFBe4nG5qtR7XFm238dpv+9KWvu9VamPBqVoOI+rAgAAAEoDoR55ZU/ATw31jo60y037fTTL9nurUk/7fddSj7QzfVWtnwg15GlFAAAAQOkg1COv7LPqk1PsTdPUaidH2vlyMygv2/b71j31pkyTan1nrPZ7U6aCikreisQnQnV5XBUAAABQGgj1yCurUm+13zeGovY+9d4clJftnvrEcxDqOxNwB+w/p+2rZ1geAAAA0G2efC8AfVuZL/2seus4uwqf267it8faU98SiSkWN+V2ZdY+b4lEu3eknSRF43H5+P1Yh9wut/xuv0KxUOu++oYV0g8fSa6Ov8dIUT5A6jc436sAAABAASLUI6/Kk4PymiOJUG8dZ1fTSZVeat1TLyVa96sC3qxe3xp053Vnd6SdJEWiptTx9n8o0YIfioUSx9pZlfonf5HfRRUVQzr7BWnTifleCAAAAAoMoR55ZQ/KS7bR28fZdTIkT5L8Hpc8LkPRuKmmUCzrUB/Osv3ek9IZEGZYXpfKPGVaH1qfqNT/5FSpfoUU797WiT4j1CBFmqRFTxHqAQAA0AahHnlVtsH0e6v9vrP99JJkGIYq/B7VtUS6dVZ9tu33hmHI53YpHIszAd+BtLPqf3Jq4gPOvP9AoqthyRv5XgkAAAAKEBuBkVf2oDy7/d4K9V33s+diWF7roLzM9+Rbj4kyKK9LaaEemRm+W+J/l78nRXj/AAAAkI5Qj7wqtwflpbffd1WpTzw28QuB7oX6RCD3eTL/UfAkq/u033eNUN8NG4+UKgdJsbC07J18rwYAAAAFhlCPvCrfoP1+bVNyT31F15V6a1het9rvk4Hc48r8R8Fq2af9vmuE+m4wDGlEslpPCz4AAAA2QKhHXm14Tv2ahmT7fVXXlXqr/d76hUA2utN+76P93jEr1DdHm/O8kiJlteB/+1p+1wEAAICCQ6hHXrU5p96u1Hcd6q2z6rtXqaf9vjdQqe8mK9Qve0eKhvK7FgAAABQUQj3yasP2+zUNicBSW+W8/b47e+qzPdIu8ZhEpZ72+64R6rupdoxUPkCKBqXvP8j3agAAAFBACPXIq9bp91GFo3HVBxMB3UmlPrfT79lT35PKvIT6bjEMafikxJ9pwQcAAEAKQj3yqszbWqm3huS5XYaqy7xdPrZ1UF72e+qt/fCebPbUJ1v22VPfNSr1OWC14C95Pb/rAAAAQEEh1COvrCPtWsIx+4z6mgqfXK6uQ3YuK/W+LCr1nuQa2VPftXJPuSRCfbdYE/CX/keKZf9vHgAAAKWFUI+8KrPOmg9H7TPqaxycUS+1tu43hvO1p572e6fsSn2EUJ+1geOkQLUUaZJWfJjv1QAAAKBAEOqRV6lH2q1JVuoHVHY9JE/KzaC8bh1pR/u9Y7Tf54DLJW2W3Fe/hH31AAAASCDUI69Sp9+vTVbqBzis1Nvn1HdjT30kmgjk3anU037fNUJ9jlgt+EveyO86AAAAUDAI9cgre099JKbVDVaoz6xS351z6qPx7NvvrT31tN93jVCfI/awvDelePa/zAIAAEDpINQjr6xKvWlKy9cnAp/TPfWV/tb9+NkKR7Nvv/cm2+8jUUJ9Vwj1OTJoW8lXJYXqpJWf5Hs1AAAAKACEeuSVdaSdJH33Y7OkxPR7J3Kzpz779ntrYn40zp76rhDqc8TtkTbbOfHnbznaDgAAAIR65JnLZSjgTfwzXLo2EeoHVDmr1Ff4ut9+bx9p5+FIu55EqM8hzqsHAABACkI98s7aV18fTITzARWZDcoLRuKKZhGsTdO0q+xZDcqz2++p1HclNdSbJu9XtwxPGZYX5xdKAAAAfR2hHnmX2oIvSQOqnLXfl/tbH9cUznxoWCTlKDpPNkfacU69Y2XeRKiPmTFF4pE8r6bIDZkgecqklnXS6kX5Xg0AAADyjFCPvLOG5Vk2drin3u9x2wPustlXnxrGfVkdaZecfk+1tEtWpV6iBb/bPD5p2E6JP9OCDwAA0OcR6pF3qaG+X8Ajv8fdyd3prGF5zVlMwE8N9Vkdaeem/d4pr8srjyt5fCGhvvvYVw8AAIAkQj3yriwl1A9weJydpXVYXubt99aAO5chuV1ZHGlH+31GrGp9c7Q5zyspASOSof7b1xPnQQIAAKDPItQj76xBeZJUU+ms9d5S2Y1j7bpznJ0k+ZLt91Ha7x1hAn4ObbqD5PZJTauktV/nezUAAADII0I98q68O5X65LC8bI61sybmZxvqrfb7MO33jpR7yiVJLRFCfbd5A4lgL0lLXsvvWgAAAJBXhHrkXWqoz7RSX9GtSr0V6jNvvU88jvb7TFCpz7HUFnwAAAD0WYR65F1q+32mlfrutN9bFfbutt8T6p0h1OdY6rA89tUDAAD0WYR65F1ZWqU+s1Bf3o1BeZFutt+3VuoJVE4Q6nNs2E6SyyPVL5fWL8n3agAAAJAnhHrkXbm3NdTXZjwoL/HY7rTf+zzd21NPpd4ZQn2O+SqkIRMSf6YFHwAAoM8i1CPvulOpt/fUZ3VOfaLC7sniODupdS8+od4ZQn0PsFvw38jvOgAAAJA3hHrkXdqRdhX5GJSX7Z76xOOitN87QqjvASN2T/wvE/ABAAD6LEI98i7tSLuqbAfldWNPfZbt99YvA8JU6h0h1PeAYTtLhkv68Vupbnm+VwMAAIA8INQj76z2e5/bpSq/p4u701mV+mzOqbf31Gd5pJ2H9vuMlHkJ9TkX6CcN2jbxZ1rwAQAA+iRCPfLOqtQPqPTJMDIL2N0ZlBeOdfdIOwblZYJKfQ+hBR8AAKBPI9Qj78ZvWq1hG5fpkPGDM35s65F2mYf6aDKMe7I90s7DnvpMEOp7yPBJif9lAj4AAECflFmvM9AD+pf79Mole2dcpZe6O/2+m+33yan57Kl3xgr1zdHmPK+kxGy2qyRDWvul1LhKqhyY7xUBAACgFxVNpf6GG27QpEmTVF5erv79+zt6jGEY7X7cfPPNPbtYZCybQC91b1Bed9vvvbTfZ8Su1Eeo1OdU+cbSJuMSf15CtR4AAKCvKZpKfTgc1pQpU7Trrrtq9uzZjh6zYsWKtL8//fTTOvPMM3Xsscf2xBKRBxXd2FMfiXbzSDuPFeppv3eC9vseNHyStPIT6YP/J4Ua8r2a4mC4pFH7SP2G5HslAAAA3VI0oX7mzJmSpDlz5jh+zKBBg9L+/sQTT2jvvffWyJEjc7k05JFVqQ9F44rG4hntj+/uOfVU6jNDqO9BI3aX3r5P+ur5xAecGb67dPpT+V4FAABAtxRNqO+ulStX6qmnntLcuXM7vS8UCikUCtl/r6+v7+mloRsqUo7AawrFVF3uPKBH41b7fff21BPqnSn3lEsi1PeILQ+WdjxbqluW75UUh3Cj9O2r0tqv8r0SAACAbuszoX7u3LmqqqrSMccc0+l9s2bNsrsCUPi8bpd8HpfC0bgaw1FVl3sdPzZM+32volLfgzw+6dBb8r2K4lG3XLpta6l5jRSPS66iGS8DAADQRl7/S+byyy/vcJid9bFo0aKcvNaf//xnnXTSSQoEAp3eN2PGDNXV1dkf3333XU5eHz2ndVheZvvqc9V+H4ubiscJ9l0h1KNgVAxI/G88KgXX53UpAAAA3ZXXSv306dM1bdq0Tu/Jxf73V199VYsXL9a8efO6vNfv98vv93f7NdF7yn1urWvK/Kx6O9R7smy/T2nbj8Tj8rvcWT1PX2GF+kg8omg8Ko+rzzQKodB4/JK/WgrVSc1rEycIAAAAFKm8/ld1bW2tamtre/x1Zs+erYkTJ2q77bbr8ddC78u+Up+orvuybb9PeVwkZspPRu1UmbfM/nNLtEVVvqo8rgZ9XkVNItQ3rZYGbJHv1QAAAGStaDYSLl26VAsXLtTSpUsVi8W0cOFCLVy4UI2NjfY9Y8eO1fz589MeV19fr0ceeURnnXVWby8ZvaQiy7PqrUq9J8v9tKlt+9bxeOiYz+WTy0i8Z7TgI+8qkr9Qblqd33UAAAB0U9HUFq+66qq0yfUTJkyQJC1YsECTJ0+WJC1evFh1dXVpj/v73/8u0zR14okn9tpa0bsqurunPsv2e7fLkMuQ4mai/R6dMwxDZZ4yNUWaCPXIP0I9AAAoEUVTqZ8zZ45M02zzYQV6STJNs80e/Z/97Gdqbm5WdXV17y4YvabSn9jL3hTu3fZ7SfK4mYCfCYbloWBYw/Ka1uR3HQAAAN1UNKEe6EiFL1Gpz3RQXrib0++l1l8I0H7vDKEeBcOu1BPqAQBAcSPUo+hl234fzUGo9yYn4Edpv3fEDvURQj3yrNyq1NN+DwAAihuhHkWvMutBeYmW+dSj6TJl/UIgHKX93gkq9SgYtN8DAIASQahH0StP7qnP9pz67uyp99p76qnUO2GF+uZoc55Xgj6PQXkAAKBEEOpR9LI9pz4czV37PaHeGSr1KBhWqG+mUg8AAIoboR5FzxqU1xTO7px6bw7a75l+7wyhHgXDDvXrpFhmvxAEAAAoJIR6FL2sB+XFE0Hc68nFkXZU6p0g1KNglG8syZBkSi3r8r0aAACArBHqUfS63X7v6s6Rdrltv1/8Q4Nmv/aNgpHMug6KBaEeBcPlTgZ7sa8eAAAUNU++FwB0V0U3B+Xlpv2++6HeNE39z98/0KIfGvT+0h/1vydMkMuV/doKEaEeBaWiVmpeS6gHAABFjUo9il62lXprH3x32u9zuaf+0+/rteiHBknSUx+t0O3Pf9Ht5yw05d5ySYR6FAh7Aj7D8gAAQPEi1KPoVWR9Tn33j7Tz5LD9/h/vLZMkbbZxIvj+4cWvNP+DZd1+3kJCpR4FhbPqAQBACSDUo+hZ0+/Dsbi9T94Ju1LfjVDvy1H7fTga1xMLl0uSrj1ynM6bPEqSdNk/Pta735bOEC9CPQpKuRXqab8HAADFi1CPomftqZcya8G3grinAI60W7B4lX5sjmhglV97bFGrSw4Yo4PGDVI4FtfP/vqelq5t7tbzFwpCPQqK3X5PqAcAAMWLUI+i53G75E/ui28KZx7qC6H93mq9P/onm8rtMuRyGbp16nYav2m11jWFdcbcd1QfjHTrNQqBHeojhHoUANrvAQBACSDUoyRUZrGvvnX6fX7b79c2hrRg0SpJ0nE/GWpfL/d59KfTdtCgfgF9tapRP3/wfUVzdHRevlCpR0GxKvXNhHoAAFC8CPUoCdawPKfH2pmmmbKnPr/t908s/F7RuKnthlZri02q0j63Sb+A/nTaDirzuvXql2t0zT8/lWl2f9J+vhDqUVBovwcAACWAUI+SUJHhsXbReGsw7taRdp7ut98/+n6i9f7YiUPb/fw2m1br9hO2l2FI/++tpZrzxrdZv1a+EepRUGi/BwAAJYBQj5JQmRyW5zTUp4Zwr6sbe+pd3Wu//+z7en36fb18bpcO33ZIh/cdOG6QLj9orCTpuv/7zG7XLzaEehQUK9SH6qVIML9rAQAAyBKhHiUh0/b7SDSlUt+N9nufp3vt91aVft+tBmqjCl+n9/5sz5GausMwxU3pF3/7QIt+qM/qNfPJCvXBWFBxs7jnA6AEBPpLrsT/d7CvHgAAFCtCPUqCdVa900p9OFlZNwzJ7erOnvrEY8PRzANqJNZ6Nv1xHbTepzIMQ9cdtY12HVmjxlBUlz/6ccavmW9WqJekYJTKKPLMMFL21RPqAQBAcSLUoyRYZ9U3hZ1Nv0+dfG8Y3R+UF41nHupfXrxaaxrDGlDp055b1jp6jM/j0q1Tt5Mkfbhsveqai+uYu4AnYP+5Odqcx5UASeyrBwAARY5Qj5KQ8aA8a/J9N6r0Usr0+2jm7fdW6/1R22+a0bF6g6vLNLK2QqYpvf3tuoxfN59chot99Sgs5VaoZwI+AAAoToR6lITKDEO91X7fncn3Umv7faaD8n5sCuv5z1dK6njqfWd23rxGkvSf/67N+LH5RqhHQeFYOwAAUOQI9SgJrYPyMm+/7w67Uh/PrFL/z4++VyRmatyQftpqcL+MX3eXkRtLkv7zTXFV6iVCPQoMoR4AABQ5Qj1KQqbt91ao93Uz1Hvs9vvMKvX/eC/Reu9kQF57rEr9p9/XqT5YXPvqCfUoKNae+ubi63oBAACQJE++FwDkgn1OfTizUN+d4+wkyZdF+/0XKxv00bI6eVyGjtiu47PpOzOoOqARNeX6dm2z3v12nfYZu0lWz5MPVqi/5OVL5HN3fowfEvr7++vsbc/WgcMP7NZgR7SDSj0AAChyhHqUBOtIO8fn1CcH5Xly1H4fziDUP5qs0u8zdqBqKv1Zv/bOm9fo27XN+s9/iyvUj9l4jD5e87HWh9bneylFY1XzKl3y8iV6dPCjumLnK7R59eb5XlLpqGBQHgAAKG6EepSEbNvvc7Wn3pqm35VoLK75HyTOps9mQF6qnUdurHnvfqe3imxf/W92+Y1OHHuiYnFn8w8gvfTdS/rTx3/SWyve0jFPHqNp46bp7PFnq9xbnu+lFT/OqQcAAEWOUI+S0BrqMxuU5+tm+32m0+9f/WqNVjWEtHGFT3uPGdit1955ZGJf/SfL69QYitonABQ6l+HSlhttme9lFJWtarbSYSMP06y3Z+nV5a/qTx//SU/99yldtuNl2mezfWjJ747USr1pSryXAACgyDAoDyUh0z314eS58jmbfu8w1Fut90dsN0S+bh6nt2n/Mg3buEyxuKl3i+y8emRuWL9h+uO+f9Qde9+hIRVDtKJphS566SKd/8L5Wlq/NN/LK15WpT4alMJN+V0LAABAFgj1KAn5br+POGi/r2uO6LnPEmfTZzv1fkP2efVF1oKP7BiGoX0220ePH/W4zh5/trwur15b/pqOfuJo/XHhHxWMBvO9xOLjq5CsbQzsqwcAAEWIUI+SYIX6SMxUKNp1C340ngj1nm6233syaL//50ffKxyNa+ygKo0bkvnZ9O3ZefPkefX/5TiuvqTMU6YLf3KhHjviMe06eFeF42Hd8+E9uubNa/K9tOJUbrXgs68eAAAUH0I9SoI1/V5ytq8+kmy/7+459b4M2u8ffb/1bPpc7YHeJbmv/qNldWp2uPUApWNE9Qjdu/+9mjlppiTpteWv5XlFRYoJ+AAAoIgR6lES3C5DZd7kvnoHLfjhXm6//3Jlgz5Yul5ul6Ejt9+0W6+ZauhGZRpSHVA0buq9JT/m7HlRPAzD0EEjDpIk1YXqVB+uz/OKihBn1QMAgCJGqEfJsFrwnZxVb++p7+awOqeD8u595b+SpP22GqjaquzPpt+QYRh2tf4//2VffV9V7i1XTSDx7+C7hu/yvJoiZIX6ZtrvAQBA8SHUo2RU+J1X6lsH5fX8kXbL17fo8eTZ9OdNHt2t12vPziOT++q/YV99X7ZZv80kSd/VE+ozVsGeegAAULwI9SgZ1r76prCDPfXJdnmvq+fb7+9/5b+Kxk1NGlWj7Yf179brtceagL/wu/VqcfC1ozQNqxomiUp9VthTDwAAihihHiWjMoNj7Vrb77tZqU+274c7qNSvbQzp7+8kzhA/vweq9JI0vKZcm/TzKxIz9cFS9tX3VVaoX9rAmfUZY089AAAoYoR6lAyr/T6jPfXdHpSX+KVAtINQ/5fXv1UwEtd2Q6u12+iabr1WR1L31b/FefV9FpX6brAr9WxhAQAAxYdQj5JRkVGlPjdH2lnt+3FTisXTW/AbghHNffNbSYm99Lk6xq49Vgs+59X3XZtVsac+a1TqAQBAESPUo2Rk1X7f3VCfMj1/w2F5/++tpWoIRjV6YKUO2HqTbr1OV6xheR98t17BCPvq+yKrUr+qZZVaoi15Xk2RSZ1+H+/8JAsAAIBCQ6hHyWg90s7JoLzEf7h7cjT9PvU5JSkYiWn2a99Iks7da5Rcrp6r0kvSyAEVGlDpVzga18Lv1vfoa6EwVfurVeWrkiQta1iW59UUmfJk+308KgXX53UpAAAAmSLUo2Rk1H4fTU6/z1H7vZQ+Af+R95ZpTWNIm/Yv05HbD+nWaziR2FefPNqO8+r7JMMw2FefLY9P8lcn/syxdgAAoMgQ6lEyKnyZn1Pf3T31Lpchtyv9rPpoLK57X/5akvSzPUd2+xcHTu2cHJbHefV9l72vnlCfOWtYXjOhHgAAFBdCPUqGXakPdx3qw/ae+u63xVvPEY4mnvOfH32vZT+2qKbCp+N3GNbt53dql80Tlfr3l/6oUJR99X0RlfpuYFgeAAAoUoR6lIzWQXldB9poslU+ddBdtqxKfDRuKh43dfdLiSr9GbtvrrJk90BvGD2wUjUVPgUjcX20rK7XXheFwz6rvp6z6jNmH2tHqAcAAMWFUI+S0TooL4Pp967chfpILK4XFq3SFysbVeX36ORdhnf7uTNh/P/27jw+qvre//jrzGRmsq9kTyCshh0UQcCKFioqoiKKV6lCtfVa0Qr0+hNt1WvrUrX2tqIVsa3WuqJWAetSRKQu7BoE2QUMZCEBsm8zmTm/P5IMREJIQmBykvfz8ZhHMmfOnPOZyUngPd/NMBjZs2Fcvbrgd0VqqT8J/pZ6db8XERERa1Gol04jzNXyMfX+7vdB7dv9/ukVuwD48egeRIU4TvrYrXWOf1y9JsvrirpH1o2pz6vIw+PzBLgai1FLvYiIiFiUQr10Gi1dp97rM8k+XAlAiCPopM/b0FL/6c6DZO0rxhVk48axPU/6uG3RsF79+r1FjZbYa86G7w5TUFp9KsuS0yQ+JJ5gezBe00teeV6gy7EWtdSLiIiIRSnUS6fR0u73736dy3eHKokOdTC2T9xJn7ch1D/36W4Apo1IJz7CddLHbYt+CRFEhzqo8nhPOK7e5zN58N0tTH1mFTf+fd1pqlBOJcMwSItIAyC7TOPqW8XfUq9QLyIiItaiUC+dhr+l3u3FNM0m9/H6TP60fCcAP/tBLyKCT76LfEP3+8MVbuw2g5vP63XSx2wrm81gZEb9uPpmlrarqfVyx+tZ/OWzPQBszilla17paalRTi2Nq28jzX4vIiIiFqVQL51GaP1M816fSU1t013P3/06l92FFUSHOrhhdPtMZHf0OvSXD00hPTa0XY7bVv5x9bubHldfWu1h5t/WsXRjLkE2g17dwgBYnJV72mqUU0cz4LeRQr2IiIhYlEK9dBphziPj45saV38qWumhcai/5fze7XLMk3FkXP1har83rv5AaTXTFqxi1e5DhDntPP+Ts7lz4hkALN2Yi8/XdA8HsY7uEXWT5e0v2x/gSiwmtL77fdVh8J54sk0RERGRjkKhXjoNm83wt9Y3tVb90o1HWulnjMlot/M2dL+/cEAi/RIj2u24bZWZFElkcBAVbi+bc490qd9VUMaVf/6CbflldAt38fp/j+YHfeO5IDOBCFcQOcVVbMguCmDl0h78LfUaU986obFA/WoYVVo9QkRERKxDoV46leNNluf1mTx5VCt9w/j79vDDzEQSI13MvbBfux3zZNhtx65Xv+G7w1y1YBU5xVX07BbGP38+hkGpUQAEO+xMHJQEwDtf5QSmaGk36ZF1oX5/2X58ZstWQBDAZofQ+okz1QVfRERELEShXjqVI5PlNQ71SzbmsPtgBTHt3EoP8PPze7P67vFkJkW263FPxtHr1f/7m3yue24NxZUehqVH89bPx9A9rvG4/yuGpQLwr015uI8zH4FYQ3JYMkFGEG6fm4LKgkCXYy0aVy8iIiIWpFAvnUqYq677/dEt9bVeH/OX7wLgZ+e1byt9A8Mw2v2YJ2NUz7pQ/9nOg9zy0gZqan2Mz0zglZ+NIjbMecz+o3vH0S3cRXGlh892KdBYWZAtiJTwFEAz4LealrUTERERC1Kol06lYbK8oyfKW7Ix199Kf8PojABVdnoNSIkkwhWE2+vDZ8I1I9J59vqzCHU2/YGG3WYweWgyoFnwOwPNgN9G/lCvD7ZERETEOhTqpVPxd7+vD/W1Xh/zPz61rfQdkd1mML5/AgC/GN+X300dTJC9+V/3y+u74P/7mwNNrh4g1qG16tvI3/1eLfUiIiJiHQr10qmE+ifKq5v9fsnGXPZ0sVb6Bo9dNZTP7rqAuT/q16LhAUPTougRF0qVx8tHWw+chgrlVNEM+G2kMfUiIiJiQQr10qmE14+pr6yppdbr8894f/N5vbtMK30DZ5CNtJjQE+9YzzAMf2u9uuBbW/dIrVXfJhpTLyIiIhZkmVD/0EMPMWbMGEJDQ4mOjm7Rc8rLy7nttttIS0sjJCSEAQMGsGDBglNbqARUw5j6cncti7Ny2Xuosr6VvkeAK7OGy4bWTbD2nx2FHK5wB7gaaauju9+bphngaixELfUiIiJiQZYJ9W63m6uvvpqf//znLX7O3Llz+eCDD3jppZfYunUrs2fP5rbbbmPJkiWnsFIJpIZ16kurPMz/+EgrfVgXa6Vvqz4J4QxKjaTWZ/KvTXmBLkfaKC0iDQODck85RTVFgS7HOkI1UZ6IiIhYj2VC/QMPPMCcOXMYPHhwi5/zxRdfMGPGDM4//3wyMjK4+eabGTp0KGvXrj2FlUogNXSx/9fXeew9VElsmFOt9K10+dC6LvhLsnICXIm0lcvuIiG0bqJETZbXCg0t9ZWHAluHiIiISCtYJtS3xZgxY1iyZAk5OTmYpsmKFSvYsWMHF1544XGfU1NTQ2lpaaObWIe/pb66bvb2m8/rpVb6Vpo8NAXDgHV7i9hfVBnocqSNGsbVa1m7VmgYU19TCp7qwNYiIiIi0kKdOtTPnz+fAQMGkJaWhtPp5KKLLuLpp5/mvPPOO+5zHnnkEaKiovy39PT001ixnKyw+onyAGLDnFx/jlrpWyspKphzesYBsHSjuuBbVcO4ek2W1wrBUWBz1H1fqcnyRERExBoCGurnzZuHYRjN3rZt29bm48+fP5/Vq1ezZMkSNmzYwBNPPMGsWbP46KOPjvucu+++m5KSEv9t3z51XbWSo2e4Vyt9210+rG7CvMXqgm9ZWtauDQxDk+WJiIiI5QQ08fzyl79k5syZze7Tq1evNh27qqqKe+65h7fffptJkyYBMGTIELKysvj973/PhAkTmnyey+XC5XK16ZwSeAkRwQAaS3+SLh6UzL2LN7Mtv4xt+aVkJkUGuiRppaNnwJdWCIuDslwtayciIiKWEdBQHx8fT3x8/Ck5tsfjwePxYLM17oxgt9vx+Xyn5JwSeINSI3n8qiEMTIki1KlW+raKCnVw/hkJLNtygCVZuWRepFBvNd0j6sbUK9S3kr+lXqFeRERErMEyY+qzs7PJysoiOzsbr9dLVlYWWVlZlJeX+/fJzMzk7bffBiAyMpJx48Zx55138sknn7Bnzx5eeOEFXnzxRaZMmRKolyGnmGEYXD0inQEpCqEn64phdbPgL87K1VrnFtTQUn+4+jDl7vIT7C1+6n4vIiIiFmOZpsz77ruPv//97/77w4cPB2DFihWcf/75AGzfvp2SkhL/Pq+99hp3330306dP5/Dhw/To0YOHHnqIW2655bTWLmJF4/snEOa0k1NcxYbvihiRERvokqQVwp3hxLhiKKopYl/ZPvrH9Q90SdagUC8iIiIWY5lQ/8ILL/DCCy80u8/3WxOTkpJ4/vnnT2FVIp1XsMPOxEFJ/PPLHBZn5SrUW1B6ZDpFhQr1rdKwrJ2634uIiIhFWKb7vYicfpfXd8H/16Y8PF7NRWE1mgG/DULrQ72WtBMRERGLUKgXkeMa2zuObuFODle4+WyXQo7VNEyWp7XqW0Hd70VERMRiFOpF5LiC7DYuHVK/Zv1XWrPeatRS3waa/V5EREQsRqFeRJp12bC6UP/vLQeodNcGuBppDa1V3wb+MfWFoFUfRERExAIU6kWkWcPTo+keG0ql28uLq74LdDnSCg2h/kDFAWq8NQGuxiIaQn1tNWgpQBEREbEAhXoRaZZhGMy6oDcAf/j3DnYcKAtwRYFVUunBXWuNSQNjg2MJc4RhYpJTpuETLeIMA0do3ffqgi8iIiIWoFAvIic0bUQ6558Rj9vr45eLNnbZmfC355cx6pGP+PlLGwJdSosYhqFx9W2hZe1ERETEQhTqReSEDMPg0alDiApxsCmnhD+v+DbQJQXEc5/uptrjY/m2AnYVWKNrtsbVt4FmwBcRERELUagXkRZJjAzmN5cPBGD+xzvZnFMS4IpOr0PlNSzZmOu/v2i9NUKyv6W+VC31LaZQLyIiIhaiUC8iLXbZ0BQuGZxErc9k7qIsamq9gS7ptHlt3T7ctT4iXEEA/PPL/ZYYhtCwVv2+cmt8CNEhHD0DvoiIiEgHp1AvIi1mGAa/vXwQ3cKd7DhQzv8t2xnokk4Lj9fHP+pn/r9v8gC6hbs4WO5m+daCAFd2Yv7u96UK9S0WWh/qKw8Ftg4RERGRFlCoF5FWiQt38dCUwQAs/M+3bPjucIArOvU+/Caf/NJquoW7uGxYClPPSgXg9XUdv0t798i6lvrc8lxqfbUBrsYi1P1eRERELEShXkRabeLAJK48MxWfCb9ctJFKd+cOiy98vheA6aO64wqyc82IutbvlTsKyS+pDmBlJ5YQmoDT5qTWrCWvIi/Q5ViDQr2IiIhYiEK9iLTJ/ZMHkhQZzN5DlTz6/rYWPaegrJrff7idm19cb5n17jftL2H9d0U47AbTR9W1eveKD2dkRiw+E97c0LG7tdsMG2kRaYBmwG8xLWknIiIiFqJQLyJtEhXi4LGrhgDw91Xf8fmu4wegbwvLmffW15z7uxU8tWIX/95ygMuf+pw3N+w/XeW22Qtf7AVg0uBkEiKD/dunnV3XWv/6+n34fGYgSmsxjatvJbXUi4iIiIUo1ItIm53XL97fev3/3vya0mpPo8fX7z3MT/++nvFPrKybPd7r48zu0YzpHUeVx8v/vLGRO9/YSJW7bbPor959iAUrv+U/Owopr2n/IQAHy2tYWr+M3cyxPRs9dsngJCJcQew7XMXq3R17QjWtVd9KYUdNlOfr+CsciIiISNcWFOgCRMTa7rmkP5/uPEj24UoefHcLj1w5hGVbDrDwP9/yZXaxf78fDUjkv8/rxYiMWHw+k6dX7OL/PtrBGxv28/X+Ep6efiZ9EsJbdM4vs4v4/Yfb+eLbI2HaZsCAlEjOzojl7IxYRmTEkBAR3MxRTuzVNdm4vT6GpUczLD260WOhziAmD0vhlTXZvL5+H2P6dDupc51KCvWt1DD7va8WqoshNDag5YiIiIg0R6FeRE5KmCuI3189lGsWrmLR+v18vusQOcVVADjtNq48M5Wf/qBXo8BusxncPr4vZ2XEcMdrWWw/UMZlT33Gw1MGc8Xw1OOe65vcEp749w4+3la3lJzDbnBe33h2FJSx73AVm3NK2ZxTyvP1E9tlxIX6Q/7EgUlEhTpa/Lo8Xh//WF23jN1PxmY0uc81I9J5ZU0272/O5zeVnlYd/3RqmAE/u6zjz9bfIQQ5ITgKqkvqxtUr1IuIiEgHplAvIidtZM9YfnpuT577dA85xVVEhTj48TndmTEmo9nW8jG9u/GvX5zLHa9msWr3IWa/nsWaPYe4f/JAgh12/367Csr4v2U7+demutnb7TaDq85M4/bxfUiLCQUgv6SadXsP19+K2JZfyt5Dlew9VMkbG/bz5Mc7efOWMSRFtaz1/v3N+RSU1RAf4eLiQclN7jMkLYrMpAi25ZfxTlYOM8ZktPAdO70aWur3l+3HNE0MwwhwRRYQFl8f6gshvl+gqxERERE5LoV6EWkXv7zwDFxBdrqFO7l6RDphrpb9eUmICOaln47iT8t3Mv/jnby6dh9Z+0p4+rrhBNls/HH5Dt75KgefCYYBk4ekMHtCX3rFN+6qnxQVzOShKUwemgJASZWHL7OLWLfnMIuzctlfVMX1f13Dov8eTUyY84R1vfD5HgB+PKoHzqCmpx8xDINrzk7ngaVbeH3dvg4b6lPCUrAZNqq91RRWFZIQmhDokjq+sHg4tEuT5YmIiEiHp1AvIu0i2GHnfyae0abn2m0Gc3/Uj7MzYpj9WhZb80q5dP5nuGt91NbPLH/hgETmXtiPzKTIFh0zKsTBBWckcMEZCVw7sjtXLfiCnQXlzHxhHa/8dFSzHzp8vb+YL7OLcdgNrh2V3ux5rhiWyiPvbWNLXimbc0oYlBrV8hd+mjjsDpLDkskpz2Ff2T6F+pYIjav7Wqll7URERKRj0+z3ItJh/KBvPO/d8QNG9oyl0u2l1mdyXr94Fs8ay8IbRrQ40H9femwoL900iuhQBxv3FXPzP9ZTU3v8GfcblrG7dEjKCSfbiwlzMnFQEgCvreu4Y9YbuuBnl3bcGjsU/7J2CvUiIiLSsSnUi0iHkhgZzCs/HcUTVw/lzVtG8+KNIxn6vZnn26JvYgQv/GQkoU47n+86xB2vZlHrPXa5ssKyGt7dWDd2f2YLu9NfM6IuMC/OyqXa07bl+U617hF1k+VpBvwW0lr1IiIiYhEK9SLS4QTZbUw9K40RGe076/iw9Gieu2EETruND77J5563N2GaZqN9Xl1bt4zd8O7RLf4wYUzvONJiQiirruX9zXntWnN70bJ2raRQLyIiIhahUC8iXcrYPt148trh2AxYtH4/D7+31R/s3bU+Xqpfxq6lrfRQt0TftPrW+tfWdszQnB6pUN8qYfVr1av7vYiIiHRwCvUi0uVcNCiJ3105BIDnPt3Dnz/5FoD3N+dRUFZDQjPL2B3PVWelYRiwZs9h9h6saPeaT5Z/TH0za9WbpsnyrQeY9uwqfrN0CxU1taervI5HoV5EREQsQqFeRLqkaWen8+tJ/QF4/MPtvLT6O/8EeT8+5/jL2B1PSnQI4/rVddletL7jtYanhacBUOYuo6Sm5JjHv8ou4pqFq7np7+tZu+cwf/t8Dxf/6VPW7jl8ukvtGNT9XkRERCxCS9qJSJf10x/0orjSw1MrdvHrdzYD4LTbuHZk9zYd75oR6XyyvZA3N+xn7o/6EWTvOJ+bhjpCiQ+Jp7CqkPu/uJ9IZ91KAuXVtXydU8K+osq6/VIMenYLI6+4igMeLzcsgX6JEQxOjcJuMwL5Ek6v2mroFovDNEneuJC0yO6khqeSGpFKjCsGw+hC74WIiIh0aAr1ItKl/fLCfhRVunl5TV239EuHJhMf4WrTscb3TyQuzElBWQ2fbC9kwoDE9iz1pPWN6UthVSHLs5cf85gz+sj32R4gDJz19/fUwJ7dp6PCDiYivO5r1vxGm0OCQkgNTyUtPI2U8BTSItI4O+lsMmMzA1CkiIiIdHUK9SLSpRmGwW8uH0St1+TDLfncMq53m4/lDLJx5ZmpPPfpHl5fv++Uh3qvz2TPwXK+yS1lW34ZdsMgIdJFfLiL+Igjt1Bn3Z/6e8+5l3/t/pA1uw+yZs8hPN66CQJ7dQtj3BnxTX6Y8W1hOR9szqeixothwDk94xjTJ+70tdqbUFTpIftwBbnFVUQEO8hMiqBbuAtaW4IJOcVVbM4tZVdBGRHBDgalRNI/OZIQp/3Y/f/ze6o9leQOvJSc2gr2l++nsLKQqtoqdhXvYlfxrka7D4gbwNS+U7mk5yWEO8Pb/ppFREREWsEwv7+ekzRSWlpKVFQUJSUlREZGBrocETmFTNM86W7VuwrKmPCH/2C3GVw5PJWYMCfRoQ5iQp1EhziIDnUSE1Z/P9SBK6iJMNmEKreXrfmlbMktZUteKd/klrI9v5Rqj++Ezw1z2kmIDCY+3MWeQxUUltUAMCQtinkXZzKmd7dmn19c6eb+Jd+wOCsXgMykCP4wbRgDUpr+m1jr9XGowk1hWQ2F5TWEOuwkR4WQGOVq0evdd7iSVd8eYtXuQ6z69hD5pdXH7NMnIZxLBidz6ZBk+iVGNHu87EOV/POr/bz9VQ7fHao85nGH3WBC/0SmnpnGuDPicTQMm/jzaCjYArG9wBkGgBuTXMNHjv9mstvm5XNbLZ76SyfEhIleB1O9Tob67Bit/vSheQ3/aHfIAQCOMOg+CjJ+AN3PAVfzPxsRERFpWmtyqEL9CSjUi0hrTVuwirV7WzbBnM0Ah92G027DEWTDYTeO3LfbcAQZVLq97D1Yga+Jv9ahTjv9kyPpnxyBgeEP0oVlNRSUVTcZ+tNjQ/h/EzOZNDgZWyta3N/flMev3tnM4Qo3DrvB9edk4AyyUVBWXXfeshoOltdwqMLN8f5liQtzkhgZTHJUMElRDV9D8Jkma3YfZvXuQ+QUVzV6jtNuY1j3aEb0iGHHgTL+s+Mgbu+R19UnIZxJg5OZdFTAL6ny8N6mPP755X7W7S1q9H5dPCiZyUOT2XOwgjc37Oeb3FL/493CnVwxLJWrRqSR+dkc2Pxmi96bIpuNpeFhvBURzm6nw7+9t9vNlWUVXFZeQbTvxB/AdCqGHVKGQ8a5R4V89WAQERFpCYX6dqRQLyKtVVBWzUdbCiiqdFNU4aao0kNxpZviKg9FlW6K6+83FdKbEx/hYkByJANTIhmQEsmA5Egy4sKOG8xN06TC7fUH7sKyGmxG3dj/1s7u3+BgeQ2/ensTH35zoNn97DaDuDAn3cJdVLprySuppqa2ZaE2yGYwND2a0b3iGN07jjO7xzTqHl9a7eGjLQd4b1PeMQG/b0I4Gd3CWLmjEHf9+QwDzu3TjSvPTGXiwCT/cIQGW/NKeWvDft7JyuFgudu//cwUFzekHsBlB59pYlL3npom/p+dzzQxTZNKt5fS6lpKq91k1+ayy7mFgpC9+Gzeuhp8NiIqk7D5jpzbbjMIshs4bDaC7AZBdhsOm4Hb66PK46Xa7cN3Ev9EO+wGPrNumMbJcNhtOINs2Azq34O67Q3/fWjYZmLiND3EUkq0WUyw2biHhYlBdVAk1c5YvHaX/7nf2+monggGDmw4seMwguq/2nFhx0kQQYYdJ3aCjKavZQPqfvgNh66v98jPsv5cpln/8zQBA5tR9zxb/dNthoFhGHXf1x/Y8J/g6B4T7dV34kjfjobyTcA0bJgYmBiADZ9h1N+v335ML6OmKzMafWM0Os/RjxsYx/584KgP7I561Gy8pannNbx3YPjPd+RcBKTryYlO2eRvznF+nXo7Uoixf+9Dq++foP73pOHaM+uvO5//vul/mmEYR74aDdvqfi6G0fhndtz6m6i1pX8NOmRPoA6iPedJDXEEkZmkHk0BERwFvcYFuopmKdS3I4V6ETkVfD6Tsppaajxe3F4fHq+Jx+vDXevDc/R9rw+HzUa/pHASIoIDXTZQ9x/Pd7/OY8W2AqJCHSREBPvH7yfUf40JdTYad2+aJsWVHvJLq8kvqSavpJr8kqq6r6XV1Hh8nNkjhtG94xjRI4YwV8umfGku4PdLDGfqmWlcPiyVpKgTv3cer4+V2wt568v9fLT1gH/OgTazVeOIzMIVsx4jeP/JHUtEOrQ/HSjkh5VVJ95RRDqG5GHw3ysDXUWzFOrbkUK9iIg1NAT8/UVV/DAzgYEpkW2eI6Gows2Sjblk7Sv2tyLajLpWXFtDy1n9fQODiOAgYsOcxIU7iQ1zERvqJDbcSVyYk2CHna2HtpJVmOVv6a+p9VJRU0ul20uFu5aKGi+Vbi+V7lpCnUHERziJD3cRE+bE1obXUOXxUlzpwWE3iAh24Gpjz4xKt5eSKg8lVR58PtPfct3wHhz9taElv+GDKXetz/+BlbvWi72mmIjKbKKr9xPk8xxp8f6ehm0+wGOYuA0fbkxqMPEYPtxG3fduw4fHMFvd4+VE5z36cOb3Npgtbuc8Ccc2gNfXZh7Vsm02+fUkT9fstgbt0Uhpnf94trzS6SUu+rtbMEdKQ2+Fozcd5001j7kWTCu9edICLoednnFhgS6ja+rWBy5/OtBVNEuhvh0p1IuIiIiIiMjp1Joc2raP7kVEREREREQk4BTqRURERERERCxKoV5ERERERETEohTqRURERERERCxKoV5ERERERETEohTqRURERERERCxKoV5ERERERETEohTqRURERERERCxKoV5ERERERETEohTqRURERERERCxKoV5ERERERETEohTqRURERERERCxKoV5ERERERETEohTqRURERERERCxKoV5ERERERETEohTqRURERERERCxKoV5ERERERETEohTqRURERERERCxKoV5ERERERETEohTqRURERERERCxKoV5ERERERETEohTqRURERERERCxKoV5ERERERETEohTqRURERERERCxKoV5ERERERETEooICXUBHZ5omAKWlpQGuRERERERERLqChvzZkEebo1B/AmVlZQCkp6cHuBIRERERERHpSsrKyoiKimp2H8NsSfTvwnw+H7m5uURERGAYRkBqKC0tJT09nX379hEZGRmQGkRON1330hXpupeuSNe9dEW67uVETNOkrKyMlJQUbLbmR82rpf4EbDYbaWlpgS4DgMjISP3SS5ej6166Il330hXpupeuSNe9NOdELfQNNFGeiIiIiIiIiEUp1IuIiIiIiIhYlEK9BbhcLu6//35cLlegSxE5bXTdS1ek6166Il330hXpupf2pInyRERERERERCxKLfUiIiIiIiIiFqVQLyIiIiIiImJRCvUiIiIiIiIiFqVQLyIiIiIiImJRCvUd3NNPP01GRgbBwcGMGjWKtWvXBrokkXbzyCOPcPbZZxMREUFCQgJXXHEF27dvb7RPdXU1s2bNIi4ujvDwcKZOncqBAwcCVLFI+/vd736HYRjMnj3bv03XvXRGOTk5/PjHPyYuLo6QkBAGDx7M+vXr/Y+bpsl9991HcnIyISEhTJgwgZ07dwawYpGT4/V6uffee+nZsychISH07t2b3/72txw9T7mue2kPCvUd2Ouvv87cuXO5//77+fLLLxk6dCgTJ06koKAg0KWJtIuVK1cya9YsVq9ezbJly/B4PFx44YVUVFT495kzZw5Lly7ljTfeYOXKleTm5nLllVcGsGqR9rNu3TqeffZZhgwZ0mi7rnvpbIqKihg7diwOh4P333+fLVu28MQTTxATE+Pf57HHHuPJJ59kwYIFrFmzhrCwMCZOnEh1dXUAKxdpu0cffZRnnnmGp556iq1bt/Loo4/y2GOPMX/+fP8+uu6lXZjSYY0cOdKcNWuW/77X6zVTUlLMRx55JIBViZw6BQUFJmCuXLnSNE3TLC4uNh0Oh/nGG2/499m6dasJmKtWrQpUmSLtoqyszOzbt6+5bNkyc9y4ceYdd9xhmqaue+mc7rrrLvPcc8897uM+n89MSkoyH3/8cf+24uJi0+Vyma+++urpKFGk3U2aNMm88cYbG2278sorzenTp5umqete2o9a6jsot9vNhg0bmDBhgn+bzWZjwoQJrFq1KoCViZw6JSUlAMTGxgKwYcMGPB5Po9+DzMxMunfvrt8DsbxZs2YxadKkRtc36LqXzmnJkiWMGDGCq6++moSEBIYPH85zzz3nf3zPnj3k5+c3uu6joqIYNWqUrnuxrDFjxrB8+XJ27NgBwMaNG/nss8+4+OKLAV330n6CAl2ANO3gwYN4vV4SExMbbU9MTGTbtm0Bqkrk1PH5fMyePZuxY8cyaNAgAPLz83E6nURHRzfaNzExkfz8/ABUKdI+XnvtNb788kvWrVt3zGO67qUz2r17N8888wxz587lnnvuYd26dfziF7/A6XQyY8YM/7Xd1P97dN2LVc2bN4/S0lIyMzOx2+14vV4eeughpk+fDqDrXtqNQr2IdAizZs1i8+bNfPbZZ4EuReSU2rdvH3fccQfLli0jODg40OWInBY+n48RI0bw8MMPAzB8+HA2b97MggULmDFjRoCrEzk1Fi1axMsvv8wrr7zCwIEDycrKYvbs2aSkpOi6l3al7vcdVLdu3bDb7cfMdnzgwAGSkpICVJXIqXHbbbfx7rvvsmLFCtLS0vzbk5KScLvdFBcXN9pfvwdiZRs2bKCgoIAzzzyToKAggoKCWLlyJU8++SRBQUEkJibqupdOJzk5mQEDBjTa1r9/f7KzswH817b+3yOdyZ133sm8efP4r//6LwYPHsz111/PnDlzeOSRRwBd99J+FOo7KKfTyVlnncXy5cv923w+H8uXL2f06NEBrEyk/ZimyW233cbbb7/Nxx9/TM+ePRs9ftZZZ+FwOBr9Hmzfvp3s7Gz9HohljR8/nk2bNpGVleW/jRgxgunTp/u/13Uvnc3YsWOPWbJ0x44d9OjRA4CePXuSlJTU6LovLS1lzZo1uu7FsiorK7HZGsctu92Oz+cDdN1L+1H3+w5s7ty5zJgxgxEjRjBy5Ej++Mc/UlFRwU9+8pNAlybSLmbNmsUrr7zC4sWLiYiI8I8fi4qKIiQkhKioKG666Sbmzp1LbGwskZGR3H777YwePZpzzjknwNWLtE1ERIR/3ogGYWFhxMXF+bfrupfOZs6cOYwZM4aHH36YadOmsXbtWhYuXMjChQsBMAyD2bNn8+CDD9K3b1969uzJvffeS0pKCldccUVgixdpo8mTJ/PQQw/RvXt3Bg4cyFdffcUf/vAHbrzxRkDXvbSjQE+/L82bP3++2b17d9PpdJojR440V69eHeiSRNoN0OTt+eef9+9TVVVl3nrrrWZMTIwZGhpqTpkyxczLywtc0SKnwNFL2pmmrnvpnJYuXWoOGjTIdLlcZmZmprlw4cJGj/t8PvPee+81ExMTTZfLZY4fP97cvn17gKoVOXmlpaXmHXfcYXbv3t0MDg42e/XqZf7qV78ya2pq/Pvoupf2YJimaQbyQwURERERERERaRuNqRcRERERERGxKIV6EREREREREYtSqBcRERERERGxKIV6EREREREREYtSqBcRERERERGxKIV6EREREREREYtSqBcRERERERGxKIV6EREREREREYtSqBcREZFW27t3L4ZhkJWVFehSREREujSFehERkU5m5syZGIZxzO2iiy4KdGmn3SeffIJhGBQXFwe6FBERkVMiKNAFiIiISPu76KKLeP755xttc7lcAapGREREThW11IuIiHRCLpeLpKSkRreYmBgArrvuOq655ppG+3s8Hrp168aLL74IwAcffMC5555LdHQ0cXFxXHrppXz77betqqGmpoa77rqL9PR0XC4Xffr04a9//av/8ZUrVzJy5EhcLhfJycnMmzeP2tpa/+MZGRn88Y9/bHTMYcOG8b//+7/++4Zh8Je//IUpU6YQGhpK3759WbJkCVA3ROCCCy4AICYmBsMwmDlzZqteg4iISEenUC8iItLFTJ8+naVLl1JeXu7f9uGHH1JZWcmUKVMAqKioYO7cuaxfv57ly5djs9mYMmUKPp+vxee54YYbePXVV3nyySfZunUrzz77LOHh4QDk5ORwySWXcPbZZ7Nx40aeeeYZ/vrXv/Lggw+2+vU88MADTJs2ja+//ppLLrmE6dOnc/jwYdLT03nrrbcA2L59O3l5efzpT39q9fFFREQ6MnW/FxER6YTeffddf4BucM8993DPPfcwceJEwsLCePvtt7n++usBeOWVV7jsssuIiIgAYOrUqY2e+7e//Y34+Hi2bNnCoEGDTnj+HTt2sGjRIpYtW8aECRMA6NWrl//xP//5z6Snp/PUU09hGAaZmZnk5uZy1113cd9992GztbzdYebMmVx77bUAPPzwwzz55JOsXbuWiy66iNjYWAASEhKIjo5u8TFFRESsQi31IiIindAFF1xAVlZWo9stt9wCQFBQENOmTePll18G6lrlFy9ezPTp0/3P37lzJ9deey29evUiMjKSjIwMALKzs1t0/qysLOx2O+PGjWvy8a1btzJ69GgMw/BvGzt2LOXl5ezfv79Vr3XIkCH+78PCwoiMjKSgoKBVxxAREbEqtdSLiIh0QmFhYfTp0+e4j0+fPp1x48ZRUFDAsmXLCAkJaTQ7/uTJk+nRowfPPfccKSkp+Hw+Bg0ahNvtbtH5Q0JCTvo12Gw2TNNstM3j8Ryzn8PhaHTfMIxWDRMQERGxMrXUi4iIdEFjxowhPT2d119/nZdffpmrr77aH44PHTrE9u3b+fWvf8348ePp378/RUVFrTr+4MGD8fl8rFy5ssnH+/fvz6pVqxqF9s8//5yIiAjS0tIAiI+PJy8vz/94aWkpe/bsaVUdTqcTAK/X26rniYiIWIVCvYiISCdUU1NDfn5+o9vBgwcb7XPdddexYMECli1b1qjrfUxMDHFxcSxcuJBdu3bx8ccfM3fu3FadPyMjgxkzZnDjjTfyzjvvsGfPHj755BMWLVoEwK233sq+ffu4/fbb2bZtG4sXL+b+++9n7ty5/vH0P/zhD/nHP/7Bp59+yqZNm5gxYwZ2u71VdfTo0QPDMHj33XcpLCxsNDmgiIhIZ6BQLyIi0gl98MEHJCcnN7qde+65jfaZPn06W7ZsITU1lbFjx/q322w2XnvtNTZs2MCgQYOYM2cOjz/+eKtreOaZZ7jqqqu49dZbyczM5Gc/+xkVFRUApKam8t5777F27VqGDh3KLbfcwk033cSvf/1r//Pvvvtuxo0bx6WXXsqkSZO44oor6N27d6tqSE1N5YEHHmDevHkkJiZy2223tfp1iIiIdGSG+f3BaiIiIiIiIiJiCWqpFxEREREREbEohXoRERERERERi1KoFxEREREREbEohXoRERERERERi1KoFxEREREREbEohXoRERERERERi1KoFxEREREREbEohXoRERERERERi1KoFxEREREREbEohXoRERERERERi1KoFxEREREREbGo/w/I4ev7vBwTXwAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -215,7 +215,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -258,7 +258,7 @@
    "source": [
     "from qiskit_algorithms.gradients import FiniteDiffEstimatorGradient\n",
     "\n",
-    "estimator = Estimator()\n",
+    "estimator = StatevectorEstimator()\n",
     "gradient = FiniteDiffEstimatorGradient(estimator, epsilon=0.01)"
    ]
   },
@@ -284,7 +284,7 @@
    ],
    "source": [
     "algorithm_globals.random_seed = 50\n",
-    "ansatz = TwoLocal(rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
+    "ansatz = n_local(2, rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
     "\n",
     "optimizer = SLSQP(maxiter=100)\n",
     "\n",
@@ -310,7 +310,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -356,22 +356,22 @@
      "text": [
       "{   'aux_operators_evaluated': None,\n",
       "    'cost_function_evals': 9,\n",
-      "    'eigenvalue': -1.8572750175655814,\n",
-      "    'optimal_circuit': <qiskit.circuit.library.n_local.two_local.TwoLocal object at 0x7ff6b9816980>,\n",
-      "    'optimal_parameters': {   ParameterVectorElement(θ[3]): 6.0929478327669955,\n",
-      "                              ParameterVectorElement(θ[2]): 0.547077760766061,\n",
-      "                              ParameterVectorElement(θ[7]): 0.3602101747090559,\n",
-      "                              ParameterVectorElement(θ[6]): -4.717616147449735,\n",
-      "                              ParameterVectorElement(θ[4]): -2.598326651673345,\n",
-      "                              ParameterVectorElement(θ[5]): 1.5683250498282117,\n",
-      "                              ParameterVectorElement(θ[1]): 4.426962358395529,\n",
-      "                              ParameterVectorElement(θ[0]): 4.296519450348804},\n",
+      "    'eigenvalue': np.float64(-1.8572750175655812),\n",
+      "    'optimal_circuit': <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fe9e7fc0bb0>,\n",
+      "    'optimal_parameters': {   ParameterVectorElement(θ[6]): np.float64(-4.717616147449723),\n",
+      "                              ParameterVectorElement(θ[7]): np.float64(0.36021017470900696),\n",
+      "                              ParameterVectorElement(θ[5]): np.float64(1.5683250498282177),\n",
+      "                              ParameterVectorElement(θ[1]): np.float64(4.426962358395508),\n",
+      "                              ParameterVectorElement(θ[4]): np.float64(-2.598326651673286),\n",
+      "                              ParameterVectorElement(θ[3]): np.float64(6.092947832766964),\n",
+      "                              ParameterVectorElement(θ[2]): np.float64(0.5470777607660052),\n",
+      "                              ParameterVectorElement(θ[0]): np.float64(4.296519450348767)},\n",
       "    'optimal_point': array([ 4.29651945,  4.42696236,  0.54707776,  6.09294783, -2.59832665,\n",
       "        1.56832505, -4.71761615,  0.36021017]),\n",
-      "    'optimal_value': -1.8572750175655814,\n",
+      "    'optimal_value': np.float64(-1.8572750175655812),\n",
       "    'optimizer_evals': None,\n",
-      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x7ff6985b6050>,\n",
-      "    'optimizer_time': 0.17816972732543945}\n"
+      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x7fe9e7fdc180>,\n",
+      "    'optimizer_time': 0.17016196250915527}\n"
      ]
     }
    ],
@@ -404,40 +404,38 @@
      "text": [
       "{   'aux_operators_evaluated': None,\n",
       "    'cost_function_evals': 1,\n",
-      "    'eigenvalue': -1.8572750175655814,\n",
-      "    'optimal_circuit': <qiskit.circuit.library.n_local.two_local.TwoLocal object at 0x7ff6b996fee0>,\n",
-      "    'optimal_parameters': {   ParameterVectorElement(θ[6]): -4.717616147449735,\n",
-      "                              ParameterVectorElement(θ[7]): 0.3602101747090559,\n",
-      "                              ParameterVectorElement(θ[5]): 1.5683250498282117,\n",
-      "                              ParameterVectorElement(θ[4]): -2.598326651673345,\n",
-      "                              ParameterVectorElement(θ[0]): 4.296519450348804,\n",
-      "                              ParameterVectorElement(θ[2]): 0.547077760766061,\n",
-      "                              ParameterVectorElement(θ[3]): 6.0929478327669955,\n",
-      "                              ParameterVectorElement(θ[1]): 4.426962358395529},\n",
+      "    'eigenvalue': np.float64(-1.8572750175655812),\n",
+      "    'optimal_circuit': <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fe9e7ec34d0>,\n",
+      "    'optimal_parameters': {   ParameterVectorElement(θ[7]): np.float64(0.36021017470900696),\n",
+      "                              ParameterVectorElement(θ[6]): np.float64(-4.717616147449723),\n",
+      "                              ParameterVectorElement(θ[5]): np.float64(1.5683250498282177),\n",
+      "                              ParameterVectorElement(θ[4]): np.float64(-2.598326651673286),\n",
+      "                              ParameterVectorElement(θ[3]): np.float64(6.092947832766964),\n",
+      "                              ParameterVectorElement(θ[2]): np.float64(0.5470777607660052),\n",
+      "                              ParameterVectorElement(θ[1]): np.float64(4.426962358395508),\n",
+      "                              ParameterVectorElement(θ[0]): np.float64(4.296519450348767)},\n",
       "    'optimal_point': array([ 4.29651945,  4.42696236,  0.54707776,  6.09294783, -2.59832665,\n",
       "        1.56832505, -4.71761615,  0.36021017]),\n",
-      "    'optimal_value': -1.8572750175655814,\n",
+      "    'optimal_value': np.float64(-1.8572750175655812),\n",
       "    'optimizer_evals': None,\n",
-      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x7ff6b9912ec0>,\n",
-      "    'optimizer_time': 0.028038740158081055}\n",
-      "\n"
+      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x7fe9e7fdcb00>,\n",
+      "    'optimizer_time': 0.11540818214416504}\n"
      ]
     }
    ],
    "source": [
     "initial_pt = result.optimal_point\n",
     "\n",
-    "estimator1 = Estimator()\n",
+    "estimator1 = StatevectorEstimator()\n",
     "gradient1 = FiniteDiffEstimatorGradient(estimator, epsilon=0.01)\n",
-    "ansatz1 = TwoLocal(rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
+    "ansatz1 = n_local(2, rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
     "optimizer1 = SLSQP(maxiter=1000)\n",
     "\n",
     "vqe1 = VQE(estimator1, ansatz1, optimizer1, gradient=gradient1, initial_point=initial_pt)\n",
     "result1 = vqe1.compute_minimum_eigenvalue(operator=H2_op)\n",
     "print(result1)\n",
     "\n",
-    "cost_function_evals1 = result1.cost_function_evals\n",
-    "print()"
+    "cost_function_evals1 = result1.cost_function_evals"
    ]
   },
   {
@@ -484,7 +482,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.0</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td colspan='2'>Mon Feb 19 11:17:07 2024 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>2.0.2</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.4.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.13.3</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td colspan='2'>Mon Jun 16 16:41:09 2025 CEST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -496,7 +494,7 @@
     {
      "data": {
       "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -530,7 +528,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/03_vqe_simulation_with_noise.ipynb b/docs/tutorials/03_vqe_simulation_with_noise.ipynb
index 5b91e4aa..8bae8ee5 100644
--- a/docs/tutorials/03_vqe_simulation_with_noise.ipynb
+++ b/docs/tutorials/03_vqe_simulation_with_noise.ipynb
@@ -92,11 +92,11 @@
    "outputs": [],
    "source": [
     "# define ansatz and optimizer\n",
-    "from qiskit.circuit.library import TwoLocal\n",
+    "from qiskit.circuit.library import n_local\n",
     "from qiskit_algorithms.optimizers import SPSA\n",
     "\n",
     "iterations = 125\n",
-    "ansatz = TwoLocal(rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
+    "ansatz = n_local(2, rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
     "spsa = SPSA(maxiter=iterations)"
    ]
   },
@@ -104,9 +104,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Performance *without* noise\n",
+    "## Performance *without* backend noise\n",
     "\n",
-    "Let's first run the `VQE` on the default Aer simulator without adding noise, with a fixed seed for the run and transpilation to obtain reproducible results. This result should be relatively close to the reference value from the exact computation."
+    "Let's first run the `VQE` on the default Aer simulator without adding noise from the backend. We'll still specify a target precision to simulate shot noise. This result should be relatively close to the reference value from the exact computation."
    ]
   },
   {
@@ -134,15 +134,12 @@
    "source": [
     "# define Aer Estimator for noiseless statevector simulation\n",
     "from qiskit_algorithms.utils import algorithm_globals\n",
-    "from qiskit_aer.primitives import Estimator as AerEstimator\n",
+    "from qiskit_aer.primitives import EstimatorV2 as AerEstimator\n",
     "\n",
     "seed = 170\n",
     "algorithm_globals.random_seed = seed\n",
     "\n",
-    "noiseless_estimator = AerEstimator(\n",
-    "    run_options={\"seed\": seed, \"shots\": 1024},\n",
-    "    transpile_options={\"seed_transpiler\": seed},\n",
-    ")"
+    "noiseless_estimator = AerEstimator(options={\"default_precision\": 1e-2})"
    ]
   },
   {
@@ -154,8 +151,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "VQE on Aer qasm simulator (no noise): -1.85160\n",
-      "Delta from reference energy value is 0.00567\n"
+      "VQE on Aer qasm simulator (no noise): -1.85293\n",
+      "Delta from reference energy value is 0.00435\n"
      ]
     }
    ],
@@ -194,7 +191,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x400 with 1 Axes>"
       ]
@@ -217,7 +214,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Performance *with* noise\n",
+    "## Performance *with* backend noise\n",
     "\n",
     "Now, let's add noise to our simulation. Here we create a fake device, using `GenericBackendV2`, which has a default noise model. As stated in the introduction, it is also possible to create custom noise models from scratch, but this task is beyond the scope of this notebook.\n",
     "\n",
@@ -235,7 +232,7 @@
      "text": [
       "NoiseModel:\n",
       "  Basis gates: ['cx', 'delay', 'id', 'measure', 'reset', 'rz', 'sx', 'x']\n",
-      "  Instructions with noise: ['x', 'id', 'sx', 'measure', 'cx']\n",
+      "  Instructions with noise: ['sx', 'cx', 'measure', 'x', 'id']\n",
       "  Qubits with noise: [0, 1, 2, 3, 4]\n",
       "  Specific qubit errors: [('cx', (0, 1)), ('cx', (1, 2)), ('cx', (2, 3)), ('cx', (3, 4)), ('id', (0,)), ('id', (1,)), ('id', (2,)), ('id', (3,)), ('id', (4,)), ('sx', (0,)), ('sx', (1,)), ('sx', (2,)), ('sx', (3,)), ('sx', (4,)), ('x', (0,)), ('x', (1,)), ('x', (2,)), ('x', (3,)), ('x', (4,)), ('measure', (0,)), ('measure', (1,)), ('measure', (2,)), ('measure', (3,)), ('measure', (4,))]\n"
      ]
@@ -267,13 +264,14 @@
    "outputs": [],
    "source": [
     "noisy_estimator = AerEstimator(\n",
-    "    backend_options={\n",
-    "        \"method\": \"density_matrix\",\n",
-    "        \"coupling_map\": coupling_map,\n",
-    "        \"noise_model\": noise_model,\n",
-    "    },\n",
-    "    run_options={\"seed\": seed, \"shots\": 1024},\n",
-    "    transpile_options={\"seed_transpiler\": seed},\n",
+    "    options={\n",
+    "        \"default_precision\": 1e-2,\n",
+    "        \"backend_options\": {\n",
+    "            \"method\": \"density_matrix\",\n",
+    "            \"coupling_map\": coupling_map,\n",
+    "            \"noise_model\": noise_model,\n",
+    "        },\n",
+    "    }\n",
     ")"
    ]
   },
@@ -304,8 +302,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "VQE on Aer qasm simulator (with noise): -1.84320\n",
-      "Delta from reference energy value is 0.01407\n"
+      "VQE on Aer qasm simulator (with noise): -1.86249\n",
+      "Delta from reference energy value is -0.00522\n"
      ]
     }
    ],
@@ -329,7 +327,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x400 with 1 Axes>"
       ]
@@ -358,7 +356,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this tutorial, you compared three calculations for the H2 molecule ground state.  First, you produced a reference value using a classical minimum eigensolver. Then, you proceeded to run `VQE` using the Qiskit Aer `Estimator` with 1024 shots. Finally, you extracted a noise model from a backend and used it to define a new `Estimator` for noisy simulations. The results are:"
+    "In this tutorial, you compared three calculations for the H2 molecule ground state.  First, you produced a reference value using a classical minimum eigensolver. Then, you proceeded to run `VQE` using the Qiskit Aer `Estimator` with a target precision of $10^{-2}$. Finally, you extracted a noise model from a backend and used it to define a new `Estimator` for noisy simulations. The results are:"
    ]
   },
   {
@@ -371,8 +369,8 @@
      "output_type": "stream",
      "text": [
       "Reference value: -1.85728\n",
-      "VQE on Aer qasm simulator (no noise): -1.85160\n",
-      "VQE on Aer qasm simulator (with noise): -1.84320\n"
+      "VQE on Aer qasm simulator (no noise): -1.85293\n",
+      "VQE on Aer qasm simulator (with noise): -1.86249\n"
      ]
     }
    ],
@@ -386,7 +384,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can notice that, while the noiseless simulation's result is closer to the exact reference value, there is still some difference. This is due to the sampling noise, introduced by limiting the number of shots to 1024. A larger number of shots would decrease this sampling error and close the gap between these two values.\n",
+    "You can notice that, while the noiseless simulation's result is closer to the exact reference value, there is still some difference. This is due to the sampling noise, introduced by limiting the target precision to $10^{-2}$, which in turn puts a limit on the number of shots the Estimator uses. A larger number of shots would decrease this sampling error and close the gap between these two values.\n",
     "\n",
     "As for the noise introduced by real devices (or simulated noise models), it could be tackled through a wide variety of error mitigation techniques. The [Qiskit Runtime Primitives](https://quantum.cloud.ibm.com/docs/api/qiskit-ibm-runtime) have enabled error mitigation through the `resilience_level` option. This option is currently available for remote simulators and real backends accessed via the Runtime Primitives, you can consult [this documentation](https://quantum.cloud.ibm.com/docs/guides/configure-error-mitigation) for further information."
    ]
@@ -403,7 +401,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0</td></tr><tr><td><code>qiskit_ibm_runtime</code></td><td>0.19.1</td></tr><tr><td><code>qiskit_aer</code></td><td>0.13.3</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.0</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td colspan='2'>Mon Feb 19 11:18:09 2024 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>2.0.2</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.4.0</td></tr><tr><td><code>qiskit_aer</code></td><td>0.17.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.13.3</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td colspan='2'>Mon Jun 16 17:04:40 2025 CEST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -415,7 +413,7 @@
     {
      "data": {
       "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -450,7 +448,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/04_vqd.ipynb b/docs/tutorials/04_vqd.ipynb
index 4ba3b728..393a3011 100644
--- a/docs/tutorials/04_vqd.ipynb
+++ b/docs/tutorials/04_vqd.ipynb
@@ -57,7 +57,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can set up, for example, a `TwoLocal` ansatz with two qubits, and choose `SLSQP` as the optimization method."
+    "You can set up, for example, a `n_local` ansatz with two qubits, and choose `SLSQP` as the optimization method."
    ]
   },
   {
@@ -71,7 +71,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 538.128x200.667 with 1 Axes>"
       ]
@@ -82,13 +82,13 @@
     }
    ],
    "source": [
-    "from qiskit.circuit.library import TwoLocal\n",
+    "from qiskit.circuit.library import n_local\n",
     "from qiskit_algorithms.optimizers import COBYLA\n",
     "\n",
-    "ansatz = TwoLocal(2, rotation_blocks=[\"ry\", \"rz\"], entanglement_blocks=\"cz\", reps=1)\n",
+    "ansatz = n_local(2, rotation_blocks=[\"ry\", \"rz\"], entanglement_blocks=\"cz\", reps=1)\n",
     "\n",
     "optimizer = COBYLA()\n",
-    "ansatz.decompose().draw(\"mpl\")"
+    "ansatz.draw(\"mpl\")"
    ]
   },
   {
@@ -104,11 +104,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit.primitives import Sampler, Estimator\n",
+    "from qiskit.primitives import StatevectorSampler, StatevectorEstimator\n",
     "from qiskit_algorithms.state_fidelities import ComputeUncompute\n",
+    "from qiskit_algorithms.utils import algorithm_globals\n",
     "\n",
-    "estimator = Estimator()\n",
-    "sampler = Sampler()\n",
+    "algorithm_globals.random_seed = 42\n",
+    "\n",
+    "estimator = StatevectorEstimator(seed=42)\n",
+    "sampler = StatevectorSampler(seed=42)\n",
     "fidelity = ComputeUncompute(sampler)"
    ]
   },
@@ -191,7 +194,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-1.85725689 -1.2445823  -0.88272936]\n"
+      "[-1.85727501 -1.24519007 -0.8838798 ]\n"
      ]
     }
    ],
@@ -217,7 +220,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -289,7 +292,7 @@
      "output_type": "stream",
      "text": [
       "Reference values: [-1.85727503 -1.24458455 -0.88272215]\n",
-      "VQD values: [-1.85725689 -1.2445823  -0.88272936]\n"
+      "VQD values: [-1.85727501 -1.24519007 -0.8838798 ]\n"
      ]
     }
    ],
@@ -313,7 +316,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.0</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td colspan='2'>Mon Feb 19 11:17:45 2024 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>2.0.2</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.4.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.13.3</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td colspan='2'>Mon Jun 16 17:07:36 2025 CEST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -325,7 +328,7 @@
     {
      "data": {
       "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -360,7 +363,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/05_qaoa.ipynb b/docs/tutorials/05_qaoa.ipynb
index cf8801b9..1da6c726 100644
--- a/docs/tutorials/05_qaoa.ipynb
+++ b/docs/tutorials/05_qaoa.ipynb
@@ -44,7 +44,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -97,14 +97,14 @@
     "\n",
     "def bitfield(n, L):\n",
     "    result = np.binary_repr(n, L)\n",
-    "    return [int(digit) for digit in result]  # [2:] to chop off the \"0b\" part\n",
+    "    return [int(digit) for digit in result]\n",
     "\n",
     "\n",
     "# use the brute-force way to generate the oracle\n",
     "L = num_nodes\n",
-    "max = 2**L\n",
+    "maximum = 2**L\n",
     "sol = np.inf\n",
-    "for i in range(max):\n",
+    "for i in range(maximum):\n",
     "    cur = bitfield(i, L)\n",
     "\n",
     "    how_many_nonzero = np.count_nonzero(cur)\n",
@@ -207,7 +207,7 @@
     }
    ],
    "source": [
-    "from qiskit.primitives import Sampler\n",
+    "from qiskit.primitives import StatevectorSampler\n",
     "from qiskit.quantum_info import Pauli\n",
     "from qiskit.result import QuasiDistribution\n",
     "\n",
@@ -216,26 +216,18 @@
     "\n",
     "from qiskit_algorithms.utils import algorithm_globals\n",
     "\n",
-    "sampler = Sampler()\n",
+    "sampler = StatevectorSampler(seed=42)\n",
     "\n",
     "\n",
     "def sample_most_likely(state_vector):\n",
     "    \"\"\"Compute the most likely binary string from state vector.\n",
     "    Args:\n",
-    "        state_vector: State vector or quasi-distribution.\n",
+    "        state_vector: Quasi-distribution.\n",
     "\n",
     "    Returns:\n",
-    "        Binary string as an array of ints.\n",
+    "        Array of bits.\n",
     "    \"\"\"\n",
-    "    if isinstance(state_vector, QuasiDistribution):\n",
-    "        values = list(state_vector.values())\n",
-    "    else:\n",
-    "        values = state_vector\n",
-    "    n = int(np.log2(len(values)))\n",
-    "    k = np.argmax(np.abs(values))\n",
-    "    x = bitfield(k, n)\n",
-    "    x.reverse()\n",
-    "    return np.asarray(x)\n",
+    "    return np.array([int(bit) for bit in max(state_vector.items(), key=lambda x: x[1])[0][::-1]])\n",
     "\n",
     "\n",
     "algorithm_globals.random_seed = 10598\n",
@@ -279,7 +271,7 @@
     "npme = NumPyMinimumEigensolver()\n",
     "result = npme.compute_minimum_eigenvalue(Operator(qubit_op))\n",
     "\n",
-    "x = sample_most_likely(result.eigenstate)\n",
+    "x = sample_most_likely(result.eigenstate.probabilities_dict())\n",
     "\n",
     "print(x)\n",
     "print(f\"Objective value computed by the NumPyMinimumEigensolver is {objective_value(x, w)}\")"
@@ -326,9 +318,101 @@
     "print(f\"Objective value computed by SamplingVQE is {objective_value(x, w)}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transpiling options\n",
+    "\n",
+    "The `QAOA` class doesn't expose the quantum circuit it uses to the user. However, this may lead to problems when running on actual quantum hardware, since the quantum circuit has to be transpiled beforehand. In order to still let the user choose the transpilation method and its properties in general, it is possible to pass to the `QAOA` class a `Transpiler`, which is any object having a `run` method that can transpile a `QuantumCircuit` into one another, or a list of `QuantumCircuit` into one another.\n",
+    "\n",
+    "For instance, let us define a custom backend on four qubits like so."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_aer.noise import NoiseModel\n",
+    "from qiskit.providers.fake_provider import GenericBackendV2\n",
+    "\n",
+    "coupling_map = [(0, 1), (1, 2), (2, 3)]\n",
+    "backend = GenericBackendV2(num_qubits=4, coupling_map=coupling_map, seed=54)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us define a `PassManager` for this backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "pm = generate_preset_pass_manager(optimization_level=2, backend=backend)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then pass this `PassManager` to the `transpiler` keyword argument of the `QAOA` constructor. We can also pass any options to the `run` method of the `Transpiler` by setting the `transpiler_options` keyword argument to a dictionary specifying said options. For instance, let us define a `callback` function that prints out that it has been called on the very first step of the transpilation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def callback(**kwargs):\n",
+    "    if kwargs[\"count\"] == 0:\n",
+    "        print(f\"Callback function has been called!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then instantiate and use the `QAOA` class like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Callback function has been called!\n",
+      "[0 1 0 1]\n",
+      "Objective value computed by QAOA is 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "qaoa = QAOA(sampler, optimizer, reps=2, transpiler=pm, transpiler_options={\"callback\": callback})\n",
+    "result = qaoa.compute_minimum_eigenvalue(qubit_op)\n",
+    "\n",
+    "x = sample_most_likely(result.eigenstate)\n",
+    "\n",
+    "print(x)\n",
+    "print(f\"Objective value computed by QAOA is {objective_value(x, w)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -338,7 +422,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.0</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td colspan='2'>Mon Feb 19 11:19:00 2024 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>2.0.2</td></tr><tr><td><code>qiskit_aer</code></td><td>0.17.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.4.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.13.5</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td colspan='2'>Tue Jul 01 16:52:04 2025 CEST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -350,7 +434,7 @@
     {
      "data": {
       "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -385,7 +469,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.9"
+   "version": "3.13.5"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/tutorials/06_grover.ipynb b/docs/tutorials/06_grover.ipynb
index 6f289d32..d2d763a0 100644
--- a/docs/tutorials/06_grover.ipynb
+++ b/docs/tutorials/06_grover.ipynb
@@ -63,7 +63,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 843.845x200.667 with 1 Axes>"
       ]
@@ -120,10 +120,10 @@
    ],
    "source": [
     "from qiskit_algorithms import Grover\n",
-    "from qiskit.primitives import Sampler\n",
+    "from qiskit.primitives import StatevectorSampler\n",
     "\n",
     "\n",
-    "grover = Grover(sampler=Sampler())\n",
+    "grover = Grover(sampler=StatevectorSampler())\n",
     "result = grover.amplify(problem)\n",
     "print(\"Result type:\", type(result))\n",
     "print()\n",
@@ -170,7 +170,7 @@
     "oracle = Statevector.from_label(\"11\")\n",
     "problem = AmplificationProblem(oracle, is_good_state=[\"11\"])\n",
     "\n",
-    "grover = Grover(sampler=Sampler())\n",
+    "grover = Grover(sampler=StatevectorSampler())\n",
     "result = grover.amplify(problem)\n",
     "print(\"Result type:\", type(result))\n",
     "print()\n",
@@ -192,7 +192,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 287.294x200.667 with 1 Axes>"
       ]
@@ -224,11 +224,14 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\"The 'tweedledum' library is required to use 'PhaseOracle'. You can install it with 'pip install tweedledum'.\"\n"
-     ]
+     "data": {
+      "image/png": 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lYfQDeP9X+Wix9KK04iIO/7EZDVe+wMiI/ctmwmcG4JGH9Nj61GIsz5nnUvzIuxgxUqRIfTBeLczAq4UZ6Ou/hTMXO3C5pRuDN4fh56tGaIg/0heHI2puMH9HQXCMGCnejCA/fDV9Dr6aLu+XApJ38JwYEQmNESMioTFiRCQ0RoyIhKay8SsbphybzQaI9kOn/LV0t+I+4DhGjIiExpeTRCQ0RoyIhMaIEZHQGDEiEhojRkRCY8SISGiMGBEJjREjIqExYkQkNEaMiITGiBGR0BgxIhIaI0ZEQmPEiEhojBgRCY0RIyKhMWJEJDRGjIiExogRkdAYMSISGiNGREJjxIhIaIwYEQmNESMioTFiRCS0/weo7FyJfnAI9gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 370.906x200.667 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -289,7 +292,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 371.107x284.278 with 1 Axes>"
       ]
@@ -339,7 +342,7 @@
     }
    ],
    "source": [
-    "grover = Grover(sampler=Sampler())\n",
+    "grover = Grover(sampler=StatevectorSampler())\n",
     "result = grover.amplify(problem)\n",
     "print(\"Success!\" if result.oracle_evaluation else \"Failure!\")\n",
     "print(\"Top measurement:\", result.top_measurement)"
@@ -375,7 +378,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 203.885x451.5 with 1 Axes>"
       ]
@@ -409,7 +412,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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S1fnkk0/i9OnTKC4uRnp6Ok6dOoWsrCzExcWhQ4cO2LNnD5KS/vxQ1LfumzdvYtiwYUhOTsbUqVNx5coVpKWl4bfffkNKSgoCAgKQkJCADz74wGR/P1M5s/YbVJdV6l0+c++vuH42oxkjMg+enp7YsGEDPD09xQ6FRCLXNnDmtxtYtfUc/hbaEdNGd4WFhQLbVzwKhQKYuDgWWq0gdohkZF/8kIFzaTf1Lv97TjH+9e/fmjEicbEP0F1yHQekTrLJ1549e6BUKrF582Z06NChxjYPDw9ERERAoVAAANLT01FWVoZOnTrBycnpvnW+8MILePjhh3X73RUSEoIhQ4YAAJKTk2ts06fu8PBwZGRkIDw8HFu2bIGLi4tum5+fHzZs2AAA2Lt3rz5vvcUoySlAzsF4g/dL/fxAM0RjXjQaDUpKSqDRaMQOxeTKNBpcr6ys80dO5NwGlm8+g7OpN7Bubi9sWNAXvbu5YNGG00jPku7lZnIlCAI++sbwRGrTrlRUVWmbISLzwD5AgLzHASmfC0gy+aquroZarYZGo8HRo0cbLH/27FkAQI8ePeotV1lZiZ07d+K1117D008/jUcffRT9+/dH//798cMPPwAArKysDKo7OTkZX3/9NVxdXbFq1ao6y9zd98qVKzV+n5mZieHDh8PBwQFt2rTBhAkTcOPGjXrfgznJ+uF/EBrxDV7md79C0Ep30AWACxcu4PHHH8eFC7VvuJa6ZWnJcP/Pd3X+yImc20B1tYCJi2NhY63Eqy8E4HjCVXz45Xmxw6JmkHLxFpIuGH7/Uu61Mvxy5mozRGQe2AcIkPc4IOVzAUmudqhSqTBy5Ejs3LkTYWFhePzxxzFmzBgMHz4cHh4etcrrk3wdPnwYkyZNQnZ2dr2v7e3tbVDd0dHR0Gq10Gg0utmzv7q7kIe9vb3ud8XFxXjsscfQtm1bREdHo7y8HBEREQgLC8Ovv/6qu9TRECEhIbh61XSD2ZNaTzwKd4P301So4ftAZ1QqWtY3QWPGjNG77LVr1wAA+/fvx+nTp/Xeb9SoUQbH1VS2FhZI6dHXaPVN9fTGaPcH6tw27MSxJtffpUsXlIuQvBty/IHGtQExjr8WlkDbxUavt6hEjUq1BlaWSvx0PBuCEa+08uvSBRaoMl6F1GiVKi/AcVKj9h394mTYqc0jIWmOfsA+IE08F9CPuZ8LuLq6Ij7e8Ku3AIkmXwCwfft2BAYG4pNPPkFMTAxiYmIwa9YsDBo0CCtXrkSfPn10ZRtKkE6ePImnnnoKVVVVmDJlCsaNG4cHH3wQbdq0gUqlQmpqKgICAqBSqRAQEFBj34bqjomJAXBngZCCgoJ635OXl5fu/zdv3owrV64gNjZWdy1wx44d0a9fP+zbtw8jR46st666XL16tdbsWnMqauUEtDI8+QKAnNwrUKNlJV+lpaV6ly0vL9f915D9THn87rJTKoEexqvPt1UrPNG+Q8MFGyk3NxdlIlzCYchxBBrXBsQ4/lBYAW2NX+1nywbAylKJlIuFWDy9B3YcyMTvOcVGqTsvNxcQuHKqWbCzAxwbt2vhjQIU3hahzdelGfoB+4A08VxAP1I9FwAknHzZ2NggMjIS77zzDs6ePYvvvvsOmzZtwpEjRzBkyBCkpqbqZsEaSpAWLFgAtVqN1atXY/78+bW2//jjjwCAwMBAWFtb19jWUN05OTkA7ixj36lTJ73f3w8//ID+/fvXuAmzb9++8Pb2xvfff9+o5MvV1dXgfZpCrbUEGvFN3m2o0d7dFVA0XNac3Dtz2ZC7H7K2trYG7VfXzG5zs21hD8B2d3cXZebLkOMINK4NiHH8tbBEnpHrfH1sIB7r5Y6F/4jHd0cuIeGbkfh02QAMmvyTUep3c3fnt/5mQqNQ4qogAAoDPtD/KO/iJMDSwfRtvi7G7gfsA9LFcwHz0NRzgaacM0s2+bpLoVAgODgYwcHBmDNnDh566CFkZ2fj1KlTGDVqFPLz85Gfnw8nJ6caM0t3aTQaxMbGAgAmTap9aYRWq8VXX30FoPZDlBuqG/izY1VUGLZsbkpKCp577rlavw8KCkJKSopBdd3V2OnTxqoqq8CO4Omoum3YEvqPzhuP2XP3NVNUzScuLk7vsqmpqYiOjsawYcPg71/3Qwbr8uGHHzYisqYRKipQ/fxEk79uY6Wnp0Pxx+MfTMmQ4w80rg2IcfxLy6rQqs92o9Xn6+mIVeEhOJVUgDWfJkKrFRC5MQGrwnvi9bGB2PB14z7f7nUhPR32dpZGiJaMYfjrB/H9scv676BQoHe39jjxVULzBWUgY/YD9gFp47mAeRDrXACQ6IIb9+Pk5KRbEOPuioJpaWkA7lz7WZfS0lLdKjN1JUjvvfcezpw5A6B28tVQ3QB0M1eHDh3S+30AQGFhYZ2rJ7Zt2xY3b+q/ZK+YLO1s4Pv8Ywbto1Ap0WXc4GaKyHz4+vriwIED8PX1FTsUEokc24BCAXy+/FEoLRSYuPiYbknttZ8lIe58AVaFh8C7o4PIUZKxvfpCQMOFjLBPS8A+QPeS4zggB5JLvrZt24b58+fXmv0pLCzEjBkzcPHiRfj7++vu+bo7jZuTk1Pn9bSOjo666dsVK1boEjG1Wo2oqCgsWbIEKtWdCcS/Jl8N1Q38eePlggULsGvXrlrbMzIysHTpUr1WbWyJgv/vebQJ0P/5FX1WToWdazPcYGJmVCqV7p5Ckic5toG5E7shNLgD3v4oAamZfy6prdUKeHlJLFRKC3y6bEA9NVBL9LdQD0wf01Xv8s8+4YXxYdI8GWUfoHvJcRyQA8klX8eOHcPatWsRFBQEFxcX9OzZE0FBQXB3d8fmzZvRsWNH7N69G0qlEsCdy/Q8PT2Rm5sLT09P9O3bF/3798fHH3+sq3PRokUA7ixy4eHhgZ49e8LFxQWLFi3Chg0bIAgCFApFrfu69Kn7zTffxIABA1BcXIznnnsOzs7OeOSRRxAcHIx27drBz88PkZGRte4Ha9OmDW7dulXr/d+8eRNt27ac5MTK0R5PfvM2nLv71FtOYaFAn9XT0PXvda8IKTU5OTmYO3eu7p5Akh+5tQH/zq2xfNbD+N+5a3hvW+0V7FIu3kLkxgQMDHHD62MDRYiQmotCocBHi/rpNZv14lBvfLV6ICwsWthNv3pgH6C/kts4IBeSS6WnT58OV1dXxMbGIisrC4mJibCyskJQUBBGjBiB2bNno3Xr1rryNjY22L9/PxYuXIgTJ07g5MmTEAQBr7zyiq7MzJkzoVKpEBUVhaysLCiVSoSFhWH+/Pm6ZeJ9fHzg6FhzySZ96ra1tcXhw4exceNGREdHIyUlBUlJSWjXrh28vLwwfvx4hIWFoXPnzjXqDggIqPPerpSUFDz66KPG+nOahG17Jzz9/Qpc/vkUUj8/gKv/rfmg6qBXnkHXvz8JR283kSI0vZKSEhw/fhzTpk0TOxSTGdjOBepnnq+3TEPbpURubSA1swi2PbfVW2b11kSs3ppooojIlJRKC/xzYV9MeMYXH33zG745kIlK9Z8rkT33ZGfMfN4fg3q6QWHI4hwtCPsA/ZXcxgFAHucCkku++vTpU2MZeX0EBgZi79699ZaZNm3afRu/UM/DN/Sp29LSErNnz8bs2bMbClUnLCwMCxcuRE5ODjp27AjgzpL4Fy9eRFRUlN71mAsLSxW8nukHr2f6oeJmMf794KQ7KyEqgJ7vtJwbOImIqHEUCgV6P+SC3g+54KNF/eDz9A7k36iAWztb7Fj3uNjhEREZheQuO5SL6dOnw83NDSNGjMAPP/yAXbt24aWXXkKvXr0wYsQIscNrEpu2DrpvNqX6DScREd2fvZ0lVMo7pyhSvMSQiOSLyVcL5ejoiJiYGLi5ueHFF1/E1KlT0a9fP/zwww+waGHPWiAiIiIikgPJXXYoJz4+Pvjhhx/EDoOaQfv27REeHo727duLHQqJhG2AiEjeOA5IE5MvIjPk7OyMcePGiR0GiYhtgIhI3jgOSBOvTyMyQ7dv38ahQ4dw+/ZtsUMhkbANEBHJG8cBaWLyRWSGcnNzsXDhQuTm5oodComEbYCISN44DkgTky8iIiIiIiITYPJFRERERERkAky+iIiIiIiITIDJF5EZsra2RteuXWFtbS12KCQStgEiInnjOCBNXGqeyAx17twZX3zxhdhhkIjYBoiI5I3jgDRx5ouIiIiIiMgEmHwRmaG0tDSEhoYiLS1N7FBIJGwDRETyxnFAmph8EZkhQRBQVVUFQRDEDoVEwjZARCRvHAekifd8EVHTWFtDtWOb2FHojzcuG5WdrQolJyaIHYZB7Gw59JFxtbR+wD5ARsdzAb2x9xFRkygUCsDGRuwwSCQKhQL2dpZih0EkKvYDkjueC+iPlx0SERERERGZAGe+iMyQl5cXoqOj4eHhIXYoJBK2ASIieeM4IE1MvojMkI2NDXx8fMQOg0TENkBEJG8cB6SJlx0SmaG8vDy8++67yMvLEzsUEgnbABGRvHEckCYmX0RmqKioCPv27UNRUZHYoZBI2AaIiOSN44A0MfkiIiIiIiIyASZfREREREREJsDki4iIiIiIyASYfBGZIQsLCwQHB8PCgl1UrtgGiIjkjeOANPFoEpkhrVaLM2fOQKvVih0KiYRtgIhI3jgOSBOTLyIiIiIiIhNg8kVERERERGQCTL6IiIiIiIhMgMkXkRlycHDA0KFD4eDgIHYoJBK2ASIieeM4IE0qsQMgoto8PDywbNkyscMgEbENEBHJG8cBaeLMF5EZqqysRHZ2NiorK8UOhUTCNkBEJG8cB6SJyReRGcrMzMTo0aORmZkpdigkErYBIiJ54zggTbzskIiaRBAEoCV9K2dtDYVCIXYUkiEIAsrKq8UOwyB2tiq2ATKqltYP2AfI2HguoD8mX0TUNJWVqH5+othR6E21YxtgYyN2GJJRVl6NVn22ix2GQUpOTIC9naXYYZCEtLR+wD5ARsdzAb3xskMiIiIiIiITYPJFRERERERkArzskMgM+fv749SpU2KHQSJiGyAikjeOA9LEmS8iIiIiIiITYPJFZIYuXbqEyZMn49KlS2KHQiJhGyAikjeOA9LE5IvIDJWXl+P8+fMoLy8XOxQSCdsAEZG8cRyQJiZfREREREREJsDki4iIiIiIyASYfBEREREREZkAky8iM+Tm5oalS5fCzc1N7FBIJGwDRETyxnFAmvicLyIz1Lp1awwbNkzsMEhEbANERPLGcUCaOPN1j3Xr1iEyMhJFRUUtqm6SnsLCQuzcuROFhYVih0IiYRsgIpI3jgPSxOTrD0VFRYiIiMDatWvRqlWrFlM3SVN+fj6ioqKQn58vdigkErYBIiJ54zggTUy+/pCQkABBENCtWzcolcoWU7cUaas1EAQBAHT/JSIi+RAEAVotxwEikh4mX39ISEgAAAQHB7eouqVCEATk/ZKEI1PX4Quvl4C7Y60AJP5jD8qv83JNqTt2/Rqsvt+B9y+m3reM1fc7MPLkcRNGRaa0YvYjEBKnYNJIvzq3H9n6FCriX0aQbxsTR0amcuFSEd6MOoG2/b9E3vU7D5bNu16Omct/RVL6TZGja37sAyR3cjgXkHTyVVlZifXr16N///5o06YNrKys4Orqil69eiEiIgK5ubnYvXs3FAoF5s2bBwDYtGkTFAqF7mflypW6+rKyshAVFYWhQ4fCx8cH9vb2sLOzQ/fu3bF69Wqo1eoar29I3QCgVquxZcsWDB48GM7OzrC2toavry/eeustlJSUNPNfSzzq26U4+OJyHHhuKS79eAKCRltje8Kqr7Er5BVk7vuvSBESkSlEfnQGSRdu4v15veHRwa7GtjnjgzCopxve+SgByRm8/0FqBEFA5EcJ6PLMLnzwRTJuFavv2QZ8vDMVD435Fq+t/C80fxkjpIR9gEj6JLvaYUFBAQYPHozExESoVCp07twZvr6+uHLlCuLj4xEXF4dp06ahtLQUoaGhOH36NCoqKhAcHAw7uz8/8Pr166f7/+XLl+PTTz+Fvb093N3dERQUhGvXriExMRGJiYmIi4vD7t27deUNqTsjIwMjR45EcnIyVCoVvL294eLigoyMDKxZswYHDx7E8ePHa+wvBdXllTg4bgUK4tPrLaeprMKxGe8DADoP71dvWSmws7ND7969JXe8SX9ybANV1VpMXByLk18Ox9bIARg68wAAoItXa6x4PQQnEq8h6vMkkaOk5rDwH/FYvTWxwXL/+vdvKK/U4JPI/lAoFCaIzLTYB+hechwH5ECyM19z585FYmIixo4di5ycHKSnpyMuLg65ubnIzs7GmjVr4OvriwkTJiA2NhYWFnf+FDExMfjll190P4MGDdLV+eSTT+L06dMoLi5Geno6Tp06haysLMTFxaFDhw7Ys2cPkpL+/FDUt+6bN29i2LBhSE5OxtSpU3HlyhWkpaXht99+Q0pKCgICApCQkIAPPvjAZH8/Uzn3wa4GE697/TJ7AypkcAmip6cnNmzYAE9PT7FDIZHItQ2c+e0GVm09h7+FdsS00V1hYaHA9hWPQqEAJi6O1d0HRNJxLD5Pr8Trrk+/Tceug1nNF5DI2AfoLrmOA1In2eRrz549UCqV2Lx5Mzp06FBjm4eHByIiInTfmqWnp6OsrAydOnWCk5PTfet84YUX8PDDD9f6ti0kJARDhgwBACQnJ9fYpk/d4eHhyMjIQHh4OLZs2QIXFxfdNj8/P2zYsAEAsHfvXn3eeotRXaFG+leHDdpHU1mFC/+OaaaIzIdGo0FJSQk0Go3YoZhcmUaD65WVdf7IiZzbwPLNZ3A29QbWze2FDQv6onc3FyzacBrpWdL/4kWO/vXv3wze56NvDN+nJWEfIEDe44CUzwUkedlhdXU11Go1NBoNjh49iqeffrre8mfPngUA9OjRo95ylZWV2LdvH44dO4bMzEwUFxdDq71z7fndpMvKysqgupOTk/H111/D1dUVq1atqrPM3X2vXLmi+11OTg5Wr16NU6dO4dy5c1Cr1S1uRajL+0+h8uZtg/dL234Q3V4b1QwRmY8LFy5gwoQJ2L59O/z9/cUOx6SWpSVjWVpywwUlTs5toLpawMTFsYiLHo5XXwjA8YSr+PDL82KHRc0g/0Y5vj2cZfB+R+PykJp5C/6dnYwekzlgHyBA3uOAlM8FJJl8qVQqjBw5Ejt37kRYWBgef/xxjBkzBsOHD4eHh0et8vokX4cPH8akSZOQnZ1d72t7e3sbVHd0dDS0Wi00Go1u9uyv7i7kYW9vr/tdRkYGdu/ejZ49e8LKygq//vprvXHpIyQkBFevXm1yPfoarO2IQeho8H4l2dfg5fEAqhUtK9kcM2aM3mWvXbsGANi/fz9Onz6t936jRpk+KbW1sEBKj75Gq2+qpzdGuz9Q57ZhJ441uf4uXbqgXGv6G/YNOf5A49qAGMdfC0ug7WKj11tUokalWgMrSyV+Op4NY3635NelCyxQZbwKqdEqVZ6odpzSqH1DHx8D26r7r4hmSs3RD9gHpInnAvox93MBV1dXxMfHN2pfSSZfALB9+3YEBgbik08+QUxMDGJiYjBr1iwMGjQIK1euRJ8+fXRlG0qQTp48iaeeegpVVVWYMmUKxo0bhwcffBBt2rSBSqVCamoqAgICoFKpEBAQUGPfhuqOiblzCV1BQQEKCgrqfU9eXl66/3/00UeRl5cHAIiMjDRK8nX16tUas2vNrdyhLWDfcLm6XMvLR7nQsgaO0tJSvcuWl5fr/mvIfqY8fnfZKZVAD+PV59uqFZ5o36Hhgo2Um5uLMhEu4TDkOAKNawNiHH8orIC2xq/2s2UDYGWpRMrFQiye3gM7DmTi95xio9Sdl5sLCOqGC1Lzs3cAHBu3683CYqBIhDZfl2boB+wD0sRzAf1I9VwAkHDyZWNjg8jISLzzzjs4e/YsvvvuO2zatAlHjhzBkCFDkJqaqpsFayhBWrBgAdRqNVavXo358+fX2v7jjz8CAAIDA2FtbV1jW0N15+TkALizjH2nTp30fn93F/EwJldXV6PXWR8Lrc2fz/MyQDW0cHZzgdDCFrq6d+ayIXc/ZG1tbQ3ar66Z3eZm2wxtsTm5u7uLMvNlyHEEGtcGxDj+Wlgiz8h1vj42EI/1csfCf8TjuyOXkPDNSHy6bAAGTf7JKPW7ubvzW38zUaW0wzXgznryBq5e6OxkDZtWpm/zdTF2P2AfkC6eC5iHpp4LNOWcWbLJ110KhQLBwcEIDg7GnDlz8NBDDyE7OxunTp3CqFGjkJ+fj/z8fDg5OdWYWbpLo9EgNjYWADBp0qRa27VaLb766isAtR+i3FDdwJ8dq6Kiognv0jgaO33aWLd/z8Oe0NcN3s93RH9kf7ynGSJqXnFxcXqXTU1NRXR0NIYNG2bQdd4ffvhhIyJrGqGiAtXPTzT56zZWeno6FDY2Jn9dQ44/0Lg2IMbxLy2rQqs+241Wn6+nI1aFh+BUUgHWfJoIrVZA5MYErArvidfHBmLD1ylNfo0L6emwt7M0QrTUVFqtAP8Ru3DhkmH3/7ZvY4Ps+COwtlI2U2SGMWY/YB+QNp4LmAexzgUACa92WBcnJyfdghh3VxRMS0sDcOfaz7qUlpbqVpmpK0F67733cObMGQC1k6+G6gagWz700KFDer8PqXD0doP7oB4G7+c/cajxgzEzvr6+OHDgAHx9fcUOhUQixzagUACfL38USgsFJi4+pltSe+1nSYg7X4BV4SHw7uggcpRkTBYWCrz6fEDDBf9i6rNdzSbxMib2AbqXHMcBOZBc8rVt2zbMnz8fKSk1vxkqLCzEjBkzcPHiRfj7++vu+bo7jZuTk1Pn9bSOjo666dsVK1boEjG1Wo2oqCgsWbIEKtWdCcS/Jl8N1Q38eePlggULsGvXrlrbMzIysHTpUhw9elSv99/S9Jj3PCys9J+A9Xg8GB36GD5QtzQqlUp3TyHJkxzbwNyJ3RAa3AFvf5SA1Mw/l9TWagW8vCQWKqUFPl02QMQIqTlMGtkFfp30v/HLrb0dXh8b2IwRiYd9gO4lx3FADiSXfB07dgxr165FUFAQXFxc0LNnTwQFBcHd3R2bN29Gx44dsXv3biiVd74xCwoKgqenJ3Jzc+Hp6Ym+ffuif//++Pjjj3V1Llq0CACwefNmeHh4oGfPnnBxccGiRYuwYcMGCIIAhUJR674ufep+8803MWDAABQXF+O5556Ds7MzHnnkEQQHB6Ndu3bw8/NDZGSkQfeDtSQuj3TBwI1v6JWAufTyx6BNb9Z6zpoU5eTkYO7cubp7Akl+5NYG/Du3xvJZD+N/567hvW21l9ROuXgLkRsTMDDETbIn3nLV2sEK+z/6G7zcWzVY1qWtDfZ/9CTc2tuZIDLTYh+gv5LbOCAXkku+pk+fjgULFiA0NBRWVlZITEzE5cuXERQUhGXLluH8+fMIDPzzQ8vGxgb79+/HiBEjYGlpiZMnT+LXX39Fq1Z/DgIzZ87E5s2b4efnh5s3byI3NxdhYWE4ffo0evXqBY1GA29vbzg61vzmTp+6bW1tcfjwYaxfvx59+vRBdXU1kpKSkJ+fDy8vL4SHh+PgwYPo3Llz8//xRNLpqd4YumcZPB7rUed2m3at0f2NMXjym7dh2crWtMGJpKSkBMePH0dJSYnYoZjMwHYuUD/zPN70uf917epnnsfe3vL41ldubSA1swi2Pbeh39+/111q9VertyZC8dBWo9zzQubF5wFH/O/LZzB9TFfY2dT+Ms7K0gJ/D/PFiS+Ho3tXZxEibH7sA/RXchsHAHmcC0huHrNPnz41lpHXR2BgIPbu3VtvmWnTpmHatGl1bqvv4cb61G1paYnZs2dj9uzZDYUqWS6PdMGQrxfjdtZVXP7pJOKXf3FngwJ47vTHUFrxxmAiIilzbWeHTW/3x9o3emHHgd8xd91JFJdVo3UrS2T8+DzatRHn5ngiImOS3MyXnOzatQu7du3S3d9299+mXrXQmBy9XPHgqyOgsLhzaaFCoWDiRUQkI60drDBtjD8cW91ZIKuVnSUTLyKSDMnNfMnJc889V+e/J06ciM8//1yEiIiIiIiI6H6YfLVg9V3uSC1b+/btER4ejvbt24sdComEbYCISN44DkgTky8iM+Ts7Ixx48aJHQaJiG2AiEjeOA5IE+/5IjJDt2/fxqFDh3D79m2xQyGRsA0QEckbxwFpYvJFZIZyc3OxcOFC5Obmih0KiYRtgIhI3jgOSBOTLyIiIiIiIhNg8kVERERERGQCTL6IiIiIiIhMgMkXkRmytrZG165dYW1tLXYoJBK2ASIieeM4IE1cap7IDHXu3BlffPGF2GGQiNgGiIjkjeOANHHmi4iIiIiIyASYfBGZobS0NISGhiItLU3sUEgkbANERPLGcUCamHwRmSFBEFBVVQVBEMQOhUTCNkBEJG8cB6SJ93wRUdNYW0O1Y5vYUeiPNy4blZ2tCiUnJogdhkHsbDn0kXG1tH7APkBGx3MBvbH3EVGTKBQKwMZG7DBIJAqFAvZ2lmKHQSQq9gOSO54L6I+XHRIREREREZkAZ76IzJCXlxeio6Ph4eEhdigkErYBIiJ54zggTUy+iMyQjY0NfHx8xA6DRMQ2QEQkbxwHpImXHRKZoby8PLz77rvIy8sTOxQSCdsAEZG8cRyQJiZfRGaoqKgI+/btQ1FRkdihkEjYBoiI5I3jgDQx+SIiIiIiIjIBJl9EREREREQmwOSLiIiIiIjIBJh8EZkhCwsLBAcHw8KCXVSu2AaIiOSN44A08WgSmSGtVoszZ85Aq9WKHQqJhG2AiEjeOA5IE5MvIiIiIiIiE2DyRUREREREZAJMvoiIiIiIiEyAyReRGXJwcMDQoUPh4OAgdigkErYBIiJ54zggTSqxAyCi2jw8PLBs2TKxwyARsQ0QEckbxwFp4swXkRmqrKxEdnY2KisrxQ6FRMI2QEQkbxwHpInJF5EZyszMxOjRo5GZmSl2KCQStgEiInnjOCBNTL6IiIiIiIhMgPd8EVGTCIIAtKRLIqytoVAoxI5CMgRBQFl5tdhhGMTOVsU2QEbV0voB+wAZG88F9Mfki4iaprIS1c9PFDsKval2bANsbMQOQzLKyqvRqs92scMwSMmJCbC3sxQ7DJKQltYP2AfI6HguoDdedkhERERERGQCnPkiMkP+/v44deqU2GGQiNgGiIjkjeOANHHmi4iIiIiIyASYfBGZoUuXLmHy5Mm4dOmS2KGQSNgGiIjkjeOANDH5IjJD5eXlOH/+PMrLy8UOhUTCNkBEJG8cB6SJyRcREREREZEJMPkiIiIiIiIyASZfREREREREJsDki8gMubm5YenSpXBzcxM7FBIJ2wARkbxxHJAmPueLyAy1bt0aw4YNEzsMEhHbABGRvHEckCbOfBGZocLCQuzcuROFhYVih0IiYRsgIpI3jgPSxOTrHuvWrUNkZCSKiopaVN0kPfn5+YiKikJ+fr7YoZBI2AaIiOSN44A0Mfn6Q1FRESIiIrB27Vq0atWqxdRNREREREQtA5OvPyQkJEAQBHTr1g1KpbLF1C0lWo0G2YdO45c3/gVBKwAABK2A0twbIkdGpnDs+jVYfb8D719MvW8Zq+93YOTJ4yaMikxpxexHICROwaSRfnVuP7L1KVTEv4wg3zYmjoxMKSn9Jt5YewI3iioBALeK1TiVVCByVKbBPkByJ4dzASZff0hISAAABAcHt6i6peL3Pcexp9/rOPz3Vcj495Ea23b1mokjU9eh/Dov2SSSssiPziDpwk28P683PDrY1dg2Z3wQBvV0wzsfJSA5g/c/SFFi+k0MmPgDHhrzLT78MhkVlRoAQGl5NXqP24eQF7/Dr2ekffkV+wCR9Ek6+aqsrMT69evRv39/tGnTBlZWVnB1dUWvXr0QERGB3Nxc7N69GwqFAvPmzQMAbNq0CQqFQvezcuVKXX1ZWVmIiorC0KFD4ePjA3t7e9jZ2aF79+5YvXo11Gp1jdc3pG4AUKvV2LJlCwYPHgxnZ2dYW1vD19cXb731FkpKSpr5ryWe8xu/Q+ys9Si5fK3O7YJGi0s/nsBPYQtlMwtmZ2eH3r17w87OruHCJElybANV1VpMXBwLe1tLbI0coPt9F6/WWPF6CE4kXkPU50kiRkjN5b9n89F/4g/4pZ7k6nTKdTwx7Sf8dDzbhJGZFvsA3UuO44AcSHap+YKCAgwePBiJiYlQqVTo3LkzfH19ceXKFcTHxyMuLg7Tpk1DaWkpQkNDcfr0aVRUVCA4OLhGI+/Xr5/u/5cvX45PP/0U9vb2cHd3R1BQEK5du4bExEQkJiYiLi4Ou3fv1pU3pO6MjAyMHDkSycnJUKlU8Pb2houLCzIyMrBmzRocPHgQx48fl1wHvPzzKcQv+0KvssWX8nFowio88/MaWKikffmmp6cnNmzYIHYYJCK5toEzv93Aqq3n8PYrwZg2uiu2fpuO7SsehUIBTFwcC+0flySTdOReK8Xw2QdRXFrVYNlKtRbPzY3B6W9GwL+zU/MHJwL2AbpLruOA1Ek2+Zo7dy4SExMxduxYvP/+++jQoYNu25UrV/DVV1/B19cXfn5+GD9+PBwcHAAAMTExcHJyqrPOJ598ErNmzUJwcDAUCoXu9/Hx8QgLC8OePXuQlJSEbt26AQAmTJigV903b97EsGHDkJGRgalTp2LFihVwcXEBAFy4cAEjRoxAQkICPvjgAyxatMgYfx6zkbh+j0HlC5OzkHPoNDyH9mqmiMyDRqNBeXk5bG1tZXefYJlGg+uVlWKHITo5t4Hlm89g+CBPrJvbCz38ndG7mwvejDqJ9CxeeixFH+9MxY1b+vf5sopqrP8yGRuXhDZjVOJiHyBA3uOAlM8FJHvZ4Z49e6BUKrF58+YaiRcAeHh4ICIiQpdApaeno6ysDJ06dbpv4gUAL7zwAh5++OEaiRcAhISEYMiQIQCA5OTkGtv0qTs8PBwZGRkIDw/Hli1bdIkXAPj5+em+9di7d68+b73FuH42A9fPZhi8X+rnB5ohGvNy4cIFPP7447hw4YLYoZjcsrRkuP/nuzp/5ETObaC6WsDExbGwsVbi1RcCcDzhKj788rzYYVEzUFdpsGV3msH7ffFDBm6XqBsu2EKxDxAg73FAyucCkpz5qq6uhlqthkajwdGjR/H000/XW/7s2bMAgB49etRbrrKyEvv27cOxY8eQmZmJ4uJiaLVaAH8mXVZWVgbVnZycjK+//hqurq5YtWpVnWXu7nvlyhXd73bt2oXo6GjEx8ejoKAAnp6eGD16NBYsWNBilrO/cuRso/bLPXYO2mqN5C89lKupnt4Y7f5AnduGnThm4mhILEUlalSqNbCyVOKn49kQeKWVJCWm38TV6+UG71daXo1fzuTjqQF1f1ZIAfsAyZmUzwUkmXypVCqMHDkSO3fuRFhYGB5//HGMGTMGw4cPh4eHR63y+iRfhw8fxqRJk5CdXf+Nvt7e3gbVHR0dDa1WC41Go5s9+6u7C3nY29vrfrdu3Tp4enpi5cqV6NixI86ePYulS5fi2LFjiI2NhYWF4ZOaISEhuHr1qsH7NdbftJ4YAPdG7dulkzcqFBojR9S8xowZo3fZa9fuLD6yf/9+nD59Wu/9Ro0aZXBcTWVrYYGUHn2NVp9vq1Z4on2Hhgs2UpcuXVD+x5cmpmTI8Qca1wbEOP5aWAJtFxu93s+WDYCVpRIpFwuxeHoP7DiQid9zio1St1+XLrBAw/cXUfOrUHUGHF9u1L7jJ06Hndo8Fp9ojn7APiBNPBfQj7mfC7i6uiI+Pr5R+0oy+QKA7du3IzAwEJ988gliYmIQExODWbNmYdCgQVi5ciX69OmjK9tQgnTy5Ek89dRTqKqqwpQpUzBu3Dg8+OCDaNOmDVQqFVJTUxEQEACVSoWAgIAa+zZUd0xMDIA7C4QUFNT/HBMvLy/d/3///fdo37697t8DBw5E+/btMW7cOPzyyy949NFH662rLlevXq0xu9bcbrZyBFo1LvnKys2GBi3ra8DS0lK9y5aXl+v+a8h+pjx+d9kplUAPk79so+Xm5qJMY/rE3ZDjCDSuDYhx/KGwAtoat8rXxwbisV7uWPiPeHx35BISvhmJT5cNwKDJPxml/rzcXECQ7iVrLYqdNeDYuF0Lr+ehsFiENl8XI/cD9gHp4rmAeRDrXACQcPJlY2ODyMhIvPPOOzh79iy+++47bNq0CUeOHMGQIUOQmpqqmwVrKEFasGAB1Go1Vq9ejfnz59fa/uOPPwIAAgMDYW1tXWNbQ3Xn5OQAuLOMfadOnfR+f/cmXneFhIQAaHync3V1bdR+jVUkWACN+NIhD6Vw9Whc0iame2cuG3L3Q9bW1tag/eqa2W1uto2YZRWTu7u7KDNfhhxHoHFtQIzjr4Ul8oxYn6+nI1aFh+BUUgHWfJoIrVZA5MYErArvidfHBmLD1ylNfg03d3d+628mtArgqqCGoLBquPC9BC1cW6uhdDR9m6+LMfsB+4C08VzAPDT1XKAp58ySTb7uUigUCA4ORnBwMObMmYOHHnoI2dnZOHXqFEaNGoX8/Hzk5+fDycmpxszSXRqNBrGxsQCASZMm1dqu1Wrx1VdfAaj9EOWG6gb+7FgVFRVNeJd3HDly5+HEf51901djp08bS6vRYE/f11GSXffzve7n2bVvYsHf675E05zFxcXpXba6uhovv/wyHBwcoFLp300//PDDRkTWNEJFBaqfn2jy122s9PR0KGxsTP66hhx/oHFtQIzjX1pWhVZ9thulLoUC+Hz5o1BaKDBx8THdktprP0vCs094YVV4CH6MzW7ypVcX0tNhb2dpjJDJCGYs+wWbdxm26MbIJzrj2w+bnoQYi7H6AfuA9PFcwDyIdS4ASHi1w7o4OTnpFsS4u6JgWtqdD/wuXbrUuU9paSk0f0xL1pUgvffeezhz5gyA2slXQ3UDd57hAACHDh3S+33U5cqVK1iyZAmGDh3a4MIh5sJCqUTAlGEG7WPd1gHez/ZvpojMh0ql0l3WSvIkxzYwd2I3hAZ3wNsfJSA1888ltbVaAS8viYVKaYFPlw2opwZqiV57MRBKpaLhgvcIHxfUTNGIi32A7iXHcUAOJJd8bdu2DfPnz0dKSs1vxAoLCzFjxgxcvHgR/v7+unu+7k7j5uTk1Hk9raOjo276dsWKFbpETK1WIyoqCkuWLNF1ir8mXw3VDfx54+WCBQuwa9euWtszMjKwdOlSHD169L7vuaSkBCNGjICVlRU+/fTT+5YzR4HTnobXiH4NFwSgtLHCE5/Ph6W9bTNHJb6cnBzMnTtXd1kqyY/c2oB/59ZYPuth/O/cNby3rfaS2ikXbyFyYwIGhrjh9bGBIkRIzaVbl7bYZMAzu1aFh2BQT7dmjEgc7AP0V3IbB+RCIQjSWrx08uTJ+OyzzwDcuS+qU6dOKCsrw++//46Kigp07NgRBw4cQGDgnQ+uiooKdO3aFZcvX0bbtm3RpUsXKJVKjB8/Hq+88goAYOPGjXj11VcBAB06dMADDzyACxcuoKysDP/6178wc+ZMaLVa3Lp1C46Of945rE/d5eXl+Nvf/objx48DANq2bQsvLy9otVpkZ2fjxo0bAIDff/8dnTt3rvV+y8vL8dRTTyExMRHHjx/Xva+WRFutQVzk50j97GcI2rqbo71HOwzaMhftg/1MHJ3xGHKpQWpqKiZMmIDt27fD399f7/169uzZmNCapKVdaqDasa1FXHbYmDYgxvE35mWHplJyYgIvuTJDX/94Ea+u/C+KiuteCMLORoU1b/TEay+Z3zjX0voB+4B4eC5gHsQ6FwAkOPM1ffp0LFiwAKGhobCyskJiYiIuX76MoKAgLFu2DOfPn6+RoNjY2GD//v0YMWIELC0tcfLkSfz66681npU1c+ZMbN68GX5+frh58yZyc3MRFhaG06dPo1evXtBoNPD29q6ReOlbt62tLQ4fPoz169ejT58+qK6uRlJSEvLz8+Hl5YXw8HAcPHiwzsSrqqoKY8aMQXx8PPbv398iEy8AsFAp0fvdKRhzaiO6vzEGrf06wsbZEfbu7eDxWA88/lkERp/4V4tOvIiIqH5jn/bBlYMvYss7/dG3uwssLO5cimipssA/3uqD3MMvmWXiRURkCMldRNqnT58ay8jrIzAwEHv37q23zLRp0zBt2rQ6t9U3eahP3ZaWlpg9ezZmz57dUKg6Wq0W48aNw+HDh/HTTz+hV69eeu9rruw92iE44kUER7wodihERCQCeztLTB3dFVNHd0XHwdG4cq0MLm1t8PpYad7jRUTyI7nkSy5mzZqFnTt34q233oKdnR1OnDih2+bj41PnUvRERERERCQeyV12KBf79+8HAKxevRp9+/at8XP3uWPUcrVv3x7h4eFMomWMbYCISN44DkgTZ75aqKysLLFDoGbk7OyMcePGiR0GiYhtgIhI3jgOSBNnvojM0O3bt3Ho0CHcvn1b7FBIJGwDRETyxnFAmph8EZmh3NxcLFy4ELm5uWKHQiJhGyAikjeOA9LE5IuIiIiIiMgEmHwRERERERGZAJMvIiIiIiIiE2DyRWSGrK2t0bVrV1hbW4sdComEbYCISN44DkgTl5onMkOdO3fGF198IXYYJCK2ASIieeM4IE2c+SIiIiIiIjIBJl9EZigtLQ2hoaFIS0sTOxQSCdsAEZG8cRyQJiZfRGZIEARUVVVBEASxQyGRsA0QEckbxwFpYvJFRERERERkAlxwg4iaxtoaqh3bxI5Cf1w1yqjsbFUoOTFB7DAMYmfLoY+Mq6X1A/YBMjqeC+iNvY+ImkShUAA2NmKHQSJRKBSwt7MUOwwiUbEfkNzxXEB/TL6IzJCXlxeio6Ph4eEhdigkErYBIiJ54zggTUy+iMyQjY0NfHx8xA6DRMQ2QEQkbxwHpIkLbhCZoby8PLz77rvIy8sTOxQSCdsAEZG8cRyQJiZfRGaoqKgI+/btQ1FRkdihkEjYBoiI5I3jgDQx+SIiIiIiIjIBJl9EREREREQmwOSLiIiIiIjIBJh8EZmhtm3bYuLEiWjbtq3YoZBI2AaIiOSN44A0MfkiMkMWFhawtLSEhQW7qFyxDRARyRvHAWni0SQyQ9evX8cnn3yC69evix0KiYRtgIhI3jgOSBOTLyIiIiIiIhNg8kVERERERGQCTL6IiIiIiIhMgMkXkRlycHDA0KFD4eDgIHYoJBK2ASIieeM4IE0qsQMgoto8PDywbNkyscMgEbENEBHJG8cBaeLMF5EZqqysRHZ2NiorK8UOhUTCNkBEJG8cB6SJyReRGcrMzMTo0aORmZkpdigkErYBIiJ54zggTbzskIiaRBAEoCV9K2dtDYVCIXYUkiEIAsrKq8UOwyB2tiq2ATKqltYP2AfI2HguoD8mX0TUNJWVqH5+othR6E21YxtgYyN2GJJRVl6NVn22ix2GQUpOTIC9naXYYZCEtLR+wD5ARsdzAb3xskMiIiIiIiITYPJFRERERERkArzskMgM+fv749SpU2KHQSJiGyAikjeOA9LEmS8iIiIiIiITYPJFZIYuXbqEyZMn49KlS2KHQiJhGyAikjeOA9LE5IvIDJWXl+P8+fMoLy8XOxQSCdsAEZG8cRyQJiZfREREREREJsDki4iIiIiIyASYfBEREREREZkAky8iM+Tm5oalS5fCzc1N7FBIJGwDRETyxnFAmvicLyIz1Lp1awwbNkzsMEhEbANERPLGcUCaOPN1j3Xr1iEyMhJFRUUtqm6SnsLCQuzcuROFhYVih0IiYRsgIpI3jgPSxOTrD0VFRYiIiMDatWvRqlWrFlM3SVN+fj6ioqKQn58vdigkErYBIiJ54zggTUy+/pCQkABBENCtWzcolcoWUzcREZHUJKXfRHmlBgCgrtJCEASRIyIiMg4mX39ISEgAAAQHB7eouomk4tj1a7D6fgfev5h63zJW3+/AyJPHTRgVmdKK2Y9ASJyCSSP96tx+ZOtTqIh/GUG+bUwcGZlCVZUWn+1NR8+XvsNDY77FzaJKAEBBYQW6PbsH//p3CsorqkWOsnmxD5DcyeFcQNLJV2VlJdavX4/+/fujTZs2sLKygqurK3r16oWIiAjk5uZi9+7dUCgUmDdvHgBg06ZNUCgUup+VK1fq6svKykJUVBSGDh0KHx8f2Nvbw87ODt27d8fq1auhVqtrvL4hdQOAWq3Gli1bMHjwYDg7O8Pa2hq+vr546623UFJS0sx/LSIicUV+dAZJF27i/Xm94dHBrsa2OeODMKinG975KAHJGbz/QWqKS9V4+rUDmPz2ccQnX6+1PfniLby28n8YNPknFNwsFyFC02AfIJI+ySZfBQUF6NWrF+bMmYOTJ0+iffv26N69OywsLBAfH4+oqCiUlpaitLQUoaGhsLGxAXBndio0NFT3069fP12dy5cvR0REBH755RcolUoEBQXBxcUFiYmJWLBgAV566aUaMRhSd0ZGBh5++GFMnz4dx44dQ7t27eDt7Y1Lly5hzZo1GDhwIMrKykzwlyNzYGdnh969e8POzq7hwiRJcmwDVdVaTFwcC3tbS2yNHKD7fRev1ljxeghOJF5D1OdJIkZIzaGqSovn5sbg4P9yGyx76nwBwl47iLJyac6AsQ/QveQ4DsiBZJeanzt3LhITEzF27Fi8//776NChg27blStX8NVXX8HX1xd+fn4YP348HBwcAAAxMTFwcnKqs84nn3wSs2bNQnBwMBQKhe738fHxCAsLw549e5CUlIRu3boBACZMmKBX3Tdv3sSwYcOQkZGBqVOnYsWKFXBxcQEAXLhwASNGjEBCQgI++OADLFq0yBh/HjJznp6e2LBhg9hhkIjk2gbO/HYDq7aew9uvBGPa6K7Y+m06tq94FAoFMHFxLLRa3vsjNbsOZuLAf6/oXf7U+QJs2Z2K8PEPNmNU4mEfoLvkOg5InWRnvvbs2QOlUonNmzfXSLwAwMPDAxEREboEKj09HWVlZejUqdN9Ey8AeOGFF/Dwww/XSLwAICQkBEOGDAEAJCcn19imT93h4eHIyMhAeHg4tmzZoku8AMDPz0/X8fbu3avPWycJ0Gg0KCkpgUajETsUkyvTaHC9srLOHzmRcxtYvvkMzqbewLq5vbBhQV/07uaCRRtOIz2Lj+qQoo+++c3gfTbuSJX0IhzsAwTIexyQ8rmAJGe+qquroVarodFocPToUTz99NP1lj979iwAoEePHvWWq6ysxL59+3Ds2DFkZmaiuLgYWq0WwJ9Jl5WVlUF1Jycn4+uvv4arqytWrVpVZ5m7+1658uc3g8ePH8fSpUuRnJyMmzdvon379ggNDUVkZCQCAgLqfR9k/i5cuIAJEyZg+/bt8Pf3Fzsck1qWloxlackNF5Q4ObeB6moBExfHIi56OF59IQDHE67iwy/Pix0WNYOL2bfxyxnDl9FOyyrCycQC9Onu0nDhFoh9gAB5jwNSPheQZPKlUqkwcuRI7Ny5E2FhYXj88ccxZswYDB8+HB4eHrXK65N8HT58GJMmTUJ2dna9r+3t7W1Q3dHR0dBqtdBoNLrZs7+6u5CHvb297neFhYXo1q0bZsyYARcXF+Tk5GDVqlXo27cvzp8/j44dO9YbZ11CQkJw9epVg/cj/YwZM0bvsteuXQMA7N+/H6dPn9Z7v1GjRhkcV1PZWlggpUdfo9U31dMbo90fqHPbsBPHmlx/ly5dUP7HlyamZMjxBxrXBsQ4/lpYAm0XG73eohI1KtUaWFkq8dPxbBhzksOvSxdYoMp4FVKjVaq8AMdJjdr3qVF/h53aPE7OmqMfsA9IE88F9GPu5wKurq6Ij49v1L6STL4AYPv27QgMDMQnn3yCmJgYxMTEYNasWRg0aBBWrlyJPn366Mo2lCCdPHkSTz31FKqqqjBlyhSMGzcODz74INq0aQOVSoXU1FQEBARApVLVmnVqqO6YmBgAdxYIKSgoqPc9eXl56f5/+PDhGD58eI3tPXv2RNeuXbF7926Eh4fXW1ddrl69WmN2jYyrtLRU77Ll5eW6/xqynxjHz06pBHoYrz7fVq3wRPsODRdspNzcXJSJcAmHIccRaFwbEKX/KqyAtsav9rNlA2BlqUTKxUIsnt4DOw5k4vecYqPUnZebCwjqhgtS87O3Bxwbt2vhjRsovG0mY1Yz9AP2AWniuYB+pHouAEg4+bKxsUFkZCTeeecdnD17Ft999x02bdqEI0eOYMiQIUhNTdXNgjWUIC1YsABqtRqrV6/G/Pnza23/8ccfAQCBgYGwtrausa2hunNycgDcWca+U6dOBr7LmpydnQHcmflrDFdX1ya9PtXv3pnLhtz9kLW1tTVov7pmdpubrUXLunXU3d1dlJkvQ44j0Lg2IMbx18ISeUau8/WxgXislzsW/iMe3x25hIRvRuLTZQMwaPJPRqnfzd2d3/qbiWoLK+QDgCAAf7mf+r7+KNveSQUrB9O3+boYux+wD0gXzwXMQ1PPBZpyzizZ5OsuhUKB4OBgBAcHY86cOXjooYeQnZ2NU6dOYdSoUcjPz0d+fj6cnJxqzCzdpdFoEBsbCwCYNKn2pRFarRZfffUVgNoPUW6obuDPjlVRUdGo96fRaKDVanHp0iUsWLAArq6ueP755xtVV2OnT0k/cXFxepdNTU1FdHQ0hg0bZtB13h9++GEjImsaoaIC1c9PNPnrNlZ6ejoUfzz+wZQMOf5A49qAGMe/tKwKrfpsN1p9vp6OWBUeglNJBVjzaSK0WgGRGxOwKrwnXh8biA1fpzT5NS6kp8PeztII0ZIxDJz0I2JPG3DJu0IB/86tkbL3f7UWwBKLMfsB+4C08VzAPIh1LgBIeLXDujg5OekWxLi7omBaWhqAO9d+1qW0tFS3ykxdCdJ7772HM2fOAKidfDVUN3BnGVEAOHTokN7v414DBw6ElZUV/Pz8cPbsWcTExKB9+/aNqovMh6+vLw4cOABfX1+xQyGRyLENKBTA58sfhdJCgYmLj+mW1F77WRLizhdgVXgIvDs6iBwlGdurLxi+SNSrLwSYTeJlTOwDdC85jgNyILnka9u2bZg/fz5SUmp+M1RYWIgZM2bg4sWL8Pf3193zdXcaNycnp87raR0dHXXTtytWrNAlYmq1GlFRUViyZInuMr+/Jl8N1Q38eePlggULsGvXrlrbMzIysHTpUhw9erTO/bdu3YoTJ04gOjoajo6OePLJJ3H58uU6y1LLoVKpdPcUkjzJsQ3MndgNocEd8PZHCUjN/HNJba1WwMtLYqFSWuDTZQPqqYFaotGDvfDUAP0Xierb3QVTn+3ajBGJh32A7iXHcUAOJJd8HTt2DGvXrkVQUBBcXFzQs2dPBAUFwd3dHZs3b0bHjh2xe/duKJVKAEBQUBA8PT2Rm5sLT09P9O3bF/3798fHH3+sq/Pug403b94MDw8P9OzZEy4uLli0aBE2bNgAQRCgUChq3delT91vvvkmBgwYgOLiYjz33HNwdnbGI488guDgYLRr1w5+fn6IjIy87/1gXbt2Re/evfHiiy/i8OHDKC4uxtq1a438VyVTy8nJwdy5c3X3BJL8yK0N+HdujeWzHsb/zl3De9tqL6mdcvEWIjcmYGCIG14fGyhChNRcVCoL7Ih6XK8ErF8PF3y/YQhsbaR3Mso+QH8lt3FALiT36TV9+nS4uroiNjYWWVlZSExMhJWVFYKCgjBixAjMnj0brVu31pW3sbHB/v37sXDhQpw4cQInT56EIAh45ZVXdGVmzpwJlUqFqKgoZGVlQalUIiwsDPPnz9ctE+/j4wNHx5pLNulTt62tLQ4fPoyNGzciOjoaKSkpSEpKQrt27eDl5YXx48cjLCwMnTt3bvC9Ozk5wdfXFxkZGUb4S5KYSkpKcPz4cUybNk3sUExmYDsXqJ+p/37FhrZLidzaQGpmEWx7bqu3zOqtiVi9NdFEEZEp2dtZ4rv1Q/Dvn3/HR9/8hv+du1Zjew//tnj1+QD8/Rlf2FhL7tQFAPsA1Sa3cQCQx7mA5D7B+vTpU2MZeX0EBgZi79699ZaZNm3afRu/UM/DN/Sp29LSErNnz8bs2bMbCrVe165dQ1paGnr37t2keoiIiExNpbLA+DBfjA/zRWrmLfSf8ANuFFXCpa0NEr4ZKcl7vIhIfiSXfMnF+PHj4evrix49esDJyQkXLlzABx98AJVKhTfeeEPs8IiIiBrNv7MTbKzv3B5gqbJg4kVEksHkq4Xq06cPtm/fjvXr16OiogIPPPAAHnvsMSxcuLDJzwsjIiIiIiLjY/LVQr322mt47bXXxA6Dmkn79u0RHh7OxwbIGNsAEZG8cRyQJiZfRGbI2dkZ48aNEzsMEhHbABGRvHEckCbJLTVPJAW3b9/GoUOHcPv2bbFDIZGwDRARyRvHAWli8kVkhnJzc7Fw4ULk5uaKHQqJhG2AiEjeOA5IE5MvIiIiIiIiE2DyRUREREREZAJMvoiIiIiIiEyAyReRGbK2tkbXrl1hbW0tdigkErYBIiJ54zggTVxqnsgMde7cGV988YXYYZCI2AaIiOSN44A0ceaLiIiIiIjIBJh8EZmhtLQ0hIaGIi0tTexQSCRsA0RE8sZxQJqYfBGZIUEQUFVVBUEQxA6FRMI2QEQkbxwHpIn3fBFR01hbQ7Vjm9hR6I83LhuVna0KJScmiB2GQexsOfSRcbW0fsA+QEbHcwG9sfcRUZMoFArAxkbsMEgkCoUC9naWYodBJCr2A5I7ngvoj5cdEhERERERmQBnvojMkJeXF6Kjo+Hh4SF2KCQStgEiInnjOCBNTL6IzJCNjQ18fHzEDoNExDZARCRvHAekiZcdEpmhvLw8vPvuu8jLyxM7FBIJ2wARkbxxHJAmJl9EZqioqAj79u1DUVGR2KGQSNgGiIjkjeOANDH5IiIiIiIiMgEmX0RERERERCbA5IuIiIiIiMgEmHwRmSELCwsEBwfDwoJdVK7YBoiI5I3jgDTxaBKZIa1WizNnzkCr1YodComEbYCISN44DkgTky8iIiIiIiITYPJFRERERERkAky+iIiIiIiITIDJF5EZcnBwwNChQ+Hg4CB2KCQStgEiInnjOCBNKrEDIKLaPDw8sGzZMrHDIBGxDRARyRvHAWnizBeRGaqsrER2djYqKyvFDoVEwjZARCRvHAekickXkRnKzMzE6NGjkZmZKXYoJBK2ASIieeM4IE287JCImkQQBKAlfStnbQ2FQiF2FJIhCALKyqvFDsMgdrYqo7aBFtcHAPYDI2txbcDIx5+fAyT3PmAIJl9E1DSVlah+fqLYUehNtWMbYGMjdhiSUVZejVZ9tosdhkFKTkyAvZ2l8SpsYX0AYD8wuhbWBox9/Pk5QHLvA4bgZYdEREREREQmwOSLiIiIiIjIBHjZIZEZ8vf3x6lTp8QOg0TENkBEJG8cB6SJM19EREREREQmwOSLyAxdunQJkydPxqVLl8QOhUTCNkBEJG8cB6SJlx0SmaHy8nKcP38e5eXlYodCImEbILkpLavCufSbOJ1yHeczCnGz6M6y1YW3K/H+9iQ8EtgOwf7OcGxlJXKkRKbBcUCamHwRERGRKARBwP/OXcNH3/yGnf/JhLpKW6tMWYUGc9fdue9FqVRg+CBPzHohEI/3duNzmoioxWHyRURERCaXlH4T05b+gpNJBXrvo9EI+PbwJXx7+BIe9G2Dze+Eom/3Ds0YJRGRcfGeLyIiIjKZ6mot3t18Bo+8+J1Biddfnc8oRP+JP+L/3juF8opqI0ZIRNR8mHwRmSE3NzcsXboUbm5uYodCImEbICkqr6jGqDmHsOSfCaiqrn2JoaG0WgHrtiXhyRk/o6hYbYQIicwHxwFpYvJFZIZat26NYcOGoXXr1mKHQiJhGyCpUVdp8Owbh/FDbLbR6/7lTD6GzvwZJWVVRq+bSCwcB6SJydc91q1bh8jISBQVFbWoukl6CgsLsXPnThQWFoodComEbYCkZs6aE/j515xmq/9EYgFeXhwLQRCa7TWITInjgDQx+fpDUVERIiIisHbtWrRq1arF1E3SlJ+fj6ioKOTn54sdComEbYCk5NCJK9i4I9WgfeKihyP74IuIix6u9z67D2Xhm59/NzQ8IrPEcUCamHz9ISEhAYIgoFu3blAqlS2mbiIiInNWUlaFKe8cN3g/13Z26NjBHq7t7Azab9bK/+HaDT4XiYjME5OvPyQkJAAAgoODW1TdRFJx7Po1WH2/A+9fvP+341bf78DIk4afxFHLsGL2IxASp2DSSL86tx/Z+hQq4l9GkG8bE0dmGlLtA9v2XcDlvFKTvd7Nokr869+/mez1jEWqx98Qcv8MIHn0A0knX5WVlVi/fj369++PNm3awMrKCq6urujVqxciIiKQm5uL3bt3Q6FQYN68eQCATZs2QaFQ6H5Wrlypqy8rKwtRUVEYOnQofHx8YG9vDzs7O3Tv3h2rV6+GWl1zpSVD6gYAtVqNLVu2YPDgwXB2doa1tTV8fX3x1ltvoaSkpJn/WkRE4or86AySLtzE+/N6w6NDzdmOOeODMKinG975KAHJGbz/oaUQBAEffWP6RGjLnjRU1fHAZjJv/AwgOZDsQ5YLCgowePBgJCYmQqVSoXPnzvD19cWVK1cQHx+PuLg4TJs2DaWlpQgNDcXp06dRUVGB4OBg2Nn92eH79eun+//ly5fj008/hb29Pdzd3REUFIRr164hMTERiYmJiIuLw+7du3XlDak7IyMDI0eORHJyMlQqFby9veHi4oKMjAysWbMGBw8exPHjx2vsT9JlZ2eH3r1783jLmBzbQFW1FhMXx+Lkl8OxNXIAhs48AADo4tUaK14PwYnEa4j6PEnkKMkQvyTkI+XiLZO/bl5BGfYdvYTRQzqb/LWp8fgZUJMcxwE5kGzyNXfuXCQmJmLs2LF4//330aFDB922K1eu4KuvvoKvry/8/Pwwfvx4ODg4AABiYmLg5ORUZ51PPvkkZs2aheDgYCgUCt3v4+PjERYWhj179iApKQndunUDAEyYMEGvum/evIlhw4YhIyMDU6dOxYoVK+Di4gIAuHDhAkaMGIGEhAR88MEHWLRokTH+PGTmPD09sWHDBrHDIBHJtQ2c+e0GVm09h7dfCca00V2x9dt0bF/xKBQKYOLiWGi1XMmuJTkSlyfaax+Nv8rkqwXiZ8Cf5DoOSJ1kk689e/ZAqVRi8+bNsLe3r7HNw8MDERERun+np6ejrKwMnTp1um/iBQAvvPBCnb8PCQnBkCFD8OWXXyI5OVmXfOlbd3h4ODIyMhAeHo4PP/ywxjY/Pz9s2LABgwcPxt69e5l8yYRGo0F5eTlsbW1lt0hLmUaD65WVYochOjm3geWbz2D4IE+sm9sLPfyd0bubC96MOon0LHk8qkNKfeB0ynVZvnZTSOn4N5bcPwPukvM4IOV+IMnkq7q6Gmq1GhqNBkePHsXTTz9db/mzZ88CAHr06FFvucrKSuzbtw/Hjh1DZmYmiouLodXeuaY8OTkZAGBlZWVQ3cnJyfj666/h6uqKVatW1Vnm7r5Xrly5b2zDhg3Dzz//jHfeeQeRkZH1vg8yfxcuXMCECROwfft2+Pv7ix2OSS1LS8aytGSxwxCdnNtAdbWAiYtjERc9HK++EIDjCVfx4ZfnxQ7LZKTUB86k3hD1tTUaLZTKlnV7u5SOf2PJ/TPgLjmPA1LuB5JMvlQqFUaOHImdO3ciLCwMjz/+OMaMGYPhw4fDw8OjVnl9kq/Dhw9j0qRJyM7Orve1vb29Dao7OjoaWq0WGo0GQ4YMqbPM3YU8/jqDd9eOHTt0r9MUISEhuHr1apProbqNGTNG77LXrl0DAOzfvx+nT5/We79Ro0YZHFdT2VpYIKVHX6PVN9XTG6PdH6hz27ATx5pcf5cuXVCuNf2N+IYcf6BxbUCM46+FJdB2sdHrLSpRo1KtgZWlEj8dz4Yxn5vr16ULLFBltPpaWh8ATNcPrrRZDCgs69wWFz283mXkXdvZ6v6bffDF+5a7er0MPV/aV+v3FZUaPNDJFxZQ17GXcRmzDbTE498cnwPN+RkAGP9zQF88F9CPuZ8LuLq6Ij4+vlH7SjL5AoDt27cjMDAQn3zyCWJiYhATE4NZs2Zh0KBBWLlyJfr06aMr21CCdPLkSTz11FOoqqrClClTMG7cODz44INo06YNVCoVUlNTERAQAJVKhYCAgBr7NlR3TEwMgDsLhBQUFNT7nry8vGr97vbt25gzZw7WrVuH8ePH17t/Q65evVrv7Bo1TWmp/kstl5eX6/5ryH5iHD87pRLoYbz6fFu1whPtOzRcsJFyc3NRptE0W/33Y8hxBBrXBkTpvworoK3xq/1s2QBYWSqRcrEQi6f3wI4Dmfg9p9godefl5gKC8U7IW1ofAEzYD9rcf9bp7nO8GqJSWuhVri55V68BmuZf5t6YbaBFHv9m+Bxozs8AwPifA/riuYB+pHouAEg4+bKxsUFkZCTeeecdnD17Ft999x02bdqEI0eOYMiQIUhNTdXNgjWUIC1YsABqtRqrV6/G/Pnza23/8ccfAQCBgYGwtrausa2hunNycgDcWca+U6dOBr5LYNGiRejSpQvGjRvX5OTL1dW1SftT/e43c1mXux+ytra2Bu1X18xuc7O1aFmX9Li7u4sy82XIcQQa1wbEOP5aWMLYSyq8PjYQj/Vyx8J/xOO7I5eQ8M1IfLpsAAZN/sko9bu5uxt95qulMVU/yIUGAuq+V+Xq9bJ693VtZwuV0gLVGi2uXr//Q5Prq8fNtT0s4KRXrE3R0tqAsY+/sT8HmvszADD+54C+eC5gHpraB5pyzizZ5OsuhUKB4OBgBAcHY86cOXjooYeQnZ2NU6dOYdSoUcjPz0d+fj6cnJzqnFnSaDSIjY0FAEyaNKnWdq1Wi6+++gpA7YcoN1Q38GfHqqioMPi9xcfHY8uWLQZNRTdUHzWfuLg4vcumpqYiOjoaw4YNM+g6778u2GIKQkUFqp+faPLXbaz09HQobGxM/rqGHH+gcW1AjONfWlaFVn22G60+X09HrAoPwamkAqz5NBFarYDIjQlYFd4Tr48NxIavU5r8GhfS02FvV/elcI3R0voAYLp+EDBiF1Iz614koa5LBe+VffBFdOxgj6vXy/HAkH8b/NrOTta4cu73GqsTN5eW1gaMffyN+Tlgis8AwPifA/riuYB5EOtcAJD4Q5b/ysnJSbcgxt2l3NPS0gDcufazLqWlpdD8MS1ZV4L03nvv4cyZMwBqJ18N1Q3cWUYUAA4dOqT3+wDuJIUzZszAa6+9hqCgIIP2JfPn6+uLAwcOwNfXV+xQSCRybAMKBfD58kehtFBg4uJjuiWl136WhLjzBVgVHgLvjg4iR0mGeCSwnaivbYrEi4yHnwE1yXEckAPJJV/btm3D/PnzkZJS85uRwsJCzJgxAxcvXoS/v7/unq+707g5OTl1Xk/r6Oiom75dsWKFLhFTq9WIiorCkiVLoFLdmUD8a/LVUN3AnzdeLliwALt27aq1PSMjA0uXLsXRo0dr/P6f//wn8vPzubKhRKlUKt09hSRPcmwDcyd2Q2hwB7z9UUKN2RKtVsDLS2KhUlrg02UDRIyQDCVq8hUg3mtT4/AzoCY5jgNyILnk69ixY1i7di2CgoLg4uKCnj17IigoCO7u7ti8eTM6duyI3bt3656XEBQUBE9PT+Tm5sLT0xN9+/ZF//798fHHH+vqvPtsrc2bN8PDwwM9e/aEi4sLFi1ahA0bNkAQBCgUilr3delT95tvvokBAwaguLgYzz33HJydnfHII48gODgY7dq1g5+fHyIjI2vcD3b9+nUsWbIEb7/9Nqqrq3Hr1i3cunULwJ3ZuVu3bumWwKeWKScnB3PnztXdE0jyI7c24N+5NZbPehj/O3cN722rvaR0ysVbiNyYgIEhbnh9bKAIEVJjhD1a92plJnntgeK9NhmOnwG1yW0ckAvJpdLTp0+Hq6srYmNjkZWVhcTERFhZWSEoKAgjRozA7Nmz0bp1a115Gxsb7N+/HwsXLsSJEydw8uRJCIKAV155RVdm5syZUKlUiIqKQlZWFpRKJcLCwjB//nzdMvE+Pj5wdHSsEYs+ddva2uLw4cPYuHEjoqOjkZKSgqSkJLRr1w5eXl4YP348wsLC0LlzZ90+OTk5KC4uxowZMzBjxowar7lmzRqsWbMGmZmZ973PjMxfSUkJjh8/jmnTpokdiskMbOcC9TPP11umoe1SIrc2kJpZBNue2+ots3prIlZvTTRRRKYnxT7g16k1hvR1x8H/5Zr0dbt3bYu+3V1M+ppNJcXjbwh+BtQmt3EAkEc/kFzy1adPnxrLyOsjMDAQe/furbfMtGnT7tv4hXoePqFP3ZaWlpg9ezZmz57dUKgA7lwDfOTIkVq/f+yxxzBx4kS8/PLLXLmQiIjMwqsvBJg8+Zr5fADv9yIisyS55EsOWrVqhUGDBtW5zcvL677biIiITG34oE4YGOKKY/FXTfJ63fzaYNJIP5O8FhGRoSR3zxcRERGZDwsLBbYuHQA7m+b/vlepVODz5Y/CyrLuZ4sREYmNM18SUt/lj9SytG/fHuHh4Wjfvr3YoZBI2AZISnwecMSHEb0xfdmveu9z9+HJDT2M+V5LZz6Mh0VcYZHImDgOSBOTLyIz5OzsjHHjxokdBomIbYCkZtoYf1y9UY63/5WgV/mGHsL8V6+9FIiF07o3JjQis8RxQJp42SGRGbp9+zYOHTqE27dvix0KiYRtgKRoyYxgrH2jJ4y9Fsb8yQ/hH2/14SIbJCkcB6SJyReRGcrNzcXChQuRm2vaFcLIfLANkFT936SHEPvZ0/D1dGy4cAM8XOzw07+exOo5PZl4keRwHJAmJl9ERERkUv0fdsW5naOwYEp3tHG0Mnj/VnaWeH1sIM7veRbDBvBhykTUcvCeLyIiIjI5O1sVVoaHYPH0HvjmwO/4/LsLiE++jrKK6jrLW1spEezfFuPDfPH3MF84tjI8aSMiEhuTLyIiIhKNna0Kk0Z2waSRXaDRaJGaWYSkCzdRWl4NrVaAva0lAn2cEOTTBpaWvGCHiFo2Jl9EZsja2hpdu3aFtbW12KGQSNgGSI6USgsE+bZBkG8bsUMhEh3HAWli8kVkhjp37owvvvhC7DBIRGwDRETyxnFAmjh/T0REREREZAJMvojMUFpaGkJDQ5GWliZ2KCQStgEiInnjOCBNTL6IzJAgCKiqqoIgCGKHQiJhGyAikjeOA9LEe76IqGmsraHasU3sKPTHG5eNys5WhZITE8QOwyB2tkYe+lpaHwDYD4ytpbUBIx9/fg6Q3PuAIdjyiKhJFAoFYGMjdhgkEoVCAXs7S7HDEBX7AMm9DfBzgOTeBwzByw6JiIiIiIhMgDNfRGbIy8sL0dHR8PDwEDsUEgnbABGRvHEckCYmX0RmyMbGBj4+PmKHQSJiGyAikjeOA9LEyw6JzFBeXh7effdd5OXliR0KiYRtgIhI3jgOSBOTLyIzVFRUhH379qGoqEjsUEgkbANERPLGcUCamHwRERERERGZAJMvIiIiIiIiE2DyRUREREREZAJMvojMUNu2bTFx4kS0bdtW7FBIJGwDRETyxnFAmhSCIAhiB0FERERERCR1nPkiIiIiIiIyASZfREREREREJsDki4iIiIiIyASYfBEREREREZkAky8iIiIiIiITYPJFRERERERkAky+iIiIiIiITIDJFxERERERkQkw+SIiIiIiIjIBJl9EREREREQmwOSLiIiIiIjIBJh8ERERERERmQCTLyIiIiIiIhNg8kVERERERGQCTL6IiIiIiIhM4P8BBoembpi3pS4AAAAASUVORK5CYII=",
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N0tX55JNP4vTp0yguLkZ6ejpOnTqFrKwsxMXFoUOHDtizZw+Skv78UNS37ps3b2LYsGFITk7G1KlTceXKFaSlpeG3335DSkoKAgICkJCQgA8++MBkfz9TObP2G1SXVepdPnPvr7h+NqMZIzIPnp6e2LBhAzw9PcUOhUQi1zZw5rcbWLX1HP4W2hHTRneFhYUC21c8CoUCmLg4FlqtIHaIZGRf/JCBc2k39S7/e04x/vXv35oxInGxD9Bdch0HpE6yydeePXugVCqxefNmdOjQocY2Dw8PREREQKFQAADS09NRVlaGTp06wcnJ6b51vvDCC3j44Yd1+90VEhKCIUOGAACSk5NrbNOn7vDwcGRkZCA8PBxbtmyBi4uLbpufnx82bNgAANi7d68+b73FKMkpQM7BeIP3S/38QDNEY140Gg1KSkqg0WjEDsXkyjQaXK+srPNHTuTcBpZvPoOzqTewbm4vbFjQF727uWDRhtNIz5Lu5WZyJQgCPvrG8ERq065UVFVpmyEi88A+QIC8xwEpnwtIMvmqrq6GWq2GRqPB0aNHGyx/9uxZAECPHj3qLVdZWYmdO3fitddew9NPP41HH30U/fv3R//+/fHDDz8AAKysrAyqOzk5GV9//TVcXV2xatWqOsvc3ffKlSs1fp+ZmYnhw4fDwcEBbdq0wYQJE3Djxo1634M5yfrhfxAa8Q1e5ne/QtBKd9AFgAsXLuDxxx/HhQu1b7iWumVpyXD/z3d1/siJnNtAdbWAiYtjYWOtxKsvBOB4wlV8+OV5scOiZpBy8RaSLhh+/1LutTL8cuZqM0RkHtgHCJD3OCDlcwFJrnaoUqkwcuRI7Ny5E2FhYXj88ccxZswYDB8+HB4eHrXK65N8HT58GJMmTUJ2dna9r+3t7W1Q3dHR0dBqtdBoNLrZs7+6u5CHvb297nfFxcV47LHH0LZtW0RHR6O8vBwREREICwvDr7/+qrvU0RAhISG4etV0g9mTWk88CneD99NUqOH7QGdUKlrWN0FjxozRu+y1a9cAAPv378fp06f13m/UqFEGx9VUthYWSOnR12j1TfX0xmj3B+rcNuzEsSbX36VLF5SLkLwbcvyBxrUBMY6/FpZA28VGr7eoRI1KtQZWlkr8dDwbghGvtPLr0gUWqDJehdRolSovwHFSo/Yd/eJk2KnNIyFpjn7APiBNPBfQj7mfC7i6uiI+3vCrtwCJJl8AsH37dgQGBuKTTz5BTEwMYmJiMGvWLAwaNAgrV65Enz59dGUbSpBOnjyJp556ClVVVZgyZQrGjRuHBx98EG3atIFKpUJqaioCAgKgUqkQEBBQY9+G6o6JiQFwZ4GQgoKCet+Tl5eX7v83b96MK1euIDY2VnctcMeOHdGvXz/s27cPI0eOrLeuuly9erXW7FpzKmrlBLQyPPkCgJzcK1CjZSVfpaWlepctLy/X/deQ/Ux5/O6yUyqBHsarz7dVKzzRvkPDBRspNzcXZSJcwmHIcQQa1wbEOP5QWAFtjV/tZ8sGwMpSiZSLhVg8vQd2HMjE7znFRqk7LzcXELhyqlmwswMcG7dr4Y0CFN4Woc3XpRn6AfuANPFcQD9SPRcAJJx82djYIDIyEu+88w7Onj2L7777Dps2bcKRI0cwZMgQpKam6mbBGkqQFixYALVajdWrV2P+/Pm1tv/4448AgMDAQFhbW9fY1lDdOTk5AO4sY9+pUye9398PP/yA/v3717gJs2/fvvD29sb333/fqOTL1dXV4H2aQq21BBrxTd5tqNHe3RVQNFzWnNw7c9mQux+ytra2Bu1X18xuc7NtYQ/Adnd3F2Xmy5DjCDSuDYhx/LWwRJ6R63x9bCAe6+WOhf+Ix3dHLiHhm5H4dNkADJr8k1Hqd3N357f+ZkKjUOKqIAAKAz7Q/yjv4iTA0sH0bb4uxu4H7APSxXMB89DUc4GmnDNLNvm6S6FQIDg4GMHBwZgzZw4eeughZGdn49SpUxg1ahTy8/ORn58PJyenGjNLd2k0GsTGxgIAJk2qfWmEVqvFV199BaD2Q5Qbqhv4s2NVVBi2bG5KSgqee+65Wr8PCgpCSkqKQXXd1djp08aqKqvAjuDpqLpt2BL6j84bj9lz9zVTVM0nLi5O77KpqamIjo7GsGHD4O9f90MG6/Lhhx82IrKmESoqUP38RJO/bmOlp6dD8cfjH0zJkOMPNK4NiHH8S8uq0KrPdqPV5+vpiFXhITiVVIA1nyZCqxUQuTEBq8J74vWxgdjwdeM+3+51IT0d9naWRoiWjGH46wfx/bHL+u+gUKB3t/Y48VVC8wVlIGP2A/YBaeO5gHkQ61wAkOiCG/fj5OSkWxDj7oqCaWlpAO5c+1mX0tJS3SozdSVI7733Hs6cOQOgdvLVUN0AdDNXhw4d0vt9AEBhYWGdqye2bdsWN2/qv2SvmCztbOD7/GMG7aNQKdFl3OBmish8+Pr64sCBA/D19RU7FBKJHNuAQgF8vvxRKC0UmLj4mG5J7bWfJSHufAFWhYfAu6ODyFGSsb36QkDDhYywT0vAPkD3kuM4IAeSS762bduG+fPn15r9KSwsxIwZM3Dx4kX4+/vr7vm6O42bk5NT5/W0jo6OuunbFStW6BIxtVqNqKgoLFmyBCrVnQnEvyZfDdUN/Hnj5YIFC7Br165a2zMyMrB06VK9Vm1siYL/73m0CdD/+RV9Vk6FnWsz3GBiZlQqle6eQpInObaBuRO7ITS4A97+KAGpmX8uqa3VCnh5SSxUSgt8umxAPTVQS/S3UA9MH9NV7/LPPuGF8WHSPBllH6B7yXEckAPJJV/Hjh3D2rVrERQUBBcXF/Ts2RNBQUFwd3fH5s2b0bFjR+zevRtKpRLAncv0PD09kZubC09PT/Tt2xf9+/fHxx9/rKtz0aJFAO4scuHh4YGePXvCxcUFixYtwoYNGyAIAhQKRa37uvSp+80338SAAQNQXFyM5557Ds7OznjkkUcQHByMdu3awc/PD5GRkbXuB2vTpg1u3bpV6/3fvHkTbdu2nOTEytEeT37zNpy7+9RbTmGhQJ/V09D173WvCCk1OTk5mDt3ru6eQJIfubUB/86tsXzWw/jfuWt4b1vtFexSLt5C5MYEDAxxw+tjA0WIkJqLQqHAR4v66TWb9eJQb3y1eiAsLFrYTb96YB+gv5LbOCAXkkulp0+fDldXV8TGxiIrKwuJiYmwsrJCUFAQRowYgdmzZ6N169a68jY2Nti/fz8WLlyIEydO4OTJkxAEAa+88oquzMyZM6FSqRAVFYWsrCwolUqEhYVh/vz5umXifXx84OhYc8kmfeq2tbXF4cOHsXHjRkRHRyMlJQVJSUlo164dvLy8MH78eISFhaFz58416g4ICKjz3q6UlBQ8+uijxvpzmoRteyc8/f0KXP75FFI/P4Cr/635oOqgV55B178/CUdvN5EiNL2SkhIcP34c06ZNEzsUkxnYzgXqZ56vt0xD26VEbm0gNbMItj231Vtm9dZErN6aaKKIyJSUSgv8c2FfTHjGFx998xu+OZCJSvWfK5E992RnzHzeH4N6ukFhyOIcLQj7AP2V3MYBQB7nApJLvvr06VNjGXl9BAYGYu/evfWWmTZt2n0bv1DPwzf0qdvS0hKzZ8/G7NmzGwpVJywsDAsXLkROTg46duwI4M6S+BcvXkRUVJTe9ZgLC0sVvJ7pB69n+qHiZjH+/eCkOyshKoCe77ScGziJiKhxFAoFej/kgt4PueCjRf3g8/QO5N+ogFs7W+xY97jY4RERGYXkLjuUi+nTp8PNzQ0jRozADz/8gF27duGll15Cr169MGLECLHDaxKbtg66bzal+g0nERHdn72dJVTKO6coUrzEkIjki8lXC+Xo6IiYmBi4ubnhxRdfxNSpU9GvXz/88MMPsGhhz1ogIiIiIpIDyV12KCc+Pj744YcfxA6DmkH79u0RHh6O9u3bix0KiYRtgIhI3jgOSBOTLyIz5OzsjHHjxokdBomIbYCISN44DkgTr08jMkO3b9/GoUOHcPv2bbFDIZGwDRARyRvHAWli8kVkhnJzc7Fw4ULk5uaKHQqJhG2AiEjeOA5IE5MvIiIiIiIiE2DyRUREREREZAJMvoiIiIiIiEyAyReRGbK2tkbXrl1hbW0tdigkErYBIiJ54zggTVxqnsgMde7cGV988YXYYZCI2AaIiOSN44A0ceaLiIiIiIjIBJh8EZmhtLQ0hIaGIi0tTexQSCRsA0RE8sZxQJqYfBGZIUEQUFVVBUEQxA6FRMI2QEQkbxwHpIn3fBFR01hbQ7Vjm9hR6I83LhuVna0KJScmiB2GQexsOfSRcbW0fsA+QEbHcwG9sfcRUZMoFArAxkbsMEgkCoUC9naWYodBJCr2A5I7ngvoj5cdEhERERERmQBnvojMkJeXF6Kjo+Hh4SF2KCQStgEiInnjOCBNTL6IzJCNjQ18fHzEDoNExDZARCRvHAekiZcdEpmhvLw8vPvuu8jLyxM7FBIJ2wARkbxxHJAmJl9EZqioqAj79u1DUVGR2KGQSNgGiIjkjeOANDH5IiIiIiIiMgEmX0RERERERCbA5IuIiIiIiMgEmHwRmSELCwsEBwfDwoJdVK7YBoiI5I3jgDTxaBKZIa1WizNnzkCr1YodComEbYCISN44DkgTky8iIiIiIiITYPJFRERERERkAky+iIiIiIiITIDJF5EZcnBwwNChQ+Hg4CB2KCQStgEiInnjOCBNKrEDIKLaPDw8sGzZMrHDIBGxDRARyRvHAWnizBeRGaqsrER2djYqKyvFDoVEwjZARCRvHAekickXkRnKzMzE6NGjkZmZKXYoJBK2ASIieeM4IE287JCImkQQBKAlfStnbQ2FQiF2FJIhCALKyqvFDsMgdrYqtgEyqpbWD9gHyNh4LqA/Jl9E1DSVlah+fqLYUehNtWMbYGMjdhiSUVZejVZ9tosdhkFKTkyAvZ2l2GGQhLS0fsA+QEbHcwG98bJDIiIiIiIiE2DyRUREREREZAK87JDIDPn7++PUqVNih0EiYhsgIpI3jgPSxJkvIiIiIiIiE2DyRWSGLl26hMmTJ+PSpUtih0IiYRsgIpI3jgPSxOSLyAyVl5fj/PnzKC8vFzsUEgnbABGRvHEckCYmX0RERERERCbA5IuIiIiIiMgEmHwRERERERGZAJMvIjPk5uaGpUuXws3NTexQSCRsA0RE8sZxQJr4nC8iM9S6dWsMGzZM7DBIRGwDRETyxnFAmjjzdY9169YhMjISRUVFLapukp7CwkLs3LkThYWFYodCImEbICKSN44D0sTk6w9FRUWIiIjA2rVr0apVqxZTN0lTfn4+oqKikJ+fL3YoJBK2ASIieeM4IE1Mvv6QkJAAQRDQrVs3KJXKFlO3FGmrNRAEAQB0/yUiIvkQBAFaLccBIpIeJl9/SEhIAAAEBwe3qLqlQhAE5P2ShCNT1+ELr5eAu2OtACT+Yw/Kr/NyTak7dv0arL7fgfcvpt63jNX3OzDy5HETRkWmtGL2IxASp2DSSL86tx/Z+hQq4l9GkG8bE0dGpnLhUhHejDqBtv2/RN71Ow+WzbtejpnLf0VS+k2Ro2t+7AMkd3I4F5B08lVZWYn169ejf//+aNOmDaysrODq6opevXohIiICubm52L17NxQKBebNmwcA2LRpExQKhe5n5cqVuvqysrIQFRWFoUOHwsfHB/b29rCzs0P37t2xevVqqNXqGq9vSN0AoFarsWXLFgwePBjOzs6wtraGr68v3nrrLZSUlDTzX0s86tulOPjichx4biku/XgCgkZbY3vCqq+xK+QVZO77r0gREpEpRH50BkkXbuL9eb3h0cGuxrY544MwqKcb3vkoAckZvP9BagRBQORHCejyzC588EUybhWr79kGfLwzFQ+N+RavrfwvNH8ZI6SEfYBI+iS72mFBQQEGDx6MxMREqFQqdO7cGb6+vrhy5Qri4+MRFxeHadOmobS0FKGhoTh9+jQqKioQHBwMO7s/P/D69eun+//ly5fj008/hb29Pdzd3REUFIRr164hMTERiYmJiIuLw+7du3XlDak7IyMDI0eORHJyMlQqFby9veHi4oKMjAysWbMGBw8exPHjx2vsLwXV5ZU4OG4FCuLT6y2nqazCsRnvAwA6D+9Xb1kpsLOzQ+/evSV3vEl/cmwDVdVaTFwci5NfDsfWyAEYOvMAAKCLV2useD0EJxKvIerzJJGjpOaw8B/xWL01scFy//r3byiv1OCTyP5QKBQmiMy02AfoXnIcB+RAsjNfc+fORWJiIsaOHYucnBykp6cjLi4Oubm5yM7Oxpo1a+Dr64sJEyYgNjYWFhZ3/hQxMTH45ZdfdD+DBg3S1fnkk0/i9OnTKC4uRnp6Ok6dOoWsrCzExcWhQ4cO2LNnD5KS/vxQ1LfumzdvYtiwYUhOTsbUqVNx5coVpKWl4bfffkNKSgoCAgKQkJCADz74wGR/P1M598GuBhOve/0yewMqZHAJoqenJzZs2ABPT0+xQyGRyLUNnPntBlZtPYe/hXbEtNFdYWGhwPYVj0KhACYujtXdB0TScSw+T6/E665Pv03HroNZzReQyNgH6C65jgNSJ9nka8+ePVAqldi8eTM6dOhQY5uHhwciIiJ035qlp6ejrKwMnTp1gpOT033rfOGFF/Dwww/X+rYtJCQEQ4YMAQAkJyfX2KZP3eHh4cjIyEB4eDi2bNkCFxcX3TY/Pz9s2LABALB371593nqLUV2hRvpXhw3aR1NZhQv/jmmmiMyHRqNBSUkJNBqN2KGYXJlGg+uVlXX+yImc28DyzWdwNvUG1s3thQ0L+qJ3Nxcs2nAa6VnS/+JFjv71798M3uejbwzfpyVhHyBA3uOAlM8FJHnZYXV1NdRqNTQaDY4ePYqnn3663vJnz54FAPTo0aPecpWVldi3bx+OHTuGzMxMFBcXQ6u9c+353aTLysrKoLqTk5Px9ddfw9XVFatWraqzzN19r1y5ovtdTk4OVq9ejVOnTuHcuXNQq9UtbkWoy/tPofLmbYP3S9t+EN1eG9UMEZmPCxcuYMKECdi+fTv8/f3FDseklqUlY1lacsMFJU7ObaC6WsDExbGIix6OV18IwPGEq/jwy/Nih0XNIP9GOb49nGXwfkfj8pCaeQv+nZ2MHpM5YB8gQN7jgJTPBSSZfKlUKowcORI7d+5EWFgYHn/8cYwZMwbDhw+Hh4dHrfL6JF+HDx/GpEmTkJ2dXe9re3t7G1R3dHQ0tFotNBqNbvbsr+4u5GFvb6/7XUZGBnbv3o2ePXvCysoKv/76a71x6SMkJARXr15tcj36GqztiEHoaPB+JdnX4OXxAKoVLSvZHDNmjN5lr127BgDYv38/Tp8+rfd+o0aZPim1tbBASo++Rqtvqqc3Rrs/UOe2YSeONbn+Ll26oFxr+hv2DTn+QOPagBjHXwtLoO1io9dbVKJGpVoDK0slfjqeDWN+t+TXpQssUGW8CqnRKlWeqHac0qh9Qx8fA9uq+6+IZkrN0Q/YB6SJ5wL6MfdzAVdXV8THxzdqX0kmXwCwfft2BAYG4pNPPkFMTAxiYmIwa9YsDBo0CCtXrkSfPn10ZRtKkE6ePImnnnoKVVVVmDJlCsaNG4cHH3wQbdq0gUqlQmpqKgICAqBSqRAQEFBj34bqjom5cwldQUEBCgoK6n1PXl5euv9/9NFHkZeXBwCIjIw0SvJ19erVGrNrza3coS1g33C5ulzLy0e50LIGjtLSUr3LlpeX6/5ryH6mPH532SmVQA/j1efbqhWeaN+h4YKNlJubizIRLuEw5DgCjWsDYhx/KKyAtsav9rNlA2BlqUTKxUIsnt4DOw5k4vecYqPUnZebCwjqhgtS87N3ABwbt+vNwmKgSIQ2X5dm6AfsA9LEcwH9SPVcAJBw8mVjY4PIyEi88847OHv2LL777jts2rQJR44cwZAhQ5CamqqbBWsoQVqwYAHUajVWr16N+fPn19r+448/AgACAwNhbW1dY1tDdefk5AC4s4x9p06d9H5/dxfxMCZXV1ej11kfC63Nn8/zMkA1tHB2c4HQwha6unfmsiF3P2RtbW0N2q+umd3mZtsMbbE5ubu7izLzZchxBBrXBsQ4/lpYIs/Idb4+NhCP9XLHwn/E47sjl5DwzUh8umwABk3+ySj1u7m781t/M1GltMM14M568gauXujsZA2bVqZv83Uxdj9gH5AunguYh6aeCzTlnFmyydddCoUCwcHBCA4Oxpw5c/DQQw8hOzsbp06dwqhRo5Cfn4/8/Hw4OTnVmFm6S6PRIDY2FgAwadKkWtu1Wi2++uorALUfotxQ3cCfHauioqIJ79I4Gjt92li3f8/DntDXDd7Pd0R/ZH+8pxkial5xcXF6l01NTUV0dDSGDRtm0HXeH374YSMiaxqhogLVz080+es2Vnp6OhQ2NiZ/XUOOP9C4NiDG8S8tq0KrPtuNVp+vpyNWhYfgVFIB1nyaCK1WQOTGBKwK74nXxwZiw9cpTX6NC+npsLezNEK01FRarQD/Ebtw4ZJh9/+2b2OD7PgjsLZSNlNkhjFmP2AfkDaeC5gHsc4FAAmvdlgXJycn3YIYd1cUTEtLA3Dn2s+6lJaW6laZqStBeu+993DmzBkAtZOvhuoGoFs+9NChQ3q/D6lw9HaD+6AeBu/nP3Go8YMxM76+vjhw4AB8fX3FDoVEIsc2oFAAny9/FEoLBSYuPqZbUnvtZ0mIO1+AVeEh8O7oIHKUZEwWFgq8+nxAwwX/YuqzXc0m8TIm9gG6lxzHATmQXPK1bds2zJ8/HykpNb8ZKiwsxIwZM3Dx4kX4+/vr7vm6O42bk5NT5/W0jo6OuunbFStW6BIxtVqNqKgoLFmyBCrVnQnEvyZfDdUN/Hnj5YIFC7Br165a2zMyMrB06VIcPXpUr/ff0vSY9zwsrPSfgPV4PBgd+hg+ULc0KpVKd08hyZMc28Dcid0QGtwBb3+UgNTMP5fU1moFvLwkFiqlBT5dNkDECKk5TBrZBX6d9L/xy629HV4fG9iMEYmHfYDuJcdxQA4kl3wdO3YMa9euRVBQEFxcXNCzZ08EBQXB3d0dmzdvRseOHbF7924olXe+MQsKCoKnpydyc3Ph6emJvn37on///vj44491dS5atAgAsHnzZnh4eKBnz55wcXHBokWLsGHDBgiCAIVCUeu+Ln3qfvPNNzFgwAAUFxfjueeeg7OzMx555BEEBwejXbt28PPzQ2RkpEH3g7UkLo90wcCNb+iVgLn08segTW/Wes6aFOXk5GDu3Lm6ewJJfuTWBvw7t8byWQ/jf+eu4b1ttZfUTrl4C5EbEzAwxE2yJ95y1drBCvs/+hu83Fs1WNalrQ32f/Qk3NrbmSAy02IfoL+S2zggF5JLvqZPn44FCxYgNDQUVlZWSExMxOXLlxEUFIRly5bh/PnzCAz880PLxsYG+/fvx4gRI2BpaYmTJ0/i119/RatWfw4CM2fOxObNm+Hn54ebN28iNzcXYWFhOH36NHr16gWNRgNvb284Otb85k6fum1tbXH48GGsX78effr0QXV1NZKSkpCfnw8vLy+Eh4fj4MGD6Ny5c/P/8UTS6aneGLpnGTwe61Hndpt2rdH9jTF48pu3YdnK1rTBiaSkpATHjx9HSUmJ2KGYzMB2LlA/8zze9Ln/de3qZ57H3t7y+NZXbm0gNbMItj23od/fv9ddavVXq7cmQvHQVqPc80LmxecBR/zvy2cwfUxX2NnU/jLOytICfw/zxYkvh6N7V2cRImx+7AP0V3IbBwB5nAtIbh6zT58+NZaR10dgYCD27t1bb5lp06Zh2rRpdW6r7+HG+tRtaWmJ2bNnY/bs2Q2FKlkuj3TBkK8X43bWVVz+6STil39xZ4MCeO70x1Ba8cZgIiIpc21nh01v98faN3phx4HfMXfdSRSXVaN1K0tk/Pg82rUR5+Z4IiJjktzMl5zs2rULu3bt0t3fdvffpl610JgcvVzx4KsjoLC4c2mhQqFg4kVEJCOtHawwbYw/HFvdWSCrlZ0lEy8ikgzJzXzJyXPPPVfnvydOnIjPP/9chIiIiIiIiOh+mHy1YPVd7kgtW/v27REeHo727duLHQqJhG2AiEjeOA5IE5MvIjPk7OyMcePGiR0GiYhtgIhI3jgOSBPv+SIyQ7dv38ahQ4dw+/ZtsUMhkbANEBHJG8cBaWLyRWSGcnNzsXDhQuTm5oodComEbYCISN44DkgTky8iIiIiIiITYPJFRERERERkAky+iIiIiIiITIDJF5EZsra2RteuXWFtbS12KCQStgEiInnjOCBNXGqeyAx17twZX3zxhdhhkIjYBoiI5I3jgDRx5ouIiIiIiMgEmHwRmaG0tDSEhoYiLS1N7FBIJGwDRETyxnFAmph8EZkhQRBQVVUFQRDEDoVEwjZARCRvHAekifd8EVHTWFtDtWOb2FHojzcuG5WdrQolJyaIHYZB7Gw59JFxtbR+wD5ARsdzAb2x9xFRkygUCsDGRuwwSCQKhQL2dpZih0EkKvYDkjueC+iPlx0SERERERGZAGe+iMyQl5cXoqOj4eHhIXYoJBK2ASIieeM4IE1MvojMkI2NDXx8fMQOg0TENkBEJG8cB6SJlx0SmaG8vDy8++67yMvLEzsUEgnbABGRvHEckCYmX0RmqKioCPv27UNRUZHYoZBI2AaIiOSN44A0MfkiIiIiIiIyASZfREREREREJsDki4iIiIiIyASYfBGZIQsLCwQHB8PCgl1UrtgGiIjkjeOANPFoEpkhrVaLM2fOQKvVih0KiYRtgIhI3jgOSBOTLyIiIiIiIhNg8kVERERERGQCTL6IiIiIiIhMgMkXkRlycHDA0KFD4eDgIHYoJBK2ASIieeM4IE0qsQMgoto8PDywbNkyscMgEbENEBHJG8cBaeLMF5EZqqysRHZ2NiorK8UOhUTCNkBEJG8cB6SJyReRGcrMzMTo0aORmZkpdigkErYBIiJ54zggTUy+iIiIiIiITID3fBFRkwiCALSkSyKsraFQKMSOQjIEQUBZebXYYRjEzlbFNkBG1dL6AfsAGRvPBfTH5IuImqayEtXPTxQ7Cr2pdmwDbGzEDkMyysqr0arPdrHDMEjJiQmwt7MUOwySkJbWD9gHyOh4LqA3XnZIRERERERkApz5IjJD/v7+OHXqlNhhkIjYBoiI5I3jgDRx5ouIiIiIiMgEmHwRmaFLly5h8uTJuHTpktihkEjYBoiI5I3jgDQx+SIyQ+Xl5Th//jzKy8vFDoVEwjZARCRvHAekickXERERERGRCTD5IiIiIiIiMgEmX0RERERERCbA5IvIDLm5uWHp0qVwc3MTOxQSCdsAEZG8cRyQJj7ni8gMtW7dGsOGDRM7DBIR2wARkbxxHJAmznwRmaHCwkLs3LkThYWFYodCImEbICKSN44D0sTk6x7r1q1DZGQkioqKWlTdJD35+fmIiopCfn6+2KGQSNgGiIjkjeOANDH5+kNRUREiIiKwdu1atGrVqsXUTURERERELQOTrz8kJCRAEAR069YNSqWyxdQtJVqNBtmHTuOXN/4FQSsAAAStgNLcGyJHRqZw7Po1WH2/A+9fTL1vGavvd2DkyeMmjIpMacXsRyAkTsGkkX51bj+y9SlUxL+MIN82Jo6MTCkp/SbeWHsCN4oqAQC3itU4lVQgclSmwT5AcieHcwEmX39ISEgAAAQHB7eouqXi9z3Hsaff6zj891XI+PeRGtt29ZqJI1PXofw6L9kkkrLIj84g6cJNvD+vNzw62NXYNmd8EAb1dMM7HyUgOYP3P0hRYvpNDJj4Ax4a8y0+/DIZFZUaAEBpeTV6j9uHkBe/w69npH35FfsAkfRJOvmqrKzE+vXr0b9/f7Rp0wZWVlZwdXVFr169EBERgdzcXOzevRsKhQLz5s0DAGzatAkKhUL3s3LlSl19WVlZiIqKwtChQ+Hj4wN7e3vY2dmhe/fuWL16NdRqdY3XN6RuAFCr1diyZQsGDx4MZ2dnWFtbw9fXF2+99RZKSkqa+a8lnvMbv0PsrPUouXytzu2CRotLP57AT2ELZTMLZmdnh969e8POzq7hwiRJcmwDVdVaTFwcC3tbS2yNHKD7fRev1ljxeghOJF5D1OdJIkZIzeW/Z/PRf+IP+KWe5Op0ynU8Me0n/HQ824SRmRb7AN1LjuOAHEh2qfmCggIMHjwYiYmJUKlU6Ny5M3x9fXHlyhXEx8cjLi4O06ZNQ2lpKUJDQ3H69GlUVFQgODi4RiPv16+f7v+XL1+OTz/9FPb29nB3d0dQUBCuXbuGxMREJCYmIi4uDrt379aVN6TujIwMjBw5EsnJyVCpVPD29oaLiwsyMjKwZs0aHDx4EMePH5dcB7z88ynEL/tCr7LFl/JxaMIqPPPzGliopH35pqenJzZs2CB2GCQiubaBM7/dwKqt5/D2K8GYNrortn6bju0rHoVCAUxcHAvtH5ckk3TkXivF8NkHUVxa1WDZSrUWz82NwelvRsC/s1PzBycC9gG6S67jgNRJNvmaO3cuEhMTMXbsWLz//vvo0KGDbtuVK1fw1VdfwdfXF35+fhg/fjwcHBwAADExMXBycqqzzieffBKzZs1CcHAwFAqF7vfx8fEICwvDnj17kJSUhG7dugEAJkyYoFfdN2/exLBhw5CRkYGpU6dixYoVcHFxAQBcuHABI0aMQEJCAj744AMsWrTIGH8es5G4fo9B5QuTs5Bz6DQ8h/ZqpojMg0ajQXl5OWxtbWV3n2CZRoPrlZVihyE6ObeB5ZvPYPggT6yb2ws9/J3Ru5sL3ow6ifQsXnosRR/vTMWNW/r3+bKKaqz/Mhkbl4Q2Y1TiYh8gQN7jgJTPBSR72eGePXugVCqxefPmGokXAHh4eCAiIkKXQKWnp6OsrAydOnW6b+IFAC+88AIefvjhGokXAISEhGDIkCEAgOTk5Brb9Kk7PDwcGRkZCA8Px5YtW3SJFwD4+fnpvvXYu3evPm+9xbh+NgPXz2YYvF/q5weaIRrzcuHCBTz++OO4cOGC2KGY3LK0ZLj/57s6f+REzm2gulrAxMWxsLFW4tUXAnA84So+/PK82GFRM1BXabBld5rB+33xQwZul6gbLthCsQ8QIO9xQMrnApKc+aquroZarYZGo8HRo0fx9NNP11v+7NmzAIAePXrUW66yshL79u3DsWPHkJmZieLiYmi1WgB/Jl1WVlYG1Z2cnIyvv/4arq6uWLVqVZ1l7u575coV3e927dqF6OhoxMfHo6CgAJ6enhg9ejQWLFjQYpazv3LkbKP2yz12DtpqjeQvPZSrqZ7eGO3+QJ3bhp04ZuJoSCxFJWpUqjWwslTip+PZEHillSQlpt/E1evlBu9XWl6NX87k46kBdX9WSAH7AMmZlM8FJJl8qVQqjBw5Ejt37kRYWBgef/xxjBkzBsOHD4eHh0et8vokX4cPH8akSZOQnV3/jb7e3t4G1R0dHQ2tVguNRqObPfuruwt52Nvb6363bt06eHp6YuXKlejYsSPOnj2LpUuX4tixY4iNjYWFheGTmiEhIbh69arB+zXW37SeGAD3Ru3bpZM3KhQaI0fUvMaMGaN32WvX7iw+sn//fpw+fVrv/UaNGmVwXE1la2GBlB59jVafb6tWeKJ9h4YLNlKXLl1Q/seXJqZkyPEHGtcGxDj+WlgCbRcbvd7Plg2AlaUSKRcLsXh6D+w4kInfc4qNUrdfly6wQMP3F1Hzq1B1BhxfbtS+4ydOh53aPBafaI5+wD4gTTwX0I+5nwu4uroiPj6+UftKMvkCgO3btyMwMBCffPIJYmJiEBMTg1mzZmHQoEFYuXIl+vTpoyvbUIJ08uRJPPXUU6iqqsKUKVMwbtw4PPjgg2jTpg1UKhVSU1MREBAAlUqFgICAGvs2VHdMTAyAOwuEFBTU/xwTLy8v3f9///33aN++ve7fAwcORPv27TFu3Dj88ssvePTRR+utqy5Xr16tMbvW3G62cgRaNS75ysrNhgYt62vA0tJSvcuWl5fr/mvIfqY8fnfZKZVAD5O/bKPl5uaiTGP6xN2Q4wg0rg2IcfyhsALaGrfK18cG4rFe7lj4j3h8d+QSEr4ZiU+XDcCgyT8Zpf683FxAkO4lay2KnTXg2LhdC6/nobBYhDZfFyP3A/YB6eK5gHkQ61wAkHDyZWNjg8jISLzzzjs4e/YsvvvuO2zatAlHjhzBkCFDkJqaqpsFayhBWrBgAdRqNVavXo358+fX2v7jjz8CAAIDA2FtbV1jW0N15+TkALizjH2nTp30fn/3Jl53hYSEAGh8p3N1dW3Ufo1VJFgAjfjSIQ+lcPVoXNImpntnLhty90PW1tbWoP3qmtltbraNmGUVk7u7uygzX4YcR6BxbUCM46+FJfKMWJ+vpyNWhYfgVFIB1nyaCK1WQOTGBKwK74nXxwZiw9cpTX4NN3d3futvJrQK4KqghqCwarjwvQQtXFuroXQ0fZuvizH7AfuAtPFcwDw09VygKefMkk2+7lIoFAgODkZwcDDmzJmDhx56CNnZ2Th16hRGjRqF/Px85Ofnw8nJqcbM0l0ajQaxsbEAgEmTJtXartVq8dVXXwGo/RDlhuoG/uxYFRUVTXiXdxw5cufhxH+dfdNXY6dPG0ur0WBP39dRkl33873u59m1b2LB3+u+RNOcxcXF6V22uroaL7/8MhwcHKBS6d9NP/zww0ZE1jRCRQWqn59o8tdtrPT0dChsbEz+uoYcf6BxbUCM419aVoVWfbYbpS6FAvh8+aNQWigwcfEx3ZLaaz9LwrNPeGFVeAh+jM1u8qVXF9LTYW9naYyQyQhmLPsFm3cZtujGyCc649sPm56EGIux+gH7gPTxXMA8iHUuAEh4tcO6ODk56RbEuLuiYFranQ/8Ll261LlPaWkpNH9MS9aVIL333ns4c+YMgNrJV0N1A3ee4QAAhw4d0vt91OXKlStYsmQJhg4d2uDCIebCQqlEwJRhBu1j3dYB3s/2b6aIzIdKpdJd1kryJMc2MHdiN4QGd8DbHyUgNfPPJbW1WgEvL4mFSmmBT5cNqKcGaoleezEQSqWi4YL3CB8X1EzRiIt9gO4lx3FADiSXfG3btg3z589HSkrNb8QKCwsxY8YMXLx4Ef7+/rp7vu5O4+bk5NR5Pa2jo6Nu+nbFihW6REytViMqKgpLlizRdYq/Jl8N1Q38eePlggULsGvXrlrbMzIysHTpUhw9evS+77mkpAQjRoyAlZUVPv300/uWM0eB056G14h+DRcEoLSxwhOfz4elvW0zRyW+nJwczJ07V3dZKsmP3NqAf+fWWD7rYfzv3DW8t632ktopF28hcmMCBoa44fWxgSJESM2lW5e22GTAM7tWhYdgUE+3ZoxIHOwD9FdyGwfkQiEI0lq8dPLkyfjss88A3LkvqlOnTigrK8Pvv/+OiooKdOzYEQcOHEBg4J0ProqKCnTt2hWXL19G27Zt0aVLFyiVSowfPx6vvPIKAGDjxo149dVXAQAdOnTAAw88gAsXLqCsrAz/+te/MHPmTGi1Wty6dQuOjn/eOaxP3eXl5fjb3/6G48ePAwDatm0LLy8vaLVaZGdn48aNGwCA33//HZ07d671fsvLy/HUU08hMTERx48f172vlkRbrUFc5OdI/exnCNq6m6O9RzsM2jIX7YP9TByd8RhyqUFqaiomTJiA7du3w9/fX+/9evbs2ZjQmqSlXWqg2rGtRVx22Jg2IMbxN+Zlh6ZScmICL7kyQ1//eBGvrvwviorrXgjCzkaFNW/0xGsvmd8419L6AfuAeHguYB7EOhcAJDjzNX36dCxYsAChoaGwsrJCYmIiLl++jKCgICxbtgznz5+vkaDY2Nhg//79GDFiBCwtLXHy5En8+uuvNZ6VNXPmTGzevBl+fn64efMmcnNzERYWhtOnT6NXr17QaDTw9vaukXjpW7etrS0OHz6M9evXo0+fPqiurkZSUhLy8/Ph5eWF8PBwHDx4sM7Eq6qqCmPGjEF8fDz279/fIhMvALBQKdH73SkYc2ojur8xBq39OsLG2RH27u3g8VgPPP5ZBEaf+FeLTryIiKh+Y5/2wZWDL2LLO/3Rt7sLLCzuXIpoqbLAP97qg9zDL5ll4kVEZAjJXUTap0+fGsvI6yMwMBB79+6tt8y0adMwbdq0OrfVN3moT92WlpaYPXs2Zs+e3VCoOlqtFuPGjcPhw4fx008/oVevXnrva67sPdohOOJFBEe8KHYoREQkAns7S0wd3RVTR3dFx8HRuHKtDC5tbfD6WGne40VE8iO55EsuZs2ahZ07d+Ktt96CnZ0dTpw4odvm4+NT51L0REREREQkHslddigX+/fvBwCsXr0affv2rfFz97lj1HK1b98e4eHhTKJljG2AiEjeOA5IE2e+WqisrCyxQ6Bm5OzsjHHjxokdBomIbYCISN44DkgTZ76IzNDt27dx6NAh3L59W+xQSCRsA0RE8sZxQJqYfBGZodzcXCxcuBC5ublih0IiYRsgIpI3jgPSxOSLiIiIiIjIBJh8ERERERERmQCTLyIiIiIiIhNg8kVkhqytrdG1a1dYW1uLHQqJhG2AiEjeOA5IE5eaJzJDnTt3xhdffCF2GCQitgEiInnjOCBNnPkiIiIiIiIyASZfRGYoLS0NoaGhSEtLEzsUEgnbABGRvHEckCYmX0RmSBAEVFVVQRAEsUMhkbANEBHJG8cBaWLyRUREREREZAJccIOImsbaGqod28SOQn9cNcqo7GxVKDkxQewwDGJny6GPjKul9QP2ATI6ngvojb2PiJpEoVAANjZih0EiUSgUsLezFDsMIlGxH5Dc8VxAf0y+iMyQl5cXoqOj4eHhIXYoJBK2ASIieeM4IE1MvojMkI2NDXx8fMQOg0TENkBEJG8cB6SJC24QmaG8vDy8++67yMvLEzsUEgnbABGRvHEckCYmX0RmqKioCPv27UNRUZHYoZBI2AaIiOSN44A0MfkiIiIiIiIyASZfREREREREJsDki4iIiIiIyASYfBGZobZt22LixIlo27at2KGQSNgGiIjkjeOANDH5IjJDFhYWsLS0hIUFu6hcsQ0QEckbxwFp4tEkMkPXr1/HJ598guvXr4sdComEbYCISN44DkgTky8iIiIiIiITYPJFRERERERkAky+iIiIiIiITIDJF5EZcnBwwNChQ+Hg4CB2KCQStgEiInnjOCBNKrEDIKLaPDw8sGzZMrHDIBGxDRARyRvHAWnizBeRGaqsrER2djYqKyvFDoVEwjZARCRvHAekickXkRnKzMzE6NGjkZmZKXYoJBK2ASIieeM4IE287JCImkQQBKAlfStnbQ2FQiF2FJIhCALKyqvFDsMgdrYqtgEyqpbWD9gHyNh4LqA/Jl9E1DSVlah+fqLYUehNtWMbYGMjdhiSUVZejVZ9tosdhkFKTkyAvZ2l2GGQhLS0fsA+QEbHcwG98bJDIiIiIiIiE2DyRUREREREZAK87JDIDPn7++PUqVNih0EiYhsgIpI3jgPSxJkvIiIiIiIiE2DyRWSGLl26hMmTJ+PSpUtih0IiYRsgIpI3jgPSxOSLyAyVl5fj/PnzKC8vFzsUEgnbABGRvHEckCYmX0RERERERCbA5IuIiIiIiMgEmHwRERERERGZAJMvIjPk5uaGpUuXws3NTexQSCRsA0RE8sZxQJr4nC8iM9S6dWsMGzZM7DBIRGwDRETyxnFAmjjzdY9169YhMjISRUVFLapukp7CwkLs3LkThYWFYodCImEbICKSN44D0sTk6w9FRUWIiIjA2rVr0apVqxZTN0lTfn4+oqKikJ+fL3YoJBK2ASIieeM4IE1Mvv6QkJAAQRDQrVs3KJXKFlM3ERGR1CSl30R5pQYAoK7SQhAEkSMiIjIOJl9/SEhIAAAEBwe3qLqJpOLY9Wuw+n4H3r+Yet8yVt/vwMiTx00YFZnSitmPQEicgkkj/ercfmTrU6iIfxlBvm1MHBmZQlWVFp/tTUfPl77DQ2O+xc2iSgBAQWEFuj27B//6dwrKK6pFjrJ5sQ+Q3MnhXEDSyVdlZSXWr1+P/v37o02bNrCysoKrqyt69eqFiIgI5ObmYvfu3VAoFJg3bx4AYNOmTVAoFLqflStX6urLyspCVFQUhg4dCh8fH9jb28POzg7du3fH6tWroVara7y+IXUDgFqtxpYtWzB48GA4OzvD2toavr6+eOutt1BSUtLMfy0iInFFfnQGSRdu4v15veHRwa7GtjnjgzCopxve+SgByRm8/0FqikvVePq1A5j89nHEJ1+vtT354i28tvJ/GDT5JxTcLBchQtNgHyCSPskmXwUFBejVqxfmzJmDkydPon379ujevTssLCwQHx+PqKgolJaWorS0FKGhobCxsQFwZ3YqNDRU99OvXz9dncuXL0dERAR++eUXKJVKBAUFwcXFBYmJiViwYAFeeumlGjEYUndGRgYefvhhTJ8+HceOHUO7du3g7e2NS5cuYc2aNRg4cCDKyspM8Jcjc2BnZ4fevXvDzs6u4cIkSXJsA1XVWkxcHAt7W0tsjRyg+30Xr9ZY8XoITiReQ9TnSSJGSM2hqkqL5+bG4OD/chsse+p8AcJeO4iycmnOgLEP0L3kOA7IgWSXmp87dy4SExMxduxYvP/+++jQoYNu25UrV/DVV1/B19cXfn5+GD9+PBwcHAAAMTExcHJyqrPOJ598ErNmzUJwcDAUCoXu9/Hx8QgLC8OePXuQlJSEbt26AQAmTJigV903b97EsGHDkJGRgalTp2LFihVwcXEBAFy4cAEjRoxAQkICPvjgAyxatMgYfx4yc56entiwYYPYYZCI5NoGzvx2A6u2nsPbrwRj2uiu2PptOraveBQKBTBxcSy0Wt77IzW7DmbiwH+v6F3+1PkCbNmdivDxDzZjVOJhH6C75DoOSJ1kZ7727NkDpVKJzZs310i8AMDDwwMRERG6BCo9PR1lZWXo1KnTfRMvAHjhhRfw8MMP10i8ACAkJARDhgwBACQnJ9fYpk/d4eHhyMjIQHh4OLZs2aJLvADAz89P1/H27t2rz1snCdBoNCgpKYFGoxE7FJMr02hwvbKyzh85kXMbWL75DM6m3sC6ub2wYUFf9O7mgkUbTiM9i4/qkKKPvvnN4H027kiV9CIc7AMEyHsckPK5gCRnvqqrq6FWq6HRaHD06FE8/fTT9ZY/e/YsAKBHjx71lqusrMS+fftw7NgxZGZmori4GFqtFsCfSZeVlZVBdScnJ+Prr7+Gq6srVq1aVWeZu/teufLnN4PHjx/H0qVLkZycjJs3b6J9+/YIDQ1FZGQkAgIC6n0fZP4uXLiACRMmYPv27fD39xc7HJNalpaMZWnJDReUODm3gepqARMXxyIuejhefSEAxxOu4sMvz4sdFjWDi9m38csZw5fRTssqwsnEAvTp7tJw4RaIfYAAeY8DUj4XkGTypVKpMHLkSOzcuRNhYWF4/PHHMWbMGAwfPhweHh61yuuTfB0+fBiTJk1CdnZ2va/t7e1tUN3R0dHQarXQaDS62bO/uruQh729ve53hYWF6NatG2bMmAEXFxfk5ORg1apV6Nu3L86fP4+OHTvWG2ddQkJCcPXqVYP3I/2MGTNG77LXrl0DAOzfvx+nT5/We79Ro0YZHFdT2VpYIKVHX6PVN9XTG6PdH6hz27ATx5pcf5cuXVD+x5cmpmTI8Qca1wbEOP5aWAJtFxu93qISNSrVGlhZKvHT8WwYc5LDr0sXWKDKeBVSo1WqvADHSY3a96lRf4ed2jxOzpqjH7APSBPPBfRj7ucCrq6uiI+Pb9S+kky+AGD79u0IDAzEJ598gpiYGMTExGDWrFkYNGgQVq5ciT59+ujKNpQgnTx5Ek899RSqqqowZcoUjBs3Dg8++CDatGkDlUqF1NRUBAQEQKVS1Zp1aqjumJgYAHcWCCkoKKj3PXl5een+f/jw4Rg+fHiN7T179kTXrl2xe/duhIeH11tXXa5evVpjdo2Mq7S0VO+y5eXluv8asp8Yx89OqQR6GK8+31at8ET7Dg0XbKTc3FyUiXAJhyHHEWhcGxCl/yqsgLbGr/azZQNgZalEysVCLJ7eAzsOZOL3nGKj1J2XmwsI6oYLUvOztwccG7dr4Y0bKLxtJmNWM/QD9gFp4rmAfqR6LgBIOPmysbFBZGQk3nnnHZw9exbfffcdNm3ahCNHjmDIkCFITU3VzYI1lCAtWLAAarUaq1evxvz582tt//HHHwEAgYGBsLa2rrGtobpzcnIA3FnGvlOnTga+y5qcnZ0B3Jn5awxXV9cmvT7V796Zy4bc/ZC1tbU1aL+6Znabm61Fy7p11N3dXZSZL0OOI9C4NiDG8dfCEnlGrvP1sYF4rJc7Fv4jHt8duYSEb0bi02UDMGjyT0ap383dnd/6m4lqCyvkA4AgAH+5n/q+/ijb3kkFKwfTt/m6GLsfsA9IF88FzENTzwWacs4s2eTrLoVCgeDgYAQHB2POnDl46KGHkJ2djVOnTmHUqFHIz89Hfn4+nJycasws3aXRaBAbGwsAmDSp9qURWq0WX331FYDaD1FuqG7gz45VUVHRqPen0Wig1Wpx6dIlLFiwAK6urnj++ecbVVdjp09JP3FxcXqXTU1NRXR0NIYNG2bQdd4ffvhhIyJrGqGiAtXPTzT56zZWeno6FH88/sGUDDn+QOPagBjHv7SsCq36bDdafb6ejlgVHoJTSQVY82kitFoBkRsTsCq8J14fG4gNX6c0+TUupKfD3s7SCNGSMQyc9CNiTxtwybtCAf/OrZGy93+1FsASizH7AfuAtPFcwDyIdS4ASHi1w7o4OTnpFsS4u6JgWloagDvXftaltLRUt8pMXQnSe++9hzNnzgConXw1VDdwZxlRADh06JDe7+NeAwcOhJWVFfz8/HD27FnExMSgffv2jaqLzIevry8OHDgAX19fsUMhkcixDSgUwOfLH4XSQoGJi4/pltRe+1kS4s4XYFV4CLw7OogcJRnbqy8YvkjUqy8EmE3iZUzsA3QvOY4DciC55Gvbtm2YP38+UlJqfjNUWFiIGTNm4OLFi/D399fd83V3GjcnJ6fO62kdHR1107crVqzQJWJqtRpRUVFYsmSJ7jK/vyZfDdUN/Hnj5YIFC7Br165a2zMyMrB06VIcPXq0zv23bt2KEydOIDo6Go6OjnjyySdx+fLlOstSy6FSqXT3FJI8ybENzJ3YDaHBHfD2RwlIzfxzSW2tVsDLS2KhUlrg02UD6qmBWqLRg73w1AD9F4nq290FU5/t2owRiYd9gO4lx3FADiSXfB07dgxr165FUFAQXFxc0LNnTwQFBcHd3R2bN29Gx44dsXv3biiVSgBAUFAQPD09kZubC09PT/Tt2xf9+/fHxx9/rKvz7oONN2/eDA8PD/Ts2RMuLi5YtGgRNmzYAEEQoFAoat3XpU/db775JgYMGIDi4mI899xzcHZ2xiOPPILg4GC0a9cOfn5+iIyMvO/9YF27dkXv3r3x4osv4vDhwyguLsbatWuN/FclU8vJycHcuXN19wSS/MitDfh3bo3lsx7G/85dw3vbai+pnXLxFiI3JmBgiBteHxsoQoTUXFQqC+yIelyvBKxfDxd8v2EIbG2kdzLKPkB/JbdxQC4k9+k1ffp0uLq6IjY2FllZWUhMTISVlRWCgoIwYsQIzJ49G61bt9aVt7Gxwf79+7Fw4UKcOHECJ0+ehCAIeOWVV3RlZs6cCZVKhaioKGRlZUGpVCIsLAzz58/XLRPv4+MDR8eaSzbpU7etrS0OHz6MjRs3Ijo6GikpKUhKSkK7du3g5eWF8ePHIywsDJ07d27wvTs5OcHX1xcZGRlG+EuSmEpKSnD8+HFMmzZN7FBMZmA7F6ifqf9+xYa2S4nc2kBqZhFse26rt8zqrYlYvTXRRBGRKdnbWeK79UPw759/x0ff/Ib/nbtWY3sP/7Z49fkA/P0ZX9hYS+7UBQD7ANUmt3EAkMe5gOQ+wfr06VNjGXl9BAYGYu/evfWWmTZt2n0bv1DPwzf0qdvS0hKzZ8/G7NmzGwq1XteuXUNaWhp69+7dpHqIiIhMTaWywPgwX4wP80Vq5i30n/ADbhRVwqWtDRK+GSnJe7yISH4kl3zJxfjx4+Hr64sePXrAyckJFy5cwAcffACVSoU33nhD7PCIiIgazb+zE2ys79weYKmyYOJFRJLB5KuF6tOnD7Zv347169ejoqICDzzwAB577DEsXLiwyc8LIyIiIiIi42Py1UK99tpreO2118QOg5pJ+/btER4ezscGyBjbABGRvHEckCYmX0RmyNnZGePGjRM7DBIR2wARkbxxHJAmyS01TyQFt2/fxqFDh3D79m2xQyGRsA0QEckbxwFpYvJFZIZyc3OxcOFC5Obmih0KiYRtgIhI3jgOSBOTLyIiIiIiIhNg8kVERERERGQCTL6IiIiIiIhMgMkXkRmytrZG165dYW1tLXYoJBK2ASIieeM4IE1cap7IDHXu3BlffPGF2GGQiNgGiIjkjeOANHHmi4iIiIiIyASYfBGZobS0NISGhiItLU3sUEgkbANERPLGcUCamHwRmSFBEFBVVQVBEMQOhUTCNkBEJG8cB6SJ93wRUdNYW0O1Y5vYUeiPNy4blZ2tCiUnJogdhkHsbDn0kXG1tH7APkBGx3MBvbH3EVGTKBQKwMZG7DBIJAqFAvZ2lmKHQSQq9gOSO54L6I+XHRIREREREZkAZ76IzJCXlxeio6Ph4eEhdigkErYBIiJ54zggTUy+iMyQjY0NfHx8xA6DRMQ2QEQkbxwHpImXHRKZoby8PLz77rvIy8sTOxQSCdsAEZG8cRyQJiZfRGaoqKgI+/btQ1FRkdihkEjYBoiI5I3jgDQx+SIiIiIiIjIBJl9EREREREQmwOSLiIiIiIjIBJh8EZkhCwsLBAcHw8KCXVSu2AaIiOSN44A08WgSmSGtVoszZ85Aq9WKHQqJhG2AiEjeOA5IE5MvIiIiIiIiE2DyRUREREREZAJMvoiIiIiIiEyAyReRGXJwcMDQoUPh4OAgdigkErYBIiJ54zggTSqxAyCi2jw8PLBs2TKxwyARsQ0QEckbxwFp4swXkRmqrKxEdnY2KisrxQ6FRMI2QEQkbxwHpInJF5EZyszMxOjRo5GZmSl2KCQStgEiInnjOCBNvOyQiJpEEASgJX0rZ20NhUIhdhSSIQgCysqrxQ7DIHa2KqO2gRbXBwD2AyNrcW3AyMefnwMk9z5gCCZfRNQ0lZWofn6i2FHoTbVjG2BjI3YYklFWXo1WfbaLHYZBSk5MgL2dpfEqbGF9AGA/MLoW1gaMffz5OUBy7wOG4GWHREREREREJsDki4iIiIiIyAR42SGRGfL398epU6fEDoNExDZARCRvHAekiTNfREREREREJsDki8gMXbp0CZMnT8alS5fEDoVEwjZARCRvHAekiZcdEpmh8vJynD9/HuXl5WKHQiJhGyC5KS2rwrn0mzidch3nMwpxs+jOstWFtyvx/vYkPBLYDsH+znBsZSVypESmwXFAmph8ERERkSgEQcD/zl3DR9/8hp3/yYS6SlurTFmFBnPX3bnvRalUYPggT8x6IRCP93bjc5qIqMVh8kVEREQml5R+E9OW/oKTSQV676PRCPj28CV8e/gSHvRtg83vhKJv9w7NGCURkXHxni8iIiIymepqLd7dfAaPvPidQYnXX53PKET/iT/i/947hfKKaiNGSETUfJh8EZkhNzc3LF26FG5ubmKHQiJhGyApKq+oxqg5h7Dknwmoqq59iaGhtFoB67Yl4ckZP6OoWG2ECInMB8cBaWLyRWSGWrdujWHDhqF169Zih0IiYRsgqVFXafDsG4fxQ2y20ev+5Uw+hs78GSVlVUavm0gsHAekicnXPdatW4fIyEgUFRW1qLpJegoLC7Fz504UFhaKHQqJhG2ApGbOmhP4+decZqv/RGIBXl4cC0EQmu01iEyJ44A0Mfn6Q1FRESIiIrB27Vq0atWqxdRN0pSfn4+oqCjk5+eLHQqJhG2ApOTQiSvYuCPVoH3ioocj++CLiIservc+uw9l4Zuffzc0PCKzxHFAmph8/SEhIQGCIKBbt25QKpUtpm4iIiJzVlJWhSnvHDd4P9d2dujYwR6u7ewM2m/Wyv/h2g0+F4mIzBOTrz8kJCQAAIKDg1tU3URScez6NVh9vwPvX7z/t+NW3+/AyJOGn8RRy7Bi9iMQEqdg0ki/Orcf2foUKuJfRpBvGxNHZhpS7QPb9l3A5bxSk73ezaJK/Ovfv5ns9YxFqsffEHL/DCB59ANJJ1+VlZVYv349+vfvjzZt2sDKygqurq7o1asXIiIikJubi927d0OhUGDevHkAgE2bNkGhUOh+Vq5cqasvKysLUVFRGDp0KHx8fGBvbw87Ozt0794dq1evhlpdc6UlQ+oGALVajS1btmDw4MFwdnaGtbU1fH198dZbb6GkpKSZ/1pEROKK/OgMki7cxPvzesOjQ83ZjjnjgzCopxve+SgByRm8/6GlEAQBH31j+kRoy540VNXxwGYyb/wMIDmQ7EOWCwoKMHjwYCQmJkKlUqFz587w9fXFlStXEB8fj7i4OEybNg2lpaUIDQ3F6dOnUVFRgeDgYNjZ/dnh+/Xrp/v/5cuX49NPP4W9vT3c3d0RFBSEa9euITExEYmJiYiLi8Pu3bt15Q2pOyMjAyNHjkRycjJUKhW8vb3h4uKCjIwMrFmzBgcPHsTx48dr7E/SZWdnh969e/N4y5gc20BVtRYTF8fi5JfDsTVyAIbOPAAA6OLVGiteD8GJxGuI+jxJ5CjJEL8k5CPl4i2Tv25eQRn2Hb2E0UM6m/y1qfH4GVCTHMcBOZBs8jV37lwkJiZi7NixeP/999GhQwfdtitXruCrr76Cr68v/Pz8MH78eDg4OAAAYmJi4OTkVGedTz75JGbNmoXg4GAoFArd7+Pj4xEWFoY9e/YgKSkJ3bp1AwBMmDBBr7pv3ryJYcOGISMjA1OnTsWKFSvg4uICALhw4QJGjBiBhIQEfPDBB1i0aJEx/jxk5jw9PbFhwwaxwyARybUNnPntBlZtPYe3XwnGtNFdsfXbdGxf8SgUCmDi4lhotVzJriU5Epcn2msfjb/K5KsF4mfAn+Q6DkidZJOvPXv2QKlUYvPmzbC3t6+xzcPDAxEREbp/p6eno6ysDJ06dbpv4gUAL7zwQp2/DwkJwZAhQ/Dll18iOTlZl3zpW3d4eDgyMjIQHh6ODz/8sMY2Pz8/bNiwAYMHD8bevXuZfMmERqNBeXk5bG1tZbdIS5lGg+uVlWKHITo5t4Hlm89g+CBPrJvbCz38ndG7mwvejDqJ9Cx5PKpDSn3gdMp1Wb52U0jp+DeW3D8D7pLzOCDlfiDJ5Ku6uhpqtRoajQZHjx7F008/XW/5s2fPAgB69OhRb7nKykrs27cPx44dQ2ZmJoqLi6HV3rmmPDk5GQBgZWVlUN3Jycn4+uuv4erqilWrVtVZ5u6+V65cuW9sw4YNw88//4x33nkHkZGR9b4PMn8XLlzAhAkTsH37dvj7+4sdjkktS0vGsrRkscMQnZzbQHW1gImLYxEXPRyvvhCA4wlX8eGX58UOy2Sk1AfOpN4Q9bU1Gi2UypZ1e7uUjn9jyf0z4C45jwNS7geSTL5UKhVGjhyJnTt3IiwsDI8//jjGjBmD4cOHw8PDo1Z5fZKvw4cPY9KkScjOzq73tb29vQ2qOzo6GlqtFhqNBkOGDKmzzN2FPP46g3fXjh07dK/TFCEhIbh69WqT66G6jRkzRu+y165dAwDs378fp0+f1nu/UaNGGRxXU9laWCClR1+j1TfV0xuj3R+oc9uwE8eaXH+XLl1QrjX9jfiGHH+gcW1AjOOvhSXQdrHR6y0qUaNSrYGVpRI/Hc+GMZ+b69elCyxQZbT6WlofAEzXD660WQwoLOvcFhc9vN5l5F3b2er+m33wxfuWu3q9DD1f2lfr9xWVGjzQyRcWUNexl3EZsw20xOPfHJ8DzfkZABj/c0BfPBfQj7mfC7i6uiI+Pr5R+0oy+QKA7du3IzAwEJ988gliYmIQExODWbNmYdCgQVi5ciX69OmjK9tQgnTy5Ek89dRTqKqqwpQpUzBu3Dg8+OCDaNOmDVQqFVJTUxEQEACVSoWAgIAa+zZUd0xMDIA7C4QUFBTU+568vLxq/e727duYM2cO1q1bh/Hjx9e7f0OuXr1a7+waNU1pqf5LLZeXl+v+a8h+Yhw/O6US6GG8+nxbtcIT7Ts0XLCRcnNzUabRNFv992PIcQQa1wZE6b8KK6Ct8av9bNkAWFkqkXKxEIun98COA5n4PafYKHXn5eYCgvFOyFtaHwBM2A/a3H/W6e5zvBqiUlroVa4ueVevAZrmX+bemG2gRR7/ZvgcaM7PAMD4nwP64rmAfqR6LgBIOPmysbFBZGQk3nnnHZw9exbfffcdNm3ahCNHjmDIkCFITU3VzYI1lCAtWLAAarUaq1evxvz582tt//HHHwEAgYGBsLa2rrGtobpzcnIA3FnGvlOnTga+S2DRokXo0qULxo0b1+Tky9XVtUn7U/3uN3NZl7sfsra2tgbtV9fMbnOztWhZl/S4u7uLMvNlyHEEGtcGxDj+WljC2EsqvD42EI/1csfCf8TjuyOXkPDNSHy6bAAGTf7JKPW7ubsbfearpTFVP8iFBgLqvlfl6vWyevd1bWcLldIC1Rotrl6//0OT66vHzbU9LOCkV6xN0dLagLGPv7E/B5r7MwAw/ueAvnguYB6a2geacs4s2eTrLoVCgeDgYAQHB2POnDl46KGHkJ2djVOnTmHUqFHIz89Hfn4+nJyc6pxZ0mg0iI2NBQBMmjSp1natVouvvvoKQO2HKDdUN/Bnx6qoqDD4vcXHx2PLli0GTUU3VB81n7i4OL3LpqamIjo6GsOGDTPoOu+/LthiCkJFBaqfn2jy122s9PR0KGxsTP66hhx/oHFtQIzjX1pWhVZ9thutPl9PR6wKD8GppAKs+TQRWq2AyI0JWBXeE6+PDcSGr1Oa/BoX0tNhb1f3pXCN0dL6AGC6fhAwYhdSM+teJKGuSwXvlX3wRXTsYI+r18vxwJB/G/zazk7WuHLu9xqrEzeXltYGjH38jfk5YIrPAMD4nwP64rmAeRDrXACQ+EOW/8rJyUm3IMbdpdzT0tIA3Ln2sy6lpaXQ/DEtWVeC9N577+HMmTMAaidfDdUN3FlGFAAOHTqk9/sA7iSFM2bMwGuvvYagoCCD9iXz5+vriwMHDsDX11fsUEgkcmwDCgXw+fJHobRQYOLiY7olpdd+loS48wVYFR4C744OIkdJhngksJ2or22KxIuMh58BNclxHJADySVf27Ztw/z585GSUvObkcLCQsyYMQMXL16Ev7+/7p6vu9O4OTk5dV5P6+joqJu+XbFihS4RU6vViIqKwpIlS6BS3ZlA/Gvy1VDdwJ83Xi5YsAC7du2qtT0jIwNLly7F0aNHa/z+n//8J/Lz87myoUSpVCrdPYUkT3JsA3MndkNocAe8/VFCjdkSrVbAy0tioVJa4NNlA0SMkAwlavIVIN5rU+PwM6AmOY4DciC55OvYsWNYu3YtgoKC4OLigp49eyIoKAju7u7YvHkzOnbsiN27d+uelxAUFARPT0/k5ubC09MTffv2Rf/+/fHxxx/r6rz7bK3NmzfDw8MDPXv2hIuLCxYtWoQNGzZAEAQoFIpa93XpU/ebb76JAQMGoLi4GM899xycnZ3xyCOPIDg4GO3atYOfnx8iIyNr3A92/fp1LFmyBG+//Taqq6tx69Yt3Lp1C8Cd2blbt27plsCnliknJwdz587V3RNI8iO3NuDfuTWWz3oY/zt3De9tq72kdMrFW4jcmICBIW54fWygCBFSY4Q9WvdqZSZ57YHivTYZjp8BtcltHJALyaXS06dPh6urK2JjY5GVlYXExERYWVkhKCgII0aMwOzZs9G6dWtdeRsbG+zfvx8LFy7EiRMncPLkSQiCgFdeeUVXZubMmVCpVIiKikJWVhaUSiXCwsIwf/583TLxPj4+cHR0rBGLPnXb2tri8OHD2LhxI6Kjo5GSkoKkpCS0a9cOXl5eGD9+PMLCwtC5c2fdPjk5OSguLsaMGTMwY8aMGq+5Zs0arFmzBpmZmfe9z4zMX0lJCY4fP45p06aJHYrJDGznAvUzz9dbpqHtUiK3NpCaWQTbntvqLbN6ayJWb000UUSmJ8U+4NepNYb0dcfB/+Wa9HW7d22Lvt1dTPqaTSXF428IfgbUJrdxAJBHP5Bc8tWnT58ay8jrIzAwEHv37q23zLRp0+7b+IV6Hj6hT92WlpaYPXs2Zs+e3VCoAO5cA3zkyJFav3/ssccwceJEvPzyy1y5kIiIzMKrLwSYPPma+XwA7/ciIrMkueRLDlq1aoVBgwbVuc3Ly+u+24iIiExt+KBOGBjiimPxV03yet382mDSSD+TvBYRkaEkd88XERERmQ8LCwW2Lh0AO5vm/75XqVTg8+WPwsqy7meLERGJjTNfElLf5Y/UsrRv3x7h4eFo37692KGQSNgGSEp8HnDEhxG9MX3Zr3rvc/fhyQ09jPleS2c+jIdFXGGRyJg4DkgTky8iM+Ts7Ixx48aJHQaJiG2ApGbaGH9cvVGOt/+VoFf5hh7C/FevvRSIhdO6NyY0IrPEcUCaeNkhkRm6ffs2Dh06hNu3b4sdComEbYCkaMmMYKx9oyeMvRbG/MkP4R9v9eEiGyQpHAekickXkRnKzc3FwoULkZtr2hXCyHywDZBU/d+khxD72dPw9XRsuHADPFzs8NO/nsTqOT2ZeJHkcByQJiZfREREZFL9H3bFuZ2jsGBKd7RxtDJ4/1Z2lnh9bCDO73kWwwbwYcpE1HLwni8iIiIyOTtbFVaGh2Dx9B745sDv+Py7C4hPvo6yiuo6y1tbKRHs3xbjw3zx9zBfOLYyPGkjIhIbky8iIiISjZ2tCpNGdsGkkV2g0WiRmlmEpAs3UVpeDa1WgL2tJQJ9nBDk0waWlrxgh4haNiZfRGbI2toaXbt2hbW1tdihkEjYBkiOlEoLBPm2QZBvG7FDIRIdxwFpYvJFZIY6d+6ML774QuwwSERsA0RE8sZxQJo4f09ERERERGQCTL6IzFBaWhpCQ0ORlpYmdigkErYBIiJ54zggTUy+iMyQIAioqqqCIAhih0IiYRsgIpI3jgPSxHu+iKhprK2h2rFN7Cj0xxuXjcrOVoWSExPEDsMgdrZGHvpaWh8A2A+MraW1ASMff34OkNz7gCHY8oioSRQKBWBjI3YYJBKFQgF7O0uxwxAV+wDJvQ3wc4Dk3gcMwcsOiYiIiIiITIAzX0RmyMvLC9HR0fDw8BA7FBIJ2wARkbxxHJAmJl9EZsjGxgY+Pj5ih0EiYhsgIpI3jgPSxMsOicxQXl4e3n33XeTl5YkdComEbYCISN44DkgTky8iM1RUVIR9+/ahqKhI7FBIJGwDRETyxnFAmph8ERERERERmQCTLyIiIiIiIhNg8kVERERERGQCTL6IzFDbtm0xceJEtG3bVuxQSCRsA0RE8sZxQJoUgiAIYgdBREREREQkdZz5IiIiIiIiMgEmX0RERERERCbA5IuIiIiIiMgEmHwRERERERGZAJMvIiIiIiIiE2DyRUREREREZAJMvoiIiIiIiEyAyRcREREREZEJMPkiIiIiIiIyASZfREREREREJsDki4iIiIiIyASYfBEREREREZkAky8iIiIiIiITYPJFRERERERkAky+iIiIiIiITOD/AQaHpm6Yt6UuAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1094.88x451.5 with 1 Axes>"
       ]
@@ -440,7 +443,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1094.88x451.5 with 1 Axes>"
       ]
@@ -688,15 +691,110 @@
     "problem.post_processing([1, 0, 1])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transpiling options\n",
+    "\n",
+    "The `Grover` class doesn't expose the quantum circuit it uses to the user. However, this may lead to problems when running on actual quantum hardware, since the quantum circuit has to be transpiled beforehand. In order to still let the user choose the transpilation method and its properties in general, it is possible to pass to the `Grover` class a `Transpiler`, which is any object having a `run` method that can transpile a `QuantumCircuit` into one another, or a list of `QuantumCircuit` into one another.\n",
+    "\n",
+    "For instance, let us define a custom backend on three qubits like so."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.providers.fake_provider import GenericBackendV2\n",
+    "\n",
+    "coupling_map = [(0, 1), (1, 2)]\n",
+    "backend = GenericBackendV2(num_qubits=3, coupling_map=coupling_map, seed=54)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us define a `PassManager` for this backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "pm = generate_preset_pass_manager(optimization_level=2, backend=backend)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then pass this `PassManager` to the `transpiler` keyword argument of the `Grover` constructor. We can also pass any options to the `run` method of the `Transpiler` by setting the `transpiler_options` keyword argument to a dictionary specifying said options. For instance, let us define a `callback` function that prints out that it has been called on the very first step of the transpilation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def callback(**kwargs):\n",
+    "    if kwargs[\"count\"] == 0:\n",
+    "        print(f\"Callback function has been called!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then instantiate and use the `Grover` class like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Callback function has been called!\n",
+      "111\n"
+     ]
+    }
+   ],
+   "source": [
+    "expression = \"(a & b & c)\"\n",
+    "try:\n",
+    "    oracle = PhaseOracle(expression)\n",
+    "    problem = AmplificationProblem(oracle)\n",
+    "    problem = AmplificationProblem(oracle, is_good_state=oracle.evaluate_bitstring)\n",
+    "    grover = Grover(\n",
+    "        sampler=StatevectorSampler(), transpiler=pm, transpiler_options={\"callback\": callback}\n",
+    "    )\n",
+    "    result = grover.amplify(problem)\n",
+    "    print(result.assignment)\n",
+    "except MissingOptionalLibraryError as ex:\n",
+    "    print(ex)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.0</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td colspan='2'>Mon Feb 19 11:19:12 2024 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>2.0.2</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.4.0</td></tr><tr><td><code>qiskit_aer</code></td><td>0.17.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.13.3</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td colspan='2'>Tue Jun 17 10:39:43 2025 CEST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -708,7 +806,7 @@
     {
      "data": {
       "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -743,7 +841,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.5"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/tutorials/07_grover_examples.ipynb b/docs/tutorials/07_grover_examples.ipynb
index 56ce6ba7..8b6214a1 100644
--- a/docs/tutorials/07_grover_examples.ipynb
+++ b/docs/tutorials/07_grover_examples.ipynb
@@ -9,6 +9,15 @@
     "This notebook has examples demonstrating how to use the Qiskit Algorithms [Grover](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.Grover.html) search algorithm, with different oracles."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "We strongly recommend to run this tutorial using Qiskit 2.0 or newer. Though the use of `PhaseOracle` is possible in Qiskit < 2.0, it requires the `tweedledum` library, which has been deprecated for a long time and isn't compatible with the most recent versions of Python.\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -51,39 +60,24 @@
     "\n",
     "`1 -2 3`, or `-1 -2 -3`, or `1 2 -3`.\n",
     "\n",
-    "With this example problem input, we then create the corresponding `oracle` for our `Grover` search. In particular, we use the `PhaseOracle` component, which supports parsing DIMACS-CNF format strings and constructing the corresponding oracle circuit."
+    "With this example problem input, we then create the corresponding `oracle` for our `Grover` search. In particular, we use the `PhaseOracle` component, which supports parsing DIMACS-CNF format strings and constructing the corresponding oracle circuit. Note that the `PhaseOracle` requires the `tweedledum` library if you use Qiskit in a lower version than `2.0`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\"The 'tweedledum' library is required to use 'BooleanExpression'. You can install it with 'pip install tweedledum'.\"\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import os\n",
-    "import tempfile\n",
     "from qiskit.exceptions import MissingOptionalLibraryError\n",
     "from qiskit.circuit.library.phase_oracle import PhaseOracle\n",
+    "from qiskit.synthesis.boolean.boolean_expression import BooleanExpression\n",
     "\n",
-    "fp = tempfile.NamedTemporaryFile(mode=\"w+t\", delete=False)\n",
-    "fp.write(input_3sat_instance)\n",
-    "file_name = fp.name\n",
-    "fp.close()\n",
     "oracle = None\n",
     "try:\n",
-    "    oracle = PhaseOracle.from_dimacs_file(file_name)\n",
+    "    oracle = PhaseOracle(BooleanExpression.from_dimacs(input_3sat_instance).expression)\n",
     "except ImportError as ex:\n",
-    "    print(ex)\n",
-    "finally:\n",
-    "    os.remove(file_name)"
+    "    print(ex)"
    ]
   },
   {
@@ -117,12 +111,20 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "000\n"
+     ]
+    }
+   ],
    "source": [
     "from qiskit_algorithms import Grover\n",
-    "from qiskit.primitives import Sampler\n",
+    "from qiskit.primitives import StatevectorSampler\n",
     "\n",
-    "grover = Grover(sampler=Sampler())\n",
+    "grover = Grover(sampler=StatevectorSampler())\n",
     "result = None\n",
     "if problem is not None:\n",
     "    result = grover.amplify(problem)\n",
@@ -135,14 +137,25 @@
    "source": [
     "As seen above, a satisfying solution to the specified 3-SAT problem is obtained. And it is indeed one of the three satisfying solutions.\n",
     "\n",
-    "Since we used the `Sampler`, the complete measurement result is also returned, as shown in the plot below, where it can be seen that the binary strings `000`, `011`, and `101` (note the bit order in each string), corresponding to the three satisfying solutions all have high probabilities associated with them."
+    "Since we used the `StatevectorSampler`, the complete measurement result is also returned, as shown in the plot below, where it can be seen that the binary strings `000`, `011`, and `101` (note the bit order in each string), corresponding to the three satisfying solutions all have high probabilities associated with them."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from qiskit.visualization import plot_histogram\n",
     "\n",
@@ -165,11 +178,14 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\"The 'tweedledum' library is required to use 'PhaseOracle'. You can install it with 'pip install tweedledum'.\"\n"
-     ]
+     "data": {
+      "image/png": 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vvvnG2BJEREREVAomXcfuUQ8ePCjRvndEREREVHZlSl0qlarQdhFBfHw8tm3bxoMliIiIiMpJqbbYqdVqaDQa/fp0H374of7n/BcLCwt4enrixIkTePnll80ycCIiIiIyVKotdp06ddJvpdu3bx/c3NwKXbdOo9GgVq1a6NatG8aMGWOSgRIRERFR8UoV7Pbu3av/v1qtxsiRIxEeHm7qMRERERGREYzex06n05lyHERERERURmY9KpaIiIiIyk+Jt9iNGjUKKpUKn376KerUqVPihYdVKhW+/vprowdIRERERCWjEhEpSUe1Wg2VSoVz586hcePGUKtLtrFPpVJBq9WWaZCVQVpaGuzt7XH37l3Y2dlV9HCIiIgUw2PaljLdPnZ2LxONpHIqTQYp8Ra7K1euAADq1atn8DMRERERVQ4lDnbu7u7F/kxEREREFYsHTxAREREpRIm32MXFxRldxM3NzejbEhEREVHJlDjYeXh4FHlu2OKoVCrk5uaW+nZEREREVDolDnbDhg0zKtgRERERUfkocbBbtWqVGYdBRERERGXFgyeIiIiIFILBjoiIiEgheEoxIiIiIoXgKcVKiKcUIyIiMg+eUqx4PKUYERER0VOIpxQjIiIiUggePEFERESkEGUOdr/++iv69esHNzc32Nvbw83NDf3798eGDRtMMDwiIiIiKqkSfxX7qNzcXLzyyiv4+eefISKwsLCAg4MDkpKSsHHjRmzatAkDBgzADz/8AAsLo8sQERERUQkZvcVu1qxZWL9+PTp27Ij9+/fjwYMHuH79Oh48eIB9+/YhKCgIP//8M2bPnm3K8RIRERFREUq83MmjvLy8YGNjg1OnThW6RS4nJwe+vr7IysrC5cuXyzzQisblToiIiMyDy50UrzQZxOgtdtevX0efPn2K/JrV0tISffr0wfXr1426/yVLlsDDwwM2NjYIDAzEkSNHiuz7yy+/oE2bNqhRowaqVq0KPz8/fPfddwZ9RATh4eFwcXFBlSpVEBwcjIsXLxo1NiIiIqLKyOhg5+rqivT09GL7ZGRkwM3NrdT3vXbtWoSFhSEiIgInTpxAy5YtERISghs3bhTav1atWnj//fcRFRWFU6dOYeTIkRg5ciR27Nih7/PZZ5/hv//9L5YtW4bDhw+jatWqCAkJwYMHD0o9PiIiIqLKyOhg99prr2HdunVFbpFLSEjA2rVr8dprr5X6vufNm4cxY8Zg5MiRaNasGZYtWwZbW1usWLGi0P5dunTBCy+8gKZNm6JBgwaYMGECfH19ceDAAQAPt9YtWLAAM2bMQL9+/eDr64tvv/0WiYmJPHqXiIiIFKPEwS4uLs7gMnDgQLRv3x7+/v6YPXs2Dhw4gIsXL+LAgQOYNWsWWrdujaCgIISGhpZqQNnZ2Th+/DiCg4P/b5BqNYKDgxEVFfXY24sIIiMjceHCBXTq1AnAw7NkJCUlGdynvb09AgMDS3SfRERERE+CEq9D4uHhAZVKVaBdRPD+++8X2r5x40Zs3rwZubm5JR5QamoqtFot6tSpY9Bep04dnD9/vsjb3b17F/Xq1UNWVhY0Gg2WLl2K5557DgCQlJSkv49H7zPvukdlZWUhKytL/3NaWhqAhweF5OTkAHgYODUaDbRaLXQ6nb5vXntubi7yH5ui0WigVquLbM+73zx5+y8++vgV1W5paQmdTmdwbl6VSgULC4si24saO+fEOXFOnBPnxDmV15xMobLNyZTP06NjLU6Jg92wYcMKDXaVRfXq1REdHY309HRERkYiLCwMXl5e6NKli1H3N2vWLMycObNA+++//w5bW1sAgJubG/z9/XHq1CnExcXp+zRp0gTe3t44cuQIUlJS9O1+fn5wd3fHvn37cO/ePX17u3bt4OTkhN9//93ghdO1a1dUqVIFW7duNRjD888/j/v372PPnj36NgsLC/Tq1QupqakGWyGrV6+Obt26IT4+HtHR0fr22rVro3379rh48SIuXLigb+ecOCfOiXPinDin8p6TKVS2OZnyecrMzCzx42D0cifmkp2dDVtbW6xfvx79+/fXtw8fPhx37tzBb7/9VqL7ee211xAfH48dO3bg8uXLaNCgAU6ePAk/Pz99n86dO8PPzw8LFy4scPvCtti5uroiNTVVf6jxk/aJSImf8jgnzolz4pw4pyd/Tl7vbUNZxM7uVenmZMrnKS0tDY6OjiVa7qTSnRLCysoKrVu3RmRkpD7Y6XQ6REZGYvz48SW+H51Opw9mnp6ecHZ2RmRkpD7YpaWl4fDhw3jjjTcKvb21tTWsra0LtFtaWsLS0tKgTaPRQKPRFOhb1FIwxS0RU9Z2tVoNtbrgrpNFtRc1ds6JcyptO+fEOQGcU1FjLG370zYnU6hsczLl81TUmApT6YIdAISFhWH48OFo06YNAgICsGDBAmRkZGDkyJEAHn4tXK9ePcyaNQvAw69N27RpgwYNGiArKwtbt27Fd999hy+++ALAw6T87rvv4pNPPkGjRo3g6emJDz74AHXr1jXYKkhERET0JCtTsLt37x4WL16MXbt2ITEx0eCryzwqlQoxMTGlut9BgwYhJSUF4eHhSEpKgp+fH7Zv364/+CEuLs4gCWdkZODNN9/EtWvXUKVKFXh7e2P16tUYNGiQvs+UKVOQkZGBsWPH4s6dOwgKCsL27dthY2Nj5OyJiIiIKhej97FLSUlB+/btERMTAzs7O/3pLrKzs3H//n0AQN26dWFpaYkrV66YdNAVgacUIyIiMg+eUqx45XJKsQ8//BAxMTH49ttvcfv2bQDAxIkTkZGRgcOHDyMgIAAeHh44e/assSWIiIiIqBSMDnZbt27Fs88+i1dffbXAMiht27bFtm3bEBsbW+iSIURERERkekYHu+vXr8Pf31//s0aj0X8FCwA1a9ZEz549sW7durKNkIiIiIhKxOhgZ29vb7AGTM2aNXHt2jWDPnZ2dkhOTjZ+dERERERUYkYHOy8vL8TGxup/9vf3x86dO3Hz5k0AwP3797Fp0ya4ubmVeZBERERE9HhGB7vu3bsjMjJSf5qL119/HTdu3EDLli0RGhoKHx8fxMTEYMSIEaYaKxEREREVw+hgN27cOCxfvlwf7F588UXMmTMHGRkZ+Pnnn5GUlISwsDBMnjzZZIMlIiIioqKZ/FyxWq0WqampcHJyKnC07JOM69gRERGZB9exK15pMojJTymm0Wj0Z4ggIiIiovJT5mB3/fp1rFmzBidPnsTdu3dhb28Pf39/vPzyy3BxcTHFGImIiIioBMoU7JYsWYLJkycjKysL+b/RXb16Nd5//33MnTsXb775ZpkHSURERESPZ3SwW7NmDd5++204Ojri/fffR8eOHVGnTh0kJydj3759WLhwof76gQMHmnLMRERERFQIow+eaNWqFa5du4bo6GjUrVu3wPXXrl2Dv78/3NzccPz48TIPtKLx4AkiIiLz4METxStNBjF6uZNz585h4MCBhYY6AKhfvz5CQ0Nx7tw5Y0sQERERUSkYHexq1KiBqlWrFtunWrVqqFGjhrEliIiIiKgUjA52ffv2xaZNm5Cbm1vo9Tk5Odi0aRP69etn9OCIiIiIqOSMDnafffYZqlatiu7du+PQoUMG10VFRaF79+6oXr06Zs+eXeZBEhEREdHjlfioWC8vrwJt2dnZOHHiBDp06AALCws4OjoiNTVVvxXPxcUFrVq1QkxMjOlGTERERESFKnGw0+l0BU4RZmlpCTc3N4O2Rw+m0Ol0ZRgeEREREZVUiYNdbGysGYdBRERERGVl9D52RERERFS5lPlcsQCQm5uLCxcuIC0tDXZ2dmjSpAksLExy10RERERUQmXaYnfr1i2MGTMG9vb28PX1RVBQEHx9fVGjRg2MHTsWN2/eNNU4iYiIiOgxjN6sduvWLTzzzDO4dOkSatWqhY4dO8LFxQVJSUk4duwYvvrqK/zxxx+IiopCrVq1TDlmIiIiIiqE0VvsPv74Y1y6dAmTJ0/G1atXsX37dqxcuRLbtm3D1atXMXXqVFy8eBH//ve/TTleIiIiIiqCSkTEmBt6eXnBw8MDu3fvLrJPt27dEBsbi8uXLxs9wMqiNCfgJSIiopLzmLalTLePnd3LRCOpnEqTQYzeYpeYmIh27doV26ddu3ZITEw0tgQRERERlYLRwc7e3h5Xr14tts/Vq1dhb29vbAkiIiIiKgWjg13nzp3x008/YdeuXYVeHxkZiZ9++gldunQxtgQRERERlYLRR8VGRERgy5YtCAkJwfPPP4/OnTujTp06SE5Oxt69e7Ft2zbY2toiPDzclOMlIiIioiIYHeyaN2+OHTt2YMSIEdiyZQu2bNkClUqFvGMxGjRogFWrVqF58+YmGywRERERFa1Mp4cICgrCxYsX8eeff+LkyZP6M0/4+/ujQ4cOUKlUphonERERET2G0cFu1KhRaNGiBSZOnIigoCAEBQWZclxEREREVEpGHzzxww8/4MaNG6YcCxERERGVgdHBrkGDBrh+/bopx0JEREREZWB0sBs1ahS2bNmChIQEU46HiIiIiIxk9D52AwYMwJ49e9C+fXtMmTIFbdu2RZ06dQo9YMLNza1MgyQiIiKixzM62Hl5eemXN3nnnXeK7KdSqZCbm2tsGSIiIiIqIaOD3bBhw7icCREREVElYnSwW7VqlQmHQURERERlZfTBE0RERERUuZTpzBMAkJWVha1bt+LkyZO4e/cu7O3t4e/vj+effx7W1tamGCMRERERlUCZgt3GjRsxduxYpKSk6M8RCzw8YMLJyQlffvkl+vTpU+ZBEhEREdHjGR3sIiMjMWDAAGg0GowaNQodO3ZEnTp1kJycjH379mH16tV48cUXsWPHDnTr1s2UYyYiIiKiQqgk/6a2UggKCsKpU6dw8OBB+Pj4FLj+1KlT6NChA/z8/LB///4yD7SipaWlwd7eHnfv3oWdnV1FD4eIiEgxPKZtKdPtY2f3MtFIKqfSZBCjD544efIkBg0aVGioAwBfX18MHDgQJ06cMLYEEREREZWC0cHO1tYWtWvXLraPk5MTbG1tjS1BRERERKVgdLALDg7Grl27iu2za9cuPPfcc8aWICIiIqJSMDrYzZ07Fzdu3MCwYcMQHx9vcF18fDyGDh2K1NRUzJ07t8yDJCIiIqLHM/qo2KFDh6JmzZr4/vvvsWbNGri5uemPio2Li4NWq4Wvry9effVVg9upVCpERkaWeeBEREREZMjoYLd37179/3Nzc3H58mVcvnzZoM9ff/1V4HY8vywRERGReRgd7HQ6nSnHQURERERlxHPFEhERESmEyYJdXFwc9u3bZ6q7IyIiIqJSMlmwW7lyJbp27WqquyMiIiKiUuJXsUREREQKwWBHREREpBAMdkREREQKYbJgZ29vDzc3N1PdHRERERGVksmC3bvvvosrV66Y6u6IiIiIqJT4VSwRERGRQpT4zBN5a9QFBATAxsamVGvWderUqfQjIyIiIqJSKXGw69KlC1QqFc6dO4fGjRvrfy4JrVZr9ACJiIiIqGRKHOzCw8OhUqng6Oho8DMRERERVQ4lDnYffvhhsT8TERERUcXiwRNERERECmF0sLt37x4uX76MnJwcg/a1a9diyJAhGD16NE6cOFHmARIRERFRyZT4q9hHTZkyBatXr0ZycjIsLS0BAF988QXGjx8PEQEArFmzBsePH4e3t7dpRktERERERTJ6i90ff/yB4OBg2Nra6ttmz56NevXqYd++fVi3bh1EBHPmzDHJQImIiIioeEZvsbt+/Tp69Oih//ncuXOIj4/HZ599hqCgIADA+vXrS7XeHREREREZz+gtdllZWbCystL//Mcff0ClUqF79+76Ni8vLyQkJJRthERERERUIkYHu/r16+PUqVP6nzdv3oxatWrB19dX33bz5k1Uq1atbCMkIiIiohIx+qvYnj17YsmSJZg0aRJsbGywfft2DBs2zKDPP//8Azc3tzIPkoiIiIgez+hgN336dGzatAnz5s0DALi4uOCjjz7SX3/jxg38+eefGD9+fNlHSURERESPZXSwc3Z2xtmzZxEZGQkA6NSpE+zs7PTXp6amYs6cOQgJCSn7KImIiIjosYwOdgBQpUoV9O7du9DrmjVrhmbNmpXl7omIiIioFHhKMSIiIiKFKNMWO61Wi3Xr1mHXrl1ITExEVlZWgT4qlUr/dS0RERERmY/RwS4jIwPdu3fHoUOHICJQqVT6U4kB0P+sUqlMMlAiIiIiKp7RX8V+8skniIqKwsyZM5GamgoRwYcffojr169j7dq18PLyQmhoaKFb8YiIiIjI9IwOdr/88gueeeYZzJgxA7Vq1dK316lTB6GhodizZw927drFc8USERERlROjg11cXByeeeaZ/7sjtdpg61z9+vXRq1cvfPPNN2UbIRERERGViNHBrmrVqlCr/+/m9vb2uH79ukEfZ2dnxMXFGT86IiIiIioxo4Odu7u7QWjz8fHB7t279VvtRASRkZFwcXEp+yiJiIiI6LGMDnbPPvss9uzZg9zcXADA8OHDERcXh3bt2mHy5MkICgpCdHQ0BgwYYLLBEhEREVHRjF7uZMyYMXBwcEBKSgpcXFwwatQonDx5EkuXLkV0dDQAYMCAAfjwww9NNFQiIiIiKo5K8i8+ZwIpKSm4fPky3N3d4ezsbMq7rlBpaWmwt7fH3bt3Dc6JS0RERGXjMW1LmW4fO7uXiUZSOZUmg5TpzBOFqV27NmrXrm3quyUiIiKix+C5YomIiIgUwugtdl5eXiXqp1KpEBMTY2wZIiIiIioho4OdTqcr9Dywd+/exZ07dwAALi4usLKyMnpwRERERFRyRge72NjYYq8LCwtDcnIydu7caWwJIiIiIioFs+xj5+HhgbVr1+L27dt4//33zVGCiIiIiB5htoMnLC0t8dxzz2HdunXmKkFERERE+Zj1qNjMzEzcunXLnCWIiIiI6P8zW7Dbv38/fvzxRzRp0sRcJYiIiIgoH6MPnujWrVuh7bm5uUhISNAfXBEeHm5sCSIiIiIqBaOD3d69ewttV6lUqFmzJrp3746wsDA899xzxpYgIiIiolIo0zp2RERERFR5lPlcsTdu3EBCQgJ0Oh3q1asHZ2dnU4yLiIiIiErJqIMnsrKy8Nlnn6FRo0ZwcXFBmzZtEBAQgHr16sHR0RETJ04sdgFjIiIiIjK9Uge7+Ph4tG3bFtOnT0dMTAxcXFwQEBCAgIAAuLi44NatW1i4cCHatGmDXbt26W93/fp1rmlHREREZEalCnY5OTl4/vnncebMGQwePBjnzp3DtWvXEBUVhaioKFy7dg3nzp3DkCFDcOvWLfTv3x+xsbGIiYlBUFAQzp8/b655EBERET31SrWP3f/+9z+cPXsWERERiIiIKLRPkyZN8N1336Fx48aIiIjAkCFDEBsbi9TUVLRu3dokgyYiIiKigkq1xW7dunVo2LBhidammzFjBho1aoSoqCg8ePAAO3bsQK9evYweKBEREREVr1TB7u+//0b37t2hUqke21elUun7Hj58GF26dDF2jERERERUAqUKdunp6bC3ty9xfzs7O1hYWKBhw4alHhgRERERlU6pgp2TkxMuXbpU4v4xMTFwcnIq9aCWLFkCDw8P2NjYIDAwEEeOHCmy79mzZzFgwAB4eHhApVJhwYIFBfp8+OGHUKlUBhdvb+9Sj4uIiIioMitVsGvXrh22bduGpKSkx/ZNSkrCli1bEBQUVKoBrV27FmFhYYiIiMCJEyfQsmVLhISE4MaNG4X2z8zMhJeXF2bPnl3s4sjNmzfH9evX9ZcDBw6UalxERERElV2pgt24ceOQnp6OF154AampqUX2u3nzJl544QVkZmbi9ddfL9WA5s2bhzFjxmDkyJFo1qwZli1bBltbW6xYsaLQ/m3btsWcOXPw8ssvw9rausj7tbCwgLOzs/7i6OhYqnERERERVXalCnZdu3bFmDFjcPjwYTRt2hQzZszA7t27cfHiRVy8eBG7d+/G+++/j6ZNm+Lw4cMYM2ZMqQ6ayM7OxvHjxxEcHPx/A1SrERwcjKioqNIMtYCLFy+ibt268PLywpAhQxAXF1em+yMiIiKqbEp9rtilS5fCzs4O8+fPx6xZszBr1iyD60UEarUakyZNKnDd46SmpkKr1aJOnToG7XXq1CnT4saBgYFYtWoVmjRpguvXr2PmzJno2LEjzpw5g+rVqxd6m6ysLGRlZel/TktLA/BwkeacnBwAD0OnRqOBVquFTqfT981rz83NhYjo2zUaDdRqdZHtefebx8Li4dOTm5tbonZLS0vodDpotVp9m0qlgoWFRZHtRY2dc+KcOCfOiXPinMprTqZQ2eZkyufp0bEWp9TBTqPRYM6cORg7dixWrVqFqKgo/T53zs7OaN++PYYPH45GjRqV9q7NpmfPnvr/+/r6IjAwEO7u7li3bh1Gjx5d6G1mzZqFmTNnFmj//fffYWtrCwBwc3ODv78/Tp06ZbAFsEmTJvD29saRI0eQkpKib/fz84O7uzv27duHe/fu6dvbtWsHJycn/P777wYvnK5du6JKlSrYunWrwRief/553L9/H3v27NG3WVhYoFevXkhNTTXYulm9enV069YN8fHxiI6O1rfXrl0b7du3x8WLF3HhwgV9O+fEOXFOnBPnxDmV95xMobLNyZTPU2ZmZokfB5Xkj7EVLDs7G7a2tli/fj369++vbx8+fDju3LmD3377rdjbe3h44N1338W777772Fpt27ZFcHBwkVsVC9ti5+rqitTUVNjZ2QF48j4RKfFTHufEOXFOnBPn9OTPyeu9bSiL2Nm9Kt2cTPk8paWlwdHREXfv3tVnkKKUeoudOVlZWaF169aIjIzUBzudTofIyEiMHz/eZHXS09MRExODoUOHFtnH2tq60IMxLC0tYWlpadCm0Wig0WgK9M17MZS0/dH7NaZdrVZDrS6462RR7UWNnXPinErbzjlxTgDnVNQYS9v+tM3JFCrbnEz5PBU1psJUqmAHAGFhYRg+fDjatGmDgIAALFiwABkZGRg5ciQAYNiwYahXr55+S1t2djb+/vtv/f8TEhIQHR2NatWq6RdGnjRpEvr06QN3d3ckJiYiIiICGo0GgwcPrphJEhEREZlBpQt2gwYNQkpKCsLDw5GUlAQ/Pz9s375df0BFXFycQQpOTEyEv7+//ue5c+di7ty56Ny5M/bu3QsAuHbtGgYPHoybN2+idu3aCAoKwqFDh1C7du1ynRsRERGROVWqfewqs7S0NNjb25fo+20iIiIqOY9pW8p0+9jZvUw0ksqpNBnENF9sExEREVGFY7AjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLCjp8aSJUvg4eEBGxsbBAYG4siRI8X2/+mnn+Dt7Q0bGxu0aNECW7duLbLvuHHjoFKpsGDBgkKvz8rKgp+fH1QqFaKjo8swCyIioqIx2NFTYe3atQgLC0NERAROnDiBli1bIiQkBDdu3Ci0/8GDBzF48GCMHj0aJ0+eRP/+/dG/f3+cOXOmQN9ff/0Vhw4dQt26dYusP2XKlGKvLw/lHWxjY2MxevRoeHp6okqVKmjQoAEiIiKQnZ1tqikREdEjGOzoqTBv3jyMGTMGI0eORLNmzbBs2TLY2tpixYoVhfZfuHAhevTogcmTJ6Np06b4+OOP0apVKyxevNigX0JCAt5++218//33sLS0LPS+tm3bht9//x1z5841+bxKqiKC7fnz56HT6fC///0PZ8+eRXBwMD755BPY2tqW2xbTf//732jfvj1sbW1Ro0aNYusRESkBgx0pXnZ2No4fP47g4GB9m1qtRnBwMKKiogq9TVRUlEF/AAgJCTHor9PpMHToUEyePBnNmzcv9H6Sk5MxZswYfPfdd7C1tTXBbIxTEcG2R48eWLlyJbp3746jR49i1apVGDBgAJydnctti2l2djZCQ0PxxhtvlPShIiJ6ojHYkeKlpqZCq9WiTp06Bu116tRBUlJSobdJSkp6bP///Oc/sLCwwDvvvFPofYgIRowYgXHjxqFNmzZlnIXxKjLY5skLlo0bN4azs3O5bTGdOXMmJk6ciBYtWhQ7PiIipWCwe4qYeh+rDz/8EN7e3qhatSpq1qyJ4OBgHD582KDPP//8g379+sHR0RF2dnYICgrCnj17TD638nb8+HEsXLgQq1atgkqlKrTPokWLcO/ePUyfPr2cR2eoooJtnrxg2bx5cyxatAivv/56uQdLoorE914qTwx2Twlz7GPVuHFjLF68GKdPn8aBAwfg4eGB7t27IyUlRd+nd+/eyM3Nxe7du3H8+HG0bNkSvXv3LjJQmIOjoyM0Gg2Sk5MN2pOTk+Hs7FzobZydnYvtv3//fty4cQNubm6wsLCAhYUFrl69in/961/w8PAAAOzevRtRUVGwtraGhYUFGjZsCABo06YNhg8fbuJZlq+SBNs8ecHy3//+N0JDQzFmzBgA5Rcs82RlZZX7H1cA2LJlCwIDA2FpaQm1Wg2NRlMu9ffu3QuVSlXo5ejRo497uEyuvMNNZZn/0/zeW1lURLAG/u93v0qVKqhZsyb69+9vymkVTahE7t69KwDk7t27FT0UowQEBMhbb72l/1mr1UrdunVl1qxZhfYfOHCg9OrVy6AtMDBQXn/99SJr5D1Gu3btEhGRlJQUASD79u3T90lLSxMAsnPnzrJMp9QCAgJk/Pjx+p+1Wq3Uq1ev2Pn37t3boK1du3b6+aempsrp06cNLnXr1pWpU6fK+fPnRUTk6tWrBtfv2LFDAMj69eslPj7eTDMtKCsrSzQajfz6668G7cOGDZO+ffsWehtXV1eZP3++QVt4eLj4+vqKiMj8+fNFpVKJRqPRXwCIWq0Wd3d3g9sdP35cAEiPHj1Eq9Xq2ydPniwBAQGF1re0tJQffvjBoG3JkiXi5OQkIiLHjh2TOnXqSEJCgv56d3f3AmPOM27cOAEgK1askLNnz8qYMWOkRo0akpycXGj/P//8UzQajXz22Wfy999/y4wZM8TS0lJOnz6t7/P999/Lzp07JSYmRs6cOSOjR48WOzs7uXHjhr7P+vXrpWbNmjJ69GixtLSUTz75RD7//PNyqZ+VlSXXr183uLz22mvi6ekpOp2u0LrmsmbNGrGysirXx7+yzL8yvPcuXrxY3NzcBIB4e3vL4cOHix3zunXrpEmTJmJtbS0+Pj6yZcsWg+sjIiKkSZMmYmtrKzVq1JBnn31WDh06ZNDH3d1dABhcipqziIj71M1luhSlIl57Iv/3u//FF1/IhQsX5OzZs7J27dpiH/filCaDMNiV0JMc7Mzxh72wGnPmzBF7e3tJSUkRERGdTidNmjSR1157TdLT0yUnJ0fmzJkjTk5OcuvWrTLPqzTWrFkj1tbWsmrVKvn7779l7NixUqNGDUlKShIRkaFDh8q0adP0/f/880+xsLCQuXPnyrlz5yQiIqLAL/ejigsWIiJXrlwRAHLy5ElTTavEKiLYiohcu3ZNGjZsKCqVSn7++WeD+yuvYCki4uXlJVZWVgbzN/cf15ycHKlXr5589dVXFfLH/VHZ2dlSu3Zt+eijj4q8D3OpDPNfuHChqNVqsbCwkICAgHIJN3nvvevWrZOWLVvqf//L87131apVYmVlJQMHDpRatWrJsGHDyiXYuLu7y0cffWQQrNPT0wt/sMV8wa4iXnv5f/dNpTQZhF/FPgXMtY8VAGzevBnVqlWDjY0N5s+fj507d8LR0REAoFKpsGvXLpw8eRLVq1eHjY0N5s2bh+3bt6NmzZomnOHjDRo0CHPnzkV4eDj8/PwQHR2N7du36+cYFxeH69ev6/u3b98eP/zwA7788ku0bNkS69evx4YNG+Dj41Ou4zaVsLAwLF++HN988w3OnTuHN954AxkZGRg5ciQAYNiwYQb7Ak6YMAHbt2/H559/jvPnz+PDDz/EsWPHMH78eACAg4MDfHx8DC6WlpZwdnZGkyZNADw8sKFLly5wd3dHy5YtsWXLFiQlJSEpKQk6nQ6RkZFo165doeNt164dIiMjDdp27typ7z906FCcOnUK0dHR+kvdunUxefJk7Nixw+B22dnZiI2NhYWFhb7NFPv4PVrjyy+/hL29PVq2bAkAOHHiBBISEqDT6XDkyBF8//336NmzJ86cOVMu9R+1ceNG3Lx5U/+clxdzHbzzaI3i5r927Vr861//gohgx44d5fZ1aN57708//WRw1HZ5vveOGDECOTk52L9/P3bt2oWVK1eW+cClV155BcHBwfDy8kLz5s0xb948pKWl4dSpUwb3Vb16dTg7O+svVatWLbSmuVTUay/vd1+tVsPf3x8uLi763/3ywGBHZdK1a1dER0fj4MGD6NGjBwYOHKh/sxQRvPXWW3BycsL+/fsxadIkpKeno1WrVmjVqlW57OeQfx2zGTNm4OrVq8jKysLhw4cRGBio77d3716sWrXK4LahoaG4cOECsrKycObMGTz//PPFjjc2Nhbvvvtukddv2bIF7u7ueOaZZ8plP4/8CwSPGDEC1apVw4QJE8ot2O7cuROXLl1CZGQkoqOjsWLFCri4uMDFxaVcgmXevPbu3QudTgcR0YfA9PR0s/9xvXz5sv55Ah4eGVyzZk106dIFt27dMnv9R3399dcICQlB/fr1C73eXCrqg2V+8+bNQ926ddGjRw9069bNJEdllzTcAMCRI0dMvo5lSd578/YvnjNnDvr3748+ffogOTm53EL17Nmz4eDgAH9/f8yZMwe5ubkmmHnJVdRrL//v/owZM7B582aD331zY7B7Cpjj4IE8VatWRcOGDfHMM8/g66+/hoWFBb7++msADw8e2Lx5M9asWYNr165h/vz5WLhwof6Ag/L4xFxZ1jGriB2oH10geMWKFbC2tsbbb79dLsF2xIgRkIe7e0BEsGjRIri5ucHKyqrctpiGh4cjJCQEAHD//n34+/vD398fx44dK9X9FKW4P646nQ7Aw5AKAN7e3li5ciVUKhV++ukns9fP79q1a9ixYwdGjx5tkrqVRUnmn7fVJj4+Xj//8tpiqNVqAQDjx483WMeyvN57Fy5cCK1Wi/bt22Pp0qWoUqUKvvnmm3IJ1e+88w7WrFmDPXv24PXXX8enn36KKVOmFFrzSVSS3/33338fAwYMQOvWrU3+u18cBrungJWVFVq3bm3w1VZZvworik6nQ1ZWFgAgMzMTwMM30fwL5NrY2KBv377l8om5sqxjZo4Fgh83//wLBHt5eaFv376YNGkSfvnll3KZ86PGjx9f7ltMV61ahaysLGg0Gvz666/6kNmlSxez/3F1cXEBALRt21b/wcra2hpeXl6Ii4sze/38Vq5cCQcHB/Tt27fQeuZUUR8s8+RttbG3tzeYv7nDjYhg7NixqF+/Pq5evaq/TXm/9+anVqv1oaOsHheqw8LC0KVLF/j6+mLcuHH4/PPPsWjRIv0Yy0NFvfbyfvebNWum75//d9/cGOyeEqbexyojIwPvvfceDh06hKtXr+L48eMYNWoUEhISEBoaCuDhG1TNmjUxdOhQHD9+HM2aNcPkyZNx5coV9OnTp9z3MaoolWEfozx3795FrVq1jJjFk6uiPti0bt0a1tbWuHz5sr5+Tk4OYmNj4erqavb6eUQEK1euRIsWLdCoUSOTLPmQk5ODqVOnokWLFqhatSrq1q2LYcOGITEx0eA+/vnnH4SGhkKlUmHgwIH6tdTK4/HPP38A6NmzZ5Gn/TNWceEmbx3Lzz77DMuXL8fPP/8MAPj000/L7b13ypQp0Gg0OHnypP69t1evXuX6oSJPYGAgcnNzERsbW4pHuGwq+nf/woUL+uvzfvfd3d2NnU7JmeyQDYV7ko+KzbNo0SJxc3MTKysrCQgIMDiCq3PnzjJ8+HCD/uvWrZPGjRuLlZWVNG/e3OCosPv378sLL7wgdevWFSsrK3FxcZG+ffvKkSNHDO7j6NGj0qlTJwEgtra28swzz8jWrVtFpGzLXeTZtGmTVK1aVVQqldStW7dA/TwrV64Ue3v7Yh8fEfMcmZWQkCAA5ODBgwbt5Tl/EZGLFy+KnZ2dfPnll499HJTG1EdFp6eny/Tp0yUqKkpiY2Pl2LFjMnLkSLG2tpYzZ87o72fChAlSr149mT59ulhZWUmnTp2kVq1aMnz48HKpLyKya9cuASCWlpYmW/Lhzp07EhwcLGvXrpXz589LVFSUBAQESOvWrQ3up1GjRvL888/Lf/7zH7GyspJu3bqJjY2NvPrqq+U2/23btgkAWbRokUG7KY9MzdOwYUP59NNPRUSkX79+olarRaPRiEqlMlj2o2fPnvrbmPO9t3v37qLRaMTS0lL/3lvWI+KL4uXlJREREUVev3r1alGr1UWuiGDO5U4q8nd/x44dcv78eRk9enSZVoTgcidmYIpgt3jxYnF3dxdra+syH26fnZ0tU6ZMER8fH7G1tRUXFxcZOnSowbpeIqVfS8gczBls0tPT5eLFixIVFSWjRo0SDw+PQv9YKTXYlXT+165dkwYNGsjo0aMf+xgoVUV8sMnOzpZ//etf4uTkJNbW1mJjYyOWlpblVl9EZPDgwVKtWjWzL/lw5MgRASBXr14VkYJrqS1atEhcXV31a6mV9/xNudxPUfKHm8qyjmVFBJuDBw/K/PnzJTo6WmJiYmT16tVSu3ZtGTZsWJHjNFewE6n43/3q1atLcHBwgQ8dpVGaDGIBKhd5O88vW7YMgYGBWLBgAUJCQnDhwgU4OTkV6J+38/ysWbPQu3dv/PDDD+jfvz9OnDgBHx8fZGZm4sSJE/jggw/QsmVL3L59GxMmTEDfvn0L7Bj+0Ucf6Vf7Bx4egl6eymM/h7yvBBo1aoSvv/66wk/jlV9Fzz8xMRFdu3ZF+/bt8eWXXxY7Vo9pW0oztQJiZ/cq0+3Nafz48fqvsx61d+/eAm2hoaH6r7YeZWNjU6J9FS0tLTF37tzHHhFprvrAw/0MbW1tS70rQFhYmEFbSEgINmzYUGSdu3fvQqVSoUaNGgAeHrncpEkTfPvtt2jVqhXGjRuHBw8eYM6cOTh48KDBkkfmnP8PP/yAtWvXYvjw4WjTpg0CAgKwYMGCAl+H1qtXD7NmzQLw8OvQzp074/PPP0evXr2wZs0aHDt2TP/7k5GRgX//+9/o27cvXFxckJqaiiVLlhh8Herm5mYwjmrVqgEAGjRoUK5HJg8aNAgpKSkIDw9HUlIS/Pz8Chy4lH9fvLwDl2bMmIH33nsPjRo1MjhwSaPR4Pz58/jmm2+QmpoKBwcHtG3bFvv379ef2s/a2hpr1qzBhx9+iKysLHh6emLixIkFXlPlpTL/7psDg105yb/zPAAsW7YMW7ZswYoVKzBt2rQC/fPvPA8AH3/8MXbu3InFixdj2bJlsLe3x86dOw1us3jxYgQEBCAuLs7gTSVvLaGKkn8/h7xTquTt51DUL1vefg75d4Y3dh+bilaR809ISEDXrl31R2U9ujN1ZaPkYFlRilvy4fz584XepqQHD+R58OABpk6disGDB8POzg7A/62l1r9/f1SvXh1qtRpOTk4Vto5leYebyqS8g02rVq1w6NChUo+TTIPBrhzk7TyffytKeXxizjN79mx8/PHHcHNzwyuvvIKJEycaLNZaHsLCwsr9EzPw8A371q1biIuLg1arRXR0NACgYcOG+k/QSp1//gWC586da7AMTEUGfVKWnJwcDBw4ECKCL774Qt8uj6xjWaVKFXz11Vfo06cPjh49qj9ysLxUxFab/Dw8PPQHchCZE4NdOaioT8zAw7WEWrVqhVq1auHgwYOYPn06rl+/jnnz5hV6P+baYlJRn5jDw8PxzTff6H/29/cHAOzZswddunQp01xLoyLmn7dA8KVLlwp89cM/ME8Pc+4KkBfqrl69it27dxu89+StpXb79m19+9KlS7Fz50588803hX5T8TTj1moyFQY7BSjqEzMAg61+vr6+sLKywuuvv45Zs2bB2tq6XMdZEZ+YV61aVWB9tIpS3vMfMWIERowYUdphUgUqyx/3ov6wm2tXgLz3nYsXL2LPnj1wcHAwuI/SrqXGYENkGgx25aCiPjEXJv9aQvlPvUREyg0Xpt4VICcnBy+99BJOnDiBzZs3Q6vV6r9NqFWrFqysrPRrqQ0fPhzh4eGoUqUKli9frl9LjSoXpb72S0pJ82ewKwcV9Ym5MNHR0fqdmIno6WDqXQESEhKwceNGAICfn59BrbzdHBwdHbF9+3a8//776NatG3JyctC8eXP89ttvlW4RcUBZf9jp6cZgV04q4hNzVFQUDh8+jK5du6J69eqIiorCxIkT8eqrr5b7UWlEVLFMuStASQ8EaNOmDXbs2FGqcRJR2TDYlZOK+MRc2dYSosfjVgMiIioLBrtyVN6fmJ/EtYQYbIiIiIxXuVcrJSIiIqIS4xY7IiLi1nIiheAWOyIiIiKF4Ba7SoafmomIiMhY3GJHREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBHREREpBAMdkREREQKwWBHREREpBCVNtgtWbIEHh4esLGxQWBgII4cOVJs/59++gne3t6wsbFBixYtsHXrVoPrRQTh4eFwcXFBlSpVEBwcjIsXL5pzCkRERETlqlIGu7Vr1yIsLAwRERE4ceIEWrZsiZCQENy4caPQ/gcPHsTgwYMxevRonDx5Ev3790f//v1x5swZfZ/PPvsM//3vf7Fs2TIcPnwYVatWRUhICB48eFBe0yIiIiIyq0oZ7ObNm4cxY8Zg5MiRaNasGZYtWwZbW1usWLGi0P4LFy5Ejx49MHnyZDRt2hQff/wxWrVqhcWLFwN4uLVuwYIFmDFjBvr16wdfX198++23SExMxIYNG8pxZkRERETmY1HRA3hUdnY2jh8/junTp+vb1Go1goODERUVVehtoqKiEBYWZtAWEhKiD21XrlxBUlISgoOD9dfb29sjMDAQUVFRePnllwvcZ1ZWFrKysvQ/3717FwBw69Yt5OTk6Mel0Wig1Wqh0+kMxqvRaJCbmwsR0bdrNBqo1eoi23NycqDLynzsY1ScO3fuQKvV6n9WqVSwsLCATqcrtP3RsZe1/s2bNwEYzik/C4uHL7nc3NxC28taPy0trcjnoyTPk6nmDxQ9V0tLyyKfj7LWv337ttGvPcC08y/ta0+tVpukvrGvvdzcXJM8/sa+9oCyPf587fG1V1GvPeD/Hn+jXns6nUne+4197QGmm78xr72SPE/37t0DAIN5FEkqmYSEBAEgBw8eNGifPHmyBAQEFHobS0tL+eGHHwzalixZIk5OTiIi8ueffwoASUxMNOgTGhoqAwcOLPQ+IyIiBAAvvPDCCy+88MJLpbjEx8c/NkdVui12lcX06dMNtgLqdDrcunULDg4OUKlUFTKmtLQ0uLq6Ij4+HnZ2dqzP+qz/lNR/mufO+qz/tNcHoN9qV7du3cf2rXTBztHRERqNBsnJyQbtycnJcHZ2LvQ2zs7OxfbP+zc5ORkuLi4Gffz8/Aq9T2tra1hbWxu01ahRozRTMRs7O7sKe3GxPuuzfsXVf5rnzvqs/7TXt7e3L1G/SnfwhJWVFVq3bo3IyEh9m06nQ2RkJNq1a1fobdq1a2fQHwB27typ7+/p6QlnZ2eDPmlpaTh8+HCR90lERET0pKl0W+wAICwsDMOHD0ebNm0QEBCABQsWICMjAyNHjgQADBs2DPXq1cOsWbMAABMmTEDnzp3x+eefo1evXlizZg2OHTuGL7/8EsDDnRbfffddfPLJJ2jUqBE8PT3xwQcfoG7duujfv39FTZOIiIjIpCplsBs0aBBSUlIQHh6OpKQk+Pn5Yfv27ahTpw4AIC4uDmr1/21sbN++PX744QfMmDED7733Hho1aoQNGzbAx8dH32fKlCnIyMjA2LFjcefOHQQFBWH79u2wsbEp9/kZy9raGhEREQW+ImZ91md9Zdd/mufO+qz/tNcvLZVISY6dJSIiIqLKrtLtY0dERERExmGwIyIiIlIIBjsiIiIihWCwIyIiIlIIBjsiIiIihWCwe4LlP2Hw0yL/Qdyc/9M3/6dxzkREpcFg9wTSarUAoF/LT0TK7Q9eRf9hValUyMjIAACDtQzLa9Uezr9i569Wq3Hz5k39z+W9WlPe715FyZuviJT73CuTp3nuAOdPxeM6dk+Q1NRU/PLLLzh79iwuXryI1q1bY8iQIfD29i73seS9bFQqVaHXFdZeVrGxsVi7di0OHjyImJgYdOjQAf3790fXrl3LfaHpvD+s+cNV/us4f9PP/8yZM1i9ejV+//13JCYmIjAwED179sSzzz6LRo0ambX2o/ICbmHzNycRQXp6OqpXr16gvbzmXd5zzi8zMxOZmZlwdHSEVquFRqMp1/oVUTO/p33+qampyMjIQI0aNZCVlYWaNWvC0tKy3Orn5ubCwqJSntfBAIPdE6Rfv344e/YsPD094erqisOHD+PcuXPw9fXFtGnTEBoaatZfum+//Rbe3t7w9fU1CBI6nQ4qlcrsf1i6deuGmzdvok2bNnB2dsbevXtx7NgxuLi4YPLkyRg3bpxZ5z979mz4+vqic+fOqFq1qr5dq9VCrVZz/maef2BgICwsLBASEgInJyds374dO3bsQLVq1TBu3DjMmDED1tbWZgs577zzDtq2bYu+ffsanIxbq9VCpVKZPfDs2bMHK1euxPnz55Geno6QkBAMGDAAQUFBZq1bmKKCbXEf+Mpq/fr1WLVqFU6ePAkRQceOHdG3b19069YNLi4uJq9XnIoI9k/7/L/66iusXLkS0dHRUKvVaNeuHbp27Ypu3bohMDAQQPl9wMnbcl+RIbdYQk+EyMhIcXBwkMuXL4uIyL179+T69euyY8cOGTp0qHh7e8vy5cvNVn///v2iVqslODhY3nrrLVm1apWcO3fOoM+DBw/ko48+ksTERJPX3717tzg4OEhqaqqIiOh0OhERuXDhgkyaNElcXV1l2rRpJq+bZ//+/aJSqeSZZ56R3r17y0cffSRRUVEGfTIzM+Xtt9+Wq1evmrz+0z7/PXv2iKOjo2RkZBi0Z2VlycKFC6V+/foSGhoq9+/fN3ltkf+bv4+Pj7Ro0UJGjx4t27ZtM+iTmZkpI0eOlLNnz5q8/oEDB6Rp06bSqVMnmT17tkydOlVatmwparVaWrVqJRs3bjR5zfz+/vtvmTZtmuzfv19yc3MNrsvNzdW/Hs3ljz/+EE9PT3nppZdk7dq1snjxYunSpYuo1Wpp0KCBrFixwqz1T548Ka+88ops2rRJcnJyDK7j/M0//z179ki9evXknXfekSNHjsiGDRvk5ZdfFhcXF3FxcZFPP/3UrGOIioqSoKAg+fbbbyUrK8vgupycHNFqtaLT6eTmzZtmfyxKgsHuCREeHi7BwcGFXpeSkiLTpk2TqlWryqlTp8xSf8KECdK2bVsJCwuTzp07i7+/v/Ts2VOmTp0qv/76q1y7dk2ioqJEpVLJvXv3TF7/P//5j7Rr107/S5X3iyQikp2dLV988YVUr15d9u/fb/LaIiKTJ0+Wjh07yueffy7Dhw+Xjh07Srt27WTgwIGyaNEiuXDhghw6dEhUKpWkpaWZvP7TPv8lS5aIv7+/PthmZWVJdna2/votW7aIg4ODbNq0yeS1RUTee+89efbZZ+WHH36QiIgI6dGjhzRt2lTatm0rU6dOlePHj8uRI0fMNv8XX3xRRo8ebdCm1Wrl6NGjMmTIEGnQoIH88ssvJq+bZ9iwYVK1alXp2LGjDBgwQD7//HM5ffq0QZ+DBw/K2LFjzfKH7aWXXpIxY8YUaE9JSZFJkyZJ7dq1Zf78+Savm2fYsGFiY2Mjvr6+0rp1a5k4caIcPHjQoM+ff/4pgwYNKhB8TeFpn//LL78sY8eOLdCenZ0t8+fPFwcHB5kwYYLJ6+YZNmyYWFpairu7u9SqVUteeukl2bFjh0GfAwcOSEhISIHgWxH4VewTYv/+/XjppZfw3XffoXv37gWuz87ORs+ePfHCCy9g/PjxJq8/dOhQODo6Yv78+cjKysL27duxZcsWnDp1CjqdDl5eXjhz5gycnZ2xa9cuk9c/e/YsunXrhkWLFmHgwIH6dsm36b1Pnz4IDAzEjBkzTF5/1KhR0Gg0WL58OXJzc3Ho0CHs27cPJ06cQGJiImxsbHDx4kX4+Phg27ZtJq//tM//2rVraN++PaZMmWLw+s6/z1doaCg8PT3x2Wefmbz+u+++i3v37mH58uVQq9U4d+4cTp48iSNHjuDEiRO4desWrl69ik6dOmHLli0mr9+5c2c8++yzCA8PB2A477t372Ls2LG4cuUKtm3bBgcHB5PX9/f3R3BwMGrXro3jx4/j2rVrUKvVaNCgAbp27YrnnnsOH3/8Mf744w/8/fffJq/ft29fuLu7Y9GiRQAevt+p1Wr9/k6TJk3Cjh07sHXrVri6upq8fmBgILp37w4fHx8cO3YMx44dw/Xr11GnTh307NkToaGhmDdvHv744w+cOXPG5PWf9vkPGzYMOTk5+Oabb2BlZYUHDx5Ao9Ho96/773//iy+++AIbNmxAkyZNTF6/U6dOeO655xAcHIyjR49iy5YtOHbsGGxtbfHiiy9i7NixWLZsGfbt24e//vrL5PVLrWJzJZVUZmamDBs2TJo1ayZz5syR6OhoSU9P119/9+5dqVevnvz8889mqX/69GnZunVrgfbExET5+uuvZcCAAaJSqWTLli1mqZ+bmythYWHi5OQkb775pmzfvl1u3rypvz45OVnq168v69evN0v969evy549ewq037x5UzZv3iwTJ04sl/nXrl37qZt/3hag2bNnS7Vq1aRHjx6yatUqg6/8Y2NjxdXVVdatW2fy+iIPd304dOhQoe3Hjh2T+fPnm/X5nz9/vtSrV08uXbpk0J732Fy+fFkaNWok0dHRJq/9zz//SOfOnWXlypUiInLnzh3ZsWOHvP/++9K7d28JDAyUzp07i0qlkg0bNpi8vojI6tWrpXbt2nLkyBGDdq1WKyIPt1x5enoW+hyV1eXLl6VXr17yv//9T0REMjIy5OTJk/LVV1/JmDFjJDAwULy9vUWlUslvv/1m8voinP/27dvFyclJfv31V4P2vK1j6enp4urqKvv27TN57YSEBBk9erR+V6fs7GxJSEiQyMhICQ8Pl4CAAKlZs6aoVCqz7xJRUtxi9wSJi4vDrFmzsHv3bjg6OiIgIADOzs5Qq9U4dOgQTp8+jX/++cds9XNycmBpaQmdTqffYpC31WDz5s149dVXcefOHbPVz8rKwqJFi7Bp0yZkZ2fD1dUVtWrVgp2dHY4cOYI7d+4gOjrabPXzyP8/IjT/ASObNm3C4MGDkZ6ebra6GRkZ+OKLL/Dbb78hOzsb7u7u5TJ/eWSH5LznX6PRlOv8AeCXX37Bjz/+iKtXr8LCwgJ2dnZwcXHByZMnUa1aNRw4cMCs9fM8+phs2rQJoaGhePDggVnqpaSkYMiQIYiLi8PLL7+M4OBgtGzZUn907K+//ophw4bh3r17Zql/6dIl6HQ6NG7c2KA9MTERUVFR+PLLL3HkyBHcvn3b5LVFBBkZGRgzZgx+//139O7dW380eI0aNQAAa9euxWuvvWa2+ScnJyMjIwNeXl4G7bdv38b58+fxxRdfYOPGjWZ5/8ub/2uvvYadO3c+lfPPysrC9OnTsWTJEgQGBmLo0KEYMGAAHBwccOfOHfz2228YP3682eZ/8+ZNZGZmFtgampWVhaSkJCxevBjLly8369+/0mCwewJFR0fju+++w6FDhyAiuH37NgIDAxEWFgZfX99yHUteyHnppZeQlpZmlq9hH3X+/Hls3rwZ0dHRuHPnDq5fv45u3brhjTfeKPDGUx50Oh3efPNN3Lp1C+vWrTN7vStXrmDTpk36P6RJSUkVNv+857885x8fH48DBw7g7NmzSEhIwLVr1xASEoJXX30Vzs7OZq//KBFBREQEkpOT8b///c9sdf755x988cUXOHDgAKysrODq6gpbW1tkZGTg77//Ro8ePTBnzhyz1c+j1WohIgbLPvTv3x9Vq1bF999/b7a69+7dw8qVK7Ft2zakpqZCo9GgevXqEBEkJCRg4MCBmDlzptnq5/dosO/fvz9sbGywZs0as9VMT0/HqlWrsHHjRqSmpsLKyqpC5l/U82/u+QMPNyD8+OOPOHXqFFJSUlC7dm1YW1sjIyMDI0eOxJQpU8xaP09hz7+FhQXWr19fLvUfh8GuksvJycHff/+NzZs3o1q1amjdujX8/f31y02cP38eXl5esLS0NMth3nn1t2zZAjs7O/j7+8PDwwN16tSBhYWFfl2j3Nxc3L171yz79wCFv5kA0P9ym9vjlrTQarW4d++e/hO0qWRmZuLQoUNYt24datSoAR8fH/j7+6N58+YAHn6SrlOnjklrFlb/p59+Qq1atdC0aVM0bNgQjRs3Rq1atfT9zDX/PLm5uQBQ4PnPysqCtbW1WWrm97jnX6fTISMjo8D6cuZw+vRpbN68GefPn8ft27eRmZmJd999F926dYOtra3Z6+cnIrh58yYCAwPx7bffokOHDmaveeHCBRw8eBCxsbG4du0aHjx4gLfeegutW7cul9fCo+7cuYMXX3wR//nPf9C2bVuz10tMTMS+fftw7tw5xMfHIysrq1zmn/eNTX46nQ5paWlmn3/+IHX9+nWcPXsW8fHxuHLlCu7fv49Ro0ahUaNGFbLGXHp6Ot5++21MmDABfn5+5V6/MAx2ldykSZPw448/wsnJCbdu3UJ8fDzc3Nzw8ssvY8qUKQZ/XMurvpeXFwYPHowJEybA0dHRrPVPnTpVYCtkdnY2VCpVuSxMWVj98lq3DYD+6xcPDw+kpqYiPj4eTk5OCAoKwsSJE9GyZctyr+/i4oKOHTvirbfegr+/v1nrHz58WL9GVZ5Hn39zLhpaWP3c3FyD3RDMuWhvYmIifvzxR0RFRaFhw4bw8/NDYGAgPD09odVqkZmZadYwmVf/8OHDaNSoEZo3b46mTZuiUaNGqFatmn7uGRkZBmsbmlphwb48F0t+XLDPzMw0S6i+dOkSFi9ejOPHj+s/UD3zzDMIDAwslxCfV//EiRNo3LgxvLy80KJFC7Rt29Zg6/j9+/dRpUoVs42johYEz1+/uLVay+sDZomVx458ZJyzZ89K9erVZcuWLZKYmCharVZiY2MlPDxc6tWrJ3Z2dvLNN99UWH17e3v57rvvRETMssTBxYsXxcLCQjp06CDh4eEFllfQ6XSSlZUlhw8fLrC2UHnUF3m4I6256p89e1aqVasmBw4ckMzMTBERuXXrlvz3v/+V5s2bi1qtlv/85z8GS5+Ud/3PPvvMbPXPnz8vKpVK6tWrJ6NHjy6wY3je879x40ZJSkpSXP0rV65I27ZtpUGDBhIaGipNmjSRGjVqSLNmzeRf//qXwcEz5lBY/Zo1a0qLFi1kypQpkpKSYtDf1K+B1NRU+fPPPw3atFqtZGVl6Xea1+l0Zlleo6j6efXyzzWvvqnnHxMTI02bNpWAgAB555135NlnnxUfHx9p2bKlDBs2TI4ePWrSeiWp36JFC2nVqpWMGDGiwIEcpp5/cnKy/PbbbwbPr06n068bJ/Lw9fDgwQOT1i2ufl7N/PLWzqwM69flYbCrxD755BPp1KmT/uf8L7D09HSZMGGCtGjRQm7cuKHI+h9++KG4ubnJuHHjpH379tKkSRMJCQmRxYsXS0JCgoiIxMfHi0qlkvj4eMXV//zzzyUoKEj/86Phcc6cOeLp6alftFpp9T/++GNp3ry5hIeHS1BQkNjb20vjxo3lgw8+kCtXrojIw6N1VSqVxMXFKa7+66+/Lr169TJ4bV25ckUiIiKkdu3a4uzsLNu3bzd53ZLWd3FxKbCWlym98847olKppHHjxjJ58mQ5f/68wfW5ubly9epVWbdunVnC3ePqa7VafX1zrF02btw46dOnj9y+fVvflpCQIEuXLhV/f3+pXr26rF692uR1S1rfzs7OrPXfeustUalU4ujoKMOHDy80ZF+5ckXmzJljlnBX0fXLgsGuEvv555/F29vbYCX/nJwc/R/Yf/75R3x9feWLL75QZP3BgwfLu+++KwkJCXLkyBFZtGiRvPrqq+Lv7y/NmjWTIUOGSP/+/aVZs2aKrL9r1y7x8PAwWHQ4JydHv/UsKSlJnnnmGfn0008VWf+NN96Qt99+W27cuCHx8fGyefNmCQsLEx8fH6levbp06NBBnnvuObM9/hVdv3379vL555+LyMMtw/nDg1arlX79+kn//v1FxDxbCyq6fqtWrWTUqFEybdo0fZBp1aqVzJs3T27duiUiIjNnzhRPT0+T164M9UNCQmTmzJki8jDEPhpex40bJx07dpQHDx6Y5fGv6PqBgYESFhYm//3vf6VDhw6i0WjEzc1Npk2bJjExMSLy8MN3gwYNTF67MtQvCwa7Siw1NVW8vb2lWbNmsn79+kI/Ffj6+urXF1JS/ZycHFm9erXMmjXLoD0pKUl27twpn376qfTv319UKpVZTqVW0fVFHq5dGBwcLHXr1pUvv/xSH6jya9mypSxZskRx9XNzc2XLli0F7js9PV3OnTsnP/74owwfPlxUKpV8/fXXiqsv8vBsM23atDH4vcvOztY/D5GRkdKwYcMCX4kpoX5sbKyEhIToT+H0zz//yE8//SRjxoyRBg0aiJ2dnYSEhIiDg4PMmzdPcfVFRObNmyeenp76ECHycKt53vMRHR0tnp6e8scffyiu/rVr1+Sll17Sv7empaXJsWPHZMaMGfo181q1aiXVq1c3yxk3Krp+WTHYVXIJCQkyaNAg8fX1leeff14iIiJk7969cuXKFQkLCxMHBweDhYqVVj9P/tNH5fnxxx9FpVIVOH+okupnZmbKxIkTxdPTU5o3by6jRo2SDRs2yN69e2Xo0KFSt25dsz7+FV0/z6P7tYiIbNq0qdye/4qof/ToUXF2dpY2bdoUeqq0CxcuiLW1tSLr3717V1atWiV79+41aL9z545ER0fL119/LR07dhSNRlPoB44nvb7Iw33c/Pz8xMvLS1atWlXg+jNnzoilpaXZnv+KrJ+RkSEbN24ssF+rVquV1NRUiYyMlN69e5vt8a/o+mXFo2KfADdu3MDWrVuxa9cuXL16FRcuXEBqaiq6dOmC1157Da+88ori6hd1xFv+IxInT56Mo0ePYu/evYqrn38M9+7dw4EDB3DgwAEcPXoUx44dQ25uLoKDgzF69Gj06tVLcfXl/6+PV9xRcDNnzsTBgwexY8cOxdXPc+nSJUydOhXHjh2Dg4MDOnTogOeffx4XLlzAjz/+CFdXV7OunVXR9YGHz4VWqy1w5POgQYNw48YN7NmzR7H17927h2nTpmHNmjXIzc3Fc889hx49euDMmTPYu3cvWrRoge+++06x9fPII+vGAQ9Pc3n16lXs27dP8fVLi8GukkpOTsaVK1dgbW2NKlWqwMvLC2q1GjExMcjMzETVqlXh6OhotuVOKkt9KysriAg8PDwM1sgTEfz222+oV6+eWdZOquj6hQXL7OxspKSkoEqVKnjw4AHs7e3NtsREZaz/qL1796J27dr6Nf2UVD+/jIwMREZGYvfu3Th69ChOnz4NBwcHjB49Gq+++io8PDwUXT+PiOifl/v376Nz586YNm0aBgwYoMj6ebUePHiA06dPY9++fdi9ezeOHz8OLy8vDBkyBC+++CJcXFwUV/9xy4vcv38f/fr1wxtvvIEXXnhBcfXLisGuElq+fDlWrlyJEydOwMLCAk2aNEHTpk3x7LPPom/fvmZbBLiy1m/WrBm8vb3RoUMH9OrVC/Xr11d0/fzythbkP31XeaoM9R+35UyJ9bdu3Yrbt29Dq9XC1dUVAQEBqFq1KjIzM6HRaHDv3j2zriFZWern5uaidu3aCAwMNHjfycrKwq5du8y2tbqi6xcm//qZd+/ehb29fbnVrgz188vJycGxY8fQrl27p7L+4zDYVTI3b95Eo0aN8NZbb2HMmDFIS0vD1q1bERkZiYsXL8LHxwcLFy6Ep6dnoZuHlVz/0qVLaNGiBebPnw9PT0+zLExb0fVv376NVq1aYcCAARg5cqTB1qD8i6SeO3cO9evXN/nitJW5fv5P0efOnYOLi4vJz3RR0fXv3buHcePGYefOncjNzYWLiwuqVq0KBwcHdO/eHaGhofoPFuZYoLey1a9bty6qVasGBwcHdOnSBQMHDoS7u7tJa1am+rm5ubh16xacnJzMVoP1K299kymPHfmo5BYuXCiBgYGFXrd7925p27atNGvWzGBtIdZXVn2VSiW+vr6iUqmkadOm8tlnnxksgBsfHy9+fn4GR6uxvjLqf/LJJ9KiRQvZt2+fiIicPn1ali1bJkOGDBFfX18JDQ2VO3fumLzuk1C/ZcuWMnDgQEXXnz9/vtSoUUPGjx8v+/btK/TAhLt378rWrVsLPaDraam/efNmsywKX9H1TYXBrpJZunSpNG/eXM6dOyciD1e1zv8COnfunDRu3FjWrVvH+gqsP3r0aBk7dqxcvXpV/vzzT3n77bfF1dVV1Gq1dO7cWX788UdZsGCB2Nrasr4C63fo0EEWLFhQoF2r1cqOHTvEzc1Nv3Yc6yuvfkBAgLRv317atm0rarVavL29JSIiQk6fPq1fR27p0qVFfvhk/Se7vqkw2FUyKSkp0qJFC3nzzTf1pyoRMVxuoV27djJnzhzWV1j9Bw8eyLx58wwW/M3NzZXExET59ddfZdCgQeLo6CgqlUq/cCjrK6d+dna2jB07Vjp37iypqaki8nA9xfwLw27atEl8fHwKPb0d6z/Z9W/cuCFBQUH60zT+/fffMn36dHF3dxeNRiMdOnSQL774Qnx8fOSdd95hfYXVNyUGu0ok75ybP//8s9SvX1/s7OxkzJgxcuLECRERSUxMlB9++EGqVaumP6UR6yunvsjDcJF3urJH107Lzs6WrVu3ikqlkmvXrrG+AutHRUVJw4YNZcaMGfpwkV98fLxUrVqV9RVYPzExUebNm1fgNG25ubmyb98+GTFihNjb25vtFIasX7H1TYnBrhJ68OCBnD17VpYuXSohISFStWpVqVatmjRp0kS8vLzkgw8+YH0F109MTCz0j4qIyEcffWS2UxixfsXW1+l0kp2dLf/73//EwcFBatSoIWPHjpU9e/bI5cuX5ZdffpERI0ZI69atWV+B9UUeLgiet+BtYafp+te//iX+/v6sr9D6psKjYiuJ1NRUrF27FnPmzIGDgwNq1aqFmjVrIiAgAP7+/sjMzMTly5fRs2dPNGrUyORHo7J+5ag/d+5c1K5dG3Z2dqhbty769u2LXr16oUqVKtDpdPjqq69Qt25d9O7dm/UVVP9Rd+7cwapVq/DDDz8gOjoa9vb2sLGxQatWrTB9+nQ888wzrK/g+oV58OAB/Pz8MHLkSEydOpX1n7L6pcFgV0mMGjUKf/31F3r27Ilq1arh5s2buHTpEhISEuDu7o6ZM2eiWbNmrP8U1K9evTpu3ryJc+fOIT4+Ho0aNUJYWJhZ10xi/Yqtf//+fVSpUsWgTURw//59pKen4/Tp06hWrRoCAwNZ/ympX1ifdevWYfDgwbCysmJ9BdU3uYrbWEh5dDqd2NraGpyXUKfTyT///CNff/21tGvXTpo0aWKWHXZZv/LWv3jxoqxYsULatWsnzZo1kzNnzrC+AuuLiISFhcnPP/8ssbGx+pOsP+rWrVv6sbH+01ffXEsssX7F1zc1BrtK4MyZM+Lj4yNHjx4t9PrMzEzx9fWViIgI1md91ldY/e+//15UKpVYWlqKp6enTJw4UXbv3i1JSUn6tcLu3r0r/fr1k1OnTrH+U1I/OTlZcnJyREQkPT1d+vTpY5YPl6xfsfXNgcGuEsjMzJRu3bpJp06d5PLly4V+Ivz888/NttMu67M+61dc/dGjR8sbb7whMTEx8sknn4iHh4eoVCpp1aqVzJo1S06cOCErVqwQCwsL1md91ldYfXNgsKskDh48KH5+ftKhQwdZvXq1JCYm6o/OefDggYSGhsorr7zC+qzP+gqqn5OTI//+979l+vTpBu1//fWXjB07Vuzt7aVatWpiaWkpI0eOZH3WZ30F1TcXBrtK5NSpUxIaGio2Njbi6Ogo/fv3l3Hjxomnp6e0bdtW/vrrL9ZnfdZXWP3bt2/L+fPnRUQkKyurwBbD1atXi0qlkujoaNZnfdZXWH1z4FGxldCNGzewefNmbNiwAVWqVIGPjw9eeuklNG3alPVZn/UVXh8AdDodRAQajQbLly/HhAkTkJmZyfqsz/pPQf2yYrCr5HQ6HdRqNeuzPus/hfUBYN68edBqtZg8eTLrsz7rP2X1jcFgR0RUieXk5ECj0VRYwGR91mf9iqtvDAY7IiIiIoV4ciIoERERERWLwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBTi/wG1STRyf88BiwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -177,7 +193,7 @@
     "try:\n",
     "    oracle = PhaseOracle(expression)\n",
     "    problem = AmplificationProblem(oracle, is_good_state=oracle.evaluate_bitstring)\n",
-    "    grover = Grover(sampler=Sampler())\n",
+    "    grover = Grover(sampler=StatevectorSampler())\n",
     "    result = grover.amplify(problem)\n",
     "    display(plot_histogram(result.circuit_results[0]))\n",
     "except MissingOptionalLibraryError as ex:\n",
@@ -199,7 +215,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.0</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td colspan='2'>Mon Feb 19 11:19:26 2024 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>2.0.2</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.4.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.13.3</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td colspan='2'>Thu Jun 19 18:50:43 2025 CEST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -211,7 +227,7 @@
     {
      "data": {
       "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -246,7 +262,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.3"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/tutorials/10_pvqd.ipynb b/docs/tutorials/10_pvqd.ipynb
index 4146b69f..eb70792b 100644
--- a/docs/tutorials/10_pvqd.ipynb
+++ b/docs/tutorials/10_pvqd.ipynb
@@ -66,7 +66,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 454.517x200.667 with 1 Axes>"
       ]
@@ -79,7 +79,7 @@
    "source": [
     "from qiskit.circuit import QuantumCircuit, ParameterVector\n",
     "\n",
-    "theta = ParameterVector(\"th\", 5)\n",
+    "theta = ParameterVector(r\"$\\theta$\", 5)\n",
     "ansatz = QuantumCircuit(2)\n",
     "ansatz.rx(theta[0], 0)\n",
     "ansatz.rx(theta[1], 1)\n",
@@ -88,10 +88,10 @@
     "ansatz.rx(theta[4], 1)\n",
     "\n",
     "# you can try different circuits, like:\n",
-    "# from qiskit.circuit.library import EfficientSU2\n",
-    "# ansatz = EfficientSU2(2, reps=1)\n",
+    "# from qiskit.circuit.library import efficient_su2\n",
+    "# ansatz = efficient_su2(2, reps=1)\n",
     "\n",
-    "ansatz.draw(\"mpl\", style=\"clifford\")"
+    "ansatz.draw(\"mpl\")"
    ]
   },
   {
@@ -125,15 +125,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit.primitives import Sampler, Estimator\n",
+    "from qiskit.primitives import StatevectorSampler, StatevectorEstimator\n",
     "from qiskit_algorithms.state_fidelities import ComputeUncompute\n",
     "\n",
     "# the fidelity is used to evaluate the objective: the overlap of the variational form and the trotter step\n",
-    "sampler = Sampler()\n",
+    "sampler = StatevectorSampler(default_shots=10_000, seed=42)\n",
     "fidelity = ComputeUncompute(sampler)\n",
     "\n",
     "# the estimator is used to evaluate the observables\n",
-    "estimator = Estimator()"
+    "estimator = StatevectorEstimator(seed=43)"
    ]
   },
   {
@@ -198,7 +198,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And then run the algorithm!"
+    "And then run the algorithm! Beware that for a large number of shots to compute the fidelity, running the algorithm might take a while."
    ]
   },
   {
@@ -210,312 +210,312 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{   'aux_ops_evaluated': array([ 0.10073973, -0.82478082]),\n",
-      "    'estimated_error': 2.2479622929783005e-06,\n",
-      "    'evolved_state': <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fee60926b60>,\n",
+      "{   'aux_ops_evaluated': array([ 0.20858505, -0.76148347]),\n",
+      "    'estimated_error': 0.0,\n",
+      "    'evolved_state': <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fd85d518c90>,\n",
       "    'fidelities': [   1.0,\n",
-      "                      0.9999999980263742,\n",
-      "                      0.9999999889765994,\n",
-      "                      0.9999999725994934,\n",
-      "                      0.9999999999504244,\n",
-      "                      0.9999999987491348,\n",
-      "                      0.9999999943574918,\n",
-      "                      0.9999999997736883,\n",
-      "                      0.9999999996001944,\n",
-      "                      0.9999999992285448,\n",
-      "                      0.9999999986662624,\n",
-      "                      0.9999999979219804,\n",
-      "                      0.9999999970053304,\n",
-      "                      0.9999999959268082,\n",
-      "                      0.9999999993099946,\n",
-      "                      0.999999999120582,\n",
-      "                      0.9999999988884068,\n",
-      "                      0.9999999986060122,\n",
-      "                      0.9999999982649594,\n",
-      "                      0.9999999978557594,\n",
-      "                      0.9999999975651538,\n",
-      "                      0.9999999970055335,\n",
-      "                      0.9999999963435264,\n",
-      "                      0.9999999955648368,\n",
-      "                      0.999999994653827,\n",
-      "                      0.9999999935934853,\n",
-      "                      0.9999999923653938,\n",
-      "                      0.999999991424641,\n",
-      "                      0.9999999901871712,\n",
-      "                      0.999999989047165,\n",
-      "                      0.9999999875772592,\n",
-      "                      0.9999999861918528,\n",
-      "                      0.999999984457688,\n",
-      "                      0.9999999827846118,\n",
-      "                      0.9999999807589705,\n",
-      "                      0.9999999787650244,\n",
-      "                      0.9999999764272754,\n",
-      "                      0.999999974089888,\n",
-      "                      0.9999999714262247,\n",
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+      "                      array([0.75133922, 0.98193893, 0.18437987, 0.95455976, 0.67601781]),\n",
+      "                      array([0.7601785 , 0.99349115, 0.18654904, 0.96578988, 0.68397096]),\n",
+      "                      array([0.76901779, 1.00504337, 0.18871822, 0.97701999, 0.69192411]),\n",
+      "                      array([0.77785707, 1.01659559, 0.19088739, 0.98825011, 0.69987727]),\n",
+      "                      array([0.78669636, 1.02814782, 0.19305657, 0.99948022, 0.70783042]),\n",
+      "                      array([0.79553564, 1.03970004, 0.19522574, 1.01071034, 0.71578357]),\n",
+      "                      array([0.80437493, 1.05125226, 0.19739492, 1.02194045, 0.72373672]),\n",
+      "                      array([0.81321421, 1.06280449, 0.19956409, 1.03317057, 0.73168987]),\n",
+      "                      array([0.8220535 , 1.07435671, 0.20173327, 1.04440068, 0.73964302]),\n",
+      "                      array([0.83089278, 1.08590893, 0.20390244, 1.0556308 , 0.74759617]),\n",
+      "                      array([0.83973207, 1.09746115, 0.20607162, 1.06686091, 0.75554932]),\n",
+      "                      array([0.84857135, 1.10901338, 0.20824079, 1.07809103, 0.76350247]),\n",
+      "                      array([0.85741063, 1.1205656 , 0.21040997, 1.08932114, 0.77145562]),\n",
+      "                      array([0.86624992, 1.13211782, 0.21257914, 1.10055126, 0.77940877]),\n",
+      "                      array([0.8750892 , 1.14367004, 0.21474832, 1.11178137, 0.78736192]),\n",
+      "                      array([0.88392849, 1.15522227, 0.21691749, 1.12301149, 0.79531507])],\n",
       "    'times': [   0.0,\n",
       "                 0.01,\n",
       "                 0.02,\n",
@@ -643,7 +643,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The energy should be constant in a real time evolution. However, we are projecting the time-evolved state onto a variational form, which might violate this rule. Ideally the energy is still more or less constant. In this evolution here we observe shifts of ~5% of the energy."
+    "The energy should be constant in a real time evolution. However, we are projecting the time-evolved state onto a variational form, which might violate this rule."
    ]
   },
   {
@@ -663,7 +663,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -742,17 +742,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fee90da00a0>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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RNpmbmwP4Z8daWFhInIaIiIiyIjo6Gi4uLurv8cywGGXTp9NnFhYWLEZERER65r+GwXDwNREREdFHLEZEREREH7EYEREREX3EMUZERER5KC0tDSkpKVLHKHAMDQ0hl8tzvB4WIyIiojwgiiJCQkLw4cMHqaMUWFZWVnB0dMzRfQZZjIiIiPLAp1Jkb28PExMT3iRYh0RRRHx8PMLCwgAATk5OWq+LxYiIiCiXpaWlqUuRra2t1HEKJGNjYwBAWFgY7O3ttT6txsHXREREuezTmCITExOJkxRsn/ZvTsZwsRgRERHlEZ4+y1262L8sRkREREQf6W0x8vHxQa1atWBubg57e3t07NgR/v7+/7nc7t27Ub58eRgZGaFKlSo4cuRIHqQlIiIifaC3xej8+fMYOXIkrl27hpMnTyIlJQUtWrRAXFxcpstcuXIFvXr1wuDBg3Hnzh107NgRHTt2xIMHD/IwORERkf4YMGAABEGAIAhQKBQoXbo05s6di507d0Iul+PNmzcZLlemTBl4e3urf3748CG6d++OIkWKQKlUomzZspg5cybi4+M1litRooR6e8bGxihRogS6d++OM2fO5Orn/ERvi9GxY8cwYMAAVKpUCdWqVYOvry+CgoJw69atTJdZvnw5WrVqhUmTJqFChQqYN28evvjiC/zyyy95mDxjqaEReOv3AIF/3UHU85dIDYtEWsQHpEbHIjk+ASqVSuqIRERUSLVq1QrBwcH4+++/MWHCBMyePRtPnz6Fra0tNm7cmG7+CxcuICAgAIMHDwYAXLt2DR4eHkhOTsbhw4fx9OlTLFiwAL6+vmjevDmSk5M1lp87dy6Cg4Ph7++PTZs2wcrKCs2aNcOCBQty/bMWmMv1o6KiAAA2NjaZznP16lWN9goALVu2xIEDBzJdJikpCUlJSeqfo6OjcxY0E2GjF2LQHxtxPDkCc0xLoY/xP/dgeJGWgGbvb8FCMMCdYo0gGCkhMzfFzxFPcTE6FAPK1UCXqrUgs7ZAsrkxtt7/C7aO9ujesROUTkUgd7CDzNKMA/6IiEhrSqUSjo6OAICvv/4a+/fvx5EjR9C3b1/4+vpi2rRpGvOvX78eHh4eqFSpEkRRxODBg1GhQgXs27cPMtk/x2RcXV1RtmxZVK9eHUuXLsW3336rXt7c3Fy9veLFi6Nhw4ZwcnLCzJkz0bVrV5QrVy7XPmuBKEYqlQrjxo1DvXr1ULly5UznCwkJgYODg8Y0BwcHhISEZLqMj48P5syZo7OsmREU/9zK3BAC5LL/P5CXJP5zpMgQAsSEJIgJSVC9j8aT6Df4Kzkcre4/RmzAP6XwVVoivn1/E0rI0GjLRXUZWpDwAqdS3mNkuZoYUL8pDJztIRQtgssRb1G2dg2U8awJmaFhrn9GIiL6f6IoQoxPlGTbgolRjv6D2djYGBERERg8eDCWLFmCCxcuoGHDhgCA2NhY7NmzB0uXLgUA+Pn54dGjR9i2bZu6FH1SrVo1NGvWDNu3b9coRhkZO3Ys5s2bhz/++AOTJ0/WOvt/KRDFaOTIkXjw4AEuXbqk83VPnTpV4yhTdHQ0XFxcdL4dpy2LcBiLNKaJKhWKJyQiPDwCSTFxKGJhCTEhEarYeHx3+w56Bwaiop0jbI3NkfY+BtEvX6DdiTgIKalQuJZAWlgkVB9i8DYlAa+S45D0/DVig08AAF6nJaL9+5swhIAHDg2gdHGCopQLziIG70zk8GrVEhWbNoTc1krnn5WIiAAxPhGBJVpIsm23FycgmBpnezlRFHH69GkcP34co0ePRsWKFVGnTh2sX79eXYx27doFURTRs2dPAMDTp08BABUqVMhwnRUqVMjS97eNjQ3s7e3x4sWLbOfODr0vRqNGjcKhQ4dw4cIFFCtW7LPzOjo6IjQ0VGNaaGio+nBdRpRKJZRKpU6yZpcgk8HQ1AS2pulvCFa3RiXU/Z9ptgAOQvP8qyoxCb/ff4yA234oJjeCTUIaUl+HIeX+PZQ69xSKVBXkaSqkvniD1BdvsCH6EU4lR2LmrmPoZ+wMub0NYks547d3T+Feowb6fT0MinIlIBjo/a8OERFl0aFDh2BmZoaUlBSoVCr07t0bs2fPBgAMGjQI48ePx4oVK2Bubo7169ejW7duMDc311iHKIqZrl+hUGQphyiKuT40RG+/3URRxOjRo7F//36cO3cObm5u/7mMp6cnTp8+jXHjxqmnnTx5Ep6enrmYVFoyIyVca7nDtZa7xvRmAAKwAaq0NKhCI5AS+AbJAUHw3LgOaY/vo5p5ceB9KtLCInHn9XP8Gv0ArjeuwGvnVQhGCigqlcZ+WTRkxezR9qtecK1fG4JMb8fyExHlOcHECG4vTki27exo0qQJfvvtNygUCjg7O8PgX/9x3LNnT4wfPx67du1Cw4YNcfnyZfj4+KjfL1OmDADg8ePHqF69erp1P378GGXLlv3PDBEREXj37l2Wvu9zQm+L0ciRI7Ft2zb88ccfMDc3V48TsrS0VD8vpV+/fihatKj6L2js2LFo1KgRfvrpJ7Rt2xY7duzAzZs3sXr1ask+h9RkcjlkzvYwcLaHcb3qmNu/g/o9VWw8kv0DUfr4aQzeuxvmsYmQpZhCFROHpFuPsPz9LTxLS4DhjpNoXsQVyi8qILKMMwIsDdG4bw9YOWd+JI6IqLATBEGr01lSMDU1RenSpTN8z9zcHN26dcP69evx7NkzlC1bFg0aNFC/X716dZQvXx5Lly5Fz549NcYZ3b17F6dOncrS1eHLly+HTCZDx44dc/x5PkvUUwAyfG3YsEE9T6NGjcT+/ftrLLdr1y6xbNmyokKhECtVqiQePnw4W9uNiooSAYhRUVE6+BT6R5WWJiYFvBSj9pwQR9dtLtaxdRZvODcQA+zqiwF29cV5pqVEAKKnwkp81WyI+G76z2Ls4fNi9JsQqaMTEUkmISFBfPTokZiQkCB1lGzr37+/2KFDh8/Oc/HiRRGAaG1tLS5atCjd+5cuXRJNTEzEjh07itevXxdfvnwp7tq1S3RxcRFbtWolpqamqud1dXUV586dKwYHB4tBQUHi+fPnxaFDh4qCIGS47n/73H7O6ve3IIqfOelH6URHR8PS0hJRUVGwsLCQOk6+IKakIvnxcyTefIi1m3yx/OppdJDbYJypKwAgVRRRO/I6XM2ssH34BLi18YKRR1XIsnkol4hIXyUmJiIwMBBubm4wMtKvf/sGDBiADx8+fPbWNgBQvnx5BAQE4NWrV3Byckr3/v379zFnzhycPXsWkZGRAP4ZJ7x06VKNU3MlSpTAy5cvAfwz9sjR0RF16tTBiBEj0KRJk89m+Nx+zur3N4tRNrEYZU3iyzdIufkIidfu4q8TZ9D23lFYCga4YeMBuSBAUCpwpKgCUS5F0O2bYSjTpB7vtUREBZY+F6PcoFKpMHjwYBw/fhznz59Xj0PKKRYjCbAYaeeV3wM8OHAENcJTEH/xNtLehqHrh7vwS43BQrPS6F26Gkya14WhV20oPavC2JL7logKDhaj9FQqlfpKtkGDBulknbooRno7+Jr0i4t7Zbi4/3PzTVEUkfL3S/SaMQdW58+iAeyR+joU0Rv248SqNZgY+zd6lauOpbPnwaRFXcitWZKIiAoamUyGsWPHSh0jHRYjynOCIEBRtgQm79yAyQBU8YlIuHQb8Seu4Ibvb4gX05D24i3CRi0ADOQwrv8FDlmr0HrUULhUrSR1fCIiKsB4Ki2beCotd6lUKtzYdQCGt/1hf/0Jkh89x7PUeLT8cBsKCLjXZigcu7aCWbvGkNtZSx2XiChLeCotb/BUGhU4MpkMdXp2Bv65kzySn73Cs5XrUX39a1jFp8DgxiOE33iE8KnLsbZIKop51sBXs6fCkvdMIiIiHeCtiilfU5RyQYsls3D7Qyj+fOwHm1lfQ1mtHOJSk7HswVWMWvMzzlZvh9ChsxB34grElFSpIxMRkR5jMSK9YVqqOKxH9UaxU2vhdGoNJrTqiObWxVA5TYnYA2cQ0udbzCxWDcNrN8b9wyeljktERHqIp9JILxVxr4T5R/dCFEUk33uKmF3HEbPvJDY9vYmgsEco2zMY1h4NYN67Lcw6N4Pcyvy/V0pERIUejxiRXhMEAcpq5WC3YAyK392LJbPnoVPJSmhp5oCku/4I/3YJ1pauh27lquPC+q2ffbozERERixEVGHKFAl1mTMS+Zw9Q4cFB2M4fA0WlUtgUHYQ9T/2wc9RUvGrYHx9W70FaVIzUcYmIKB9iMaICSW5rBavh3VDs7Ab4rPkNPSvUQA8rF6Q8CUTEd8txonwL9K/miVsHjkgdlYgoXxswYAAEQUj3atWqVZ5sf/bs2XB3d8+TbQEcY0QFnCAIaNi/Fxr274W0qBjE7jmJKN8D2HzzJLaHhCCsRz+sbdkTloO7wLRtAwgG/L8EEdH/atWqFTZs2KAxTalUSpQmd/GIERUacktzWA7uDJcLGzH4pwVoV6IC+pkWQ+LVuwgdMhMPvuiC+Z2/QsTLV1JHJSLKV5RKJRwdHTVe1tbWOHfuHBQKBS5evKied/HixbC3t0doaCgA4NixY6hfvz6srKxga2uLL7/8Es+ePdNY/+vXr9GrVy/Y2NjA1NQUNWvWxPXr1+Hr64s5c+bg7t276iNVvr6+ufpZWYyo0BEEAU2/GYSDgY/Q7cEJWHv3h8zOCtue3cOM/VvRrGwVhE9dhpTAN1JHJaJCIC4uDnFxcRoXhyQnJyMuLg5JSUkZzqtSqdTTUlJSEBcXh8TExCzNq0uNGzfGuHHj0LdvX0RFReHOnTuYMWMG1q5dCwcHB3UOb29v3Lx5E6dPn4ZMJkOnTp3UuWJjY9GoUSO8efMGBw8exN27dzF58mSoVCr06NEDEyZMQKVKlRAcHIzg4GD06NFDp58hHZGyJSoqSgQgRkVFSR2FdCgtIVHcNGaKWNbESlxgVloMsKsvBtg3FN8MnC5e3bZH6nhEpOcSEhLER48eiQkJCeneAyACEMPCwtTT5s+fLwIQhwwZojGviYmJCEAMDAxUT1u6dKkIQOzdu7fGvHZ2diIA8cGDB+ppq1evznb2/v37i3K5XDQ1NdV4LViwQBRFUUxKShLd3d3F7t27ixUrVhSHDh362fW9e/dOBCDev39fFEVR/P3330Vzc3MxIiIiw/lnzZolVqtWLUtZP7efs/r9zSNGRABkRkr0Xe6DxzERGP3HFhh7eQAqFQ7v3QfP3l3RyqkkYk9e5eX+RFQoNWnSBH5+fhqvESNGAAAUCgW2bt2KvXv3IjExEUuXLtVY9u+//0avXr1QsmRJWFhYoESJEgCAoKAgAICfnx+qV68OGxubPP1MmeFIU6J/kclkMPfygLmXB5IePUPQkJEwuPoYxaOSEdp7MhSVy8B6XF+YtG0AGQdqE5EOxMbGAgBMTEzU0yZNmoRx48bB4H/+nQkLCwMAGBsbq6eNHDkSQ4cOhVwu15j3xYsX6eYdMGCAVhlNTU1RunTpTN+/cuUKACAyMhKRkZEwNTVVv9euXTu4urpizZo1cHZ2hkqlQuXKlZGcnJwuX37AI0ZEmVBWLIV5V47h7xu34P31SAgmxkh+8DeuD5yE8hZ2+G3EeKiSdXu+nogKH1NTU5iamkIQBPU0hUIBU1PTdFd+fZpXJvv/r29DQ0OYmpqme5p8ZvPq2rNnzzB+/HisWbMGHh4e6N+/v3r8UEREBPz9/TF9+nQ0bdoUFSpUwPv37zWWr1q1Kvz8/BAZGZnh+hUKBdLS0nSeOzMsRkT/oUSt6ij/01S43tkN60kD4at6h78TorB7/Ua88uyD6M0HIbIgEVEBlpSUhJCQEI1XeHg40tLS8NVXX6Fly5YYOHAgNmzYgHv37uGnn34CAFhbW8PW1harV69GQEAAzpw5A29vb4119+rVC46OjujYsSMuX76M58+fY+/evbh69SoAoESJEggMDISfnx/Cw8PTDUjXNRYjoiyS21jCZvIg/PL4BuZ07IXxRasgNSgY77x/wJOa3bBi0CgkxcZJHZOISOeOHTsGJycnjVf9+vWxYMECvHz5Er///jsAwMnJCatXr8b06dNx9+5dyGQy7NixA7du3ULlypUxfvx4/PDDDxrrVigUOHHiBOzt7dGmTRtUqVIFixYtUp8a7NKlC1q1aoUmTZqgSJEi2L59e65+VkHkaNJsiY6OhqWlJaKiomBhYSF1HJKQKj4R0ZsO4sOKrfj9xT0sin8BD1M7nPhtPcx7toZgyDFIRPSPxMREBAYGws3NLd0pL9Kdz+3nrH5/84gRkZZkJkawGtEdxW/uQrGubWBvYITOgjXeeS9GUN2vEL3zKFI/Di4kIiL9wGJElEMyYyVGblyJ56HBGLRoDuRFrJH64g32DpuEClZFsOO7+bzMn4hIT7AYEemIqY0V7Eb2QvG/dsJmxghsSAlFQEI0ji/5DW9aDkf8xVtSRyQiov/AYkSkYzJTY1iP6YMD/n6Y3LQdvrEtg6Q7jxHceRxufTkM13bskzoiERFlgsWIKJfYuBTF96cOovrtfbAc0gUwNMCiUwfh2asLpns0R0pQsNQRiSiP8bR67tLF/mUxIsplBvY2sPMZh2KXNgOujhAAuPuHIcizD8Jnr0TahxipIxJRLvt0Y8X4+HiJkxRsn/ZvTm5kycv1s4mX61NOPT58Cpbr/kTCxdsAgAPyaKQ1rolx63+BwiR/3RqfiHQnODgYHz58gL29PUxMTDTudE05I4oi4uPjERYWBisrKzg5OaWbJ6vf3yxG2cRiRLogiiLiT13DyxnL0eDGPkSKKVhc0gPf/LYEJs09+Q8mUQEkiiJCQkLw4cMHqaMUWFZWVnB0dMzw31AWo1zCYkS6lJKYiF+Gj8f23buwyaQ8DAUZjBvXgvXsb2BcKfMHNhKR/kpLS0NKCh8jpGuGhobpHqT7byxGuYTFiHJDalQMopZtxoffd0NMTkHf6IeoWq0qFu7ZAltXF6njERHpPd75mkiPGFiaw3bWNyh+eQse1iqJaykfsOnmJTz26v/PQ2rz8MnSRESFGY8YZROPGFFe+POnlXj022Z0jfrnygpF1bJInfgV3Fo3kTgZEZF+4hEjIj3WbsJITH58Cbbzx0BmYYand+6iQptm6FOpFuLfhEgdj4iowGIxIsqnBEMDWA3vBpdr23CtihOSoELQ3wEIaTQQURv/4Ok1IqJcwFNp2cRTaSSVM6s3wmDtQRQNDAMACNXKInJ4B9Ts1l7iZERE+R9PpREVMF7D+qPBlZ2wXTAWMnNT/Hr1NDy6d8DURm2hiuXddImIdIHFiEiPCAYGsBrWFS5XtuBNMWuoANjeDkBQvb6IPXyBz2EiIsohFiMiPWTgaIft/ndw5uc16FquGtLehiF0wHe40GEYXt6+J3U8IiK9xWJEpMeajB6C4hc3w2p8P6QYyDD8yDZUrlkDByfNgahSSR2PiEjvsBgR6TmZsRK204bCaOf3MDEzgyEAp/VH8bbdKCT7B0odj4hIr7AYERUQJRrWwY2wVzgx9yfYmlsg8cZ9vGoyCPuHT0ZyfILU8YiI9AKLEVEBYqBQoOb0MSh+eTNMWtTFnfhIdFn9A75wKIaIq7eljkdElO+xGBEVQAZFHeC4ZRFUX3eFpcwQZVMM8KGTNyLm/w5VYpLU8YiI8i3e4DGbeINH0jdvHvojZtE6GB67CgBIKVUU78d0g2fvLhInIyLKO7zBIxEBAIpWKofymxfDwXcB5EVssPDuBTTo0xUL2naHmJQsdTwionyFxYiokDBr2xBFL/gitpgd0gAUvfQIr5sPRdJdf6mjERHlG3pdjC5cuIB27drB2dkZgiDgwIEDn53/3LlzEAQh3SskhE8rp8LB0M4a+/6+hwtLf0cjZzckP36O162G49aU73nlGhER9LwYxcXFoVq1ali5cmW2lvP390dwcLD6ZW9vn0sJifKnBuOGofjFTTBt3wQxyUlo/8Ms1HZyxbMLV6WORkQkKQOpA+RE69at0bp162wvZ29vDysrK90HItIjcjtrOKydgwelbBE74yYMYmMQ32sKPswcBcuhXSDI9Pq/m4iItFIo/+Vzd3eHk5MTmjdvjsuXL3923qSkJERHR2u8iAoKQRDQbNpY3L1xE2u8usAkWYWI6T8juOt4RDz+W+p4RER5rlAVIycnJ6xatQp79+7F3r174eLigsaNG+P27cxvfOfj4wNLS0v1y8XFJQ8TE+WNEjWqofmJzbBb7A3BxAiHTp1E6cqVsGXyLKmjERHlqQJzHyNBELB//3507NgxW8s1atQIxYsXx+bNmzN8PykpCUlJ/39DvOjoaLi4uPA+RlRgJT8LQus69XEm/BUGGxfFgoEjYOczDnILM6mjERFpjfcxyqLatWsjICAg0/eVSiUsLCw0XkQFmaJUcRx96Y8FbXvA28wNsbuO43XjgYi/dlfqaEREua7QFyM/Pz84OTlJHYMoX1GYGGPaoR1w+3MlDFydkBIUjF6NW2Bqi05ISUyUOh4RUa7R62IUGxsLPz8/+Pn5AQACAwPh5+eHoKAgAMDUqVPRr18/9fzLli3DH3/8gYCAADx48ADjxo3DmTNnMHLkSCniE+V7xh5V4XJ2A+7XL4eDSWH48eQfONe8H1KCgqWORkSUK/T6cv2bN2+iSZMm6p+9vb0BAP3794evry+Cg4PVJQkAkpOTMWHCBLx58wYmJiaoWrUqTp06pbEOItIkMzdFpwPrsHqMHYK2/IGST4LxuvFAFPlpEsw6NZU6HhGRThWYwdd5hQ+RpcIs5eVbhH49D0l/PcA7VTI2lVBi8fEDMLOzkToaEdFncfA1Eemcoaszih5cAavx/TAp5il+u30RvSt8gaRHz6SORkSkEyxGRJQtgoEBbKcNxbffL4CroQlGptriTcthiNr4B3gAmoj0HYsREWnly/Hf4MnLF6ja0gtiYjLCJ/6Ig+0G4cMbDswmIv3FYkREWjNyKgLHbd/DdvY3eIEk9DmyGdVLlUXAifNSRyMi0gqLERHliCCTwWpkLxj9NAGWBgrYp8mQNmAWotbt46k1ItI7LEZEpBP1+vXAncePsLp9XxikpCF8ylKEDJ6J2NB3UkcjIsoyFiMi0hn7UiVQdc8K2M4bBRjIsXLXVriXKIU7B49KHY2IKEtYjIhIpwRBgNWIHrDd/RM2JofiWWIM/uw/HtE7WI6IKP9jMSKiXGFVvwb+un8X31VpgN5yO7wbvRBh4xZBlZAkdTQiokyxGBFRrnEsVxrz/M7BZuoQQBAQueVPDCjtjr8vXZc6GhFRhliMiChXCTIZbCYMgNPuJfhFDMPmt0/QtEkTfDh6UepoRETpsBgRUZ4waVQT3kd3wN2iCKYZl0BEv2mI/H4dxLQ0qaMREamxGBFRnilZuwZuhgShy4ghAID3P/riavvhCA98KXEyIqJ/sBgRUZ6SGxuhyGJv2K/8DrFKOfoe34aaFSrj3sHjUkcjIoKB1AGIqHAy794KMiMByd06IzUlGYlfL0BMigzmXZpLHY2ICjEeMSIiyVRp3xK3Hj3E1mY9YZssImzEXIRP/xmq5BSpoxFRIcViRESSsi9VAk2OrIfVuL4AgBO/rIWXS2mE/f1c4mREVBixGBGR5AS5HLbfDYPN2jmYEheA82FBmFavFZLu/y11NCIqZFiMiCjfsO7ghT/27kMbaxeMU9nhTduvEbP/lNSxiKgQYTEionylevtWOPj8IWya1oWYkISwYXOws+8opKVw3BER5T4WIyLKd+RW5nDa9j2sxvTB/sRQ9NyyEq1LVEDy+yipoxFRAcdiRET5kiCXw3bGCNgM6QoFZCj7PhkhbUci+VmQ1NGIqABjMSKifG3Q0oX4a/8heJeuiZS/X+JNy+GIO8OH0BJR7mAxIqJ8r2rH1ih+cg2UtSoj9UMMurX5Eot7DoJKpZI6GhEVMCxGRKQXDBxsUXT/clyq7YqjSeGYvtMXNwdPgcibQRKRDrEYEZHeEJQK9D+0GXM69sYC8zKwPXQVb7t5Iy3ig9TRiKiAYDEiIr0ik8kwc/9WfLNnAwQzEyRe8cMtr364d5T3OyKinGMxIiK9ZNqiLoodXYUUlyIY8ugs6n/ZGieWrZI6FhHpORYjItJbivJusNn5I4zMzWEgAgbzNyBq/X6pYxGRHmMxIiK9Zl+mJC688MeBHl/DVVAg/NslCJ+6DGJqqtTRiEgP6aQYRUZG8rJZIpKMkYU5GmxbDpvpwwEAV1dtRIeSlRD1NkTiZESkb7QuRo8ePcKiRYtQt25dFClSBPb29ujXrx/27t2LuLg4XWYkIvpPgiDAeuxXsF07G2Njn+LPV08xpmZjpLwOlToaEemRbBUjf39/TJgwAWXKlEGdOnXw119/YcSIEQgNDcWRI0fg6uqKuXPnws7ODq1bt8Zvv/2WW7mJiDJk1aEpNm3whaeJHcYmWeNNy2FI9HsidSwi0hOCKIpiVmfesGEDrl+/jg4dOqBp06ZQKBQZzvfixQv88ccf+PPPP3HqVMG6hDY6OhqWlpaIioqChYWF1HGIKBMpr0MR0mcykh89h2CsROKcYag4sLvUsYhIIln9/s5WMSIWIyJ9ooqJQ8iQWTh09AhGxjzBvK598e0uXwiCIHU0IspjWf3+1sng64SEBLx58ybd9IcPH+pi9UREWpGZm8Jp6yLcLlsEqRBx58/jeDd5Ca9YI6JM5bgY7dmzB2XKlEHbtm1RtWpVXL/+/0+97tu3b05XT0SUI4KBAX69cRbrBo3BHPPSiPE9gJD+30EVlyB1NCLKh3JcjObPn49bt27Bz88PGzZswODBg7Ft2zYAAM/SEVF+IJPJMGjdchRbPx+CkQJxxy9jdvXGePPgsdTRiCifMcjpClJSUuDg4AAAqFGjBi5cuIBOnTohICCA5/GJKF8xa9cYBo52WPRlD8z7+xE21qiFu9dvwMq9otTRiCifyPERI3t7e9y7d0/9s42NDU6ePInHjx9rTCciyg+MalVG9+2r4Ko0w1eG9njfdQISrvhJHYuI8okcX5X2+vVrGBgYwNHRMd17ly9fRr169XKy+nyHV6URFQxRQW8QO2I+Ev96ACgMUeTnqbDo0lzqWESUS3LtqrRevXrhwYMH6p+LFSuWYSkCUOBKEREVHJbFi8Jp7zKYtm2ElKQktOvVAwu79OXYSKJCLtvFaOfOnWjatKlGOfo3URQRGxub42BERLlNZqyEw7o5OF+vFC6kvMf8fdtwd+RsiGlpUkcjIoloNcbI3d0dXl5eGZajsLAwWFlZ5TQXEVGeEORyDN3vi7md+uBni/Iw330GoUNmQZWYJHU0IpJAtouRIAjw9fWFl5cXvLy8cP/+/XTzqFQqnYQjIsoLMpkMM/ZtQU/fnwGFIeIOncetL4ch4uUrqaMRUR7LdjESRRFyuRzbtm1D06ZNMyxHvEyfiPSRWaemcN75I6JNDNHr3G7Ur1gVL/7ykzoWEeUhrS/Xl8lk2Lp1K5o1awYvLy9emk9EBYJx/S8grJiMGEHEh8R4BPedgqRHz6SORUR5RKtTaeqFP5aj5s2bo2nTpixHRFQguLdvhUsXLmBztRawj4jD23ajkHD1rtSxiCgPaHUqTWMFMhm2bNmiLkd+fn66ykZEJJkynrXQ5PRWGNWuAlV0LA63H4zds3ykjkVEuSzbxejw4cOwtLTUXMnHctSiRQt06dJFZ+H+y4ULF9CuXTs4OztDEAQcOHDgP5c5d+4cvvjiCyiVSpQuXRq+vr65npOI9JPc2gJOe5YivH5lDI24i55zp2HvhFlSxyKiXJTtYtS6dWsolcr0K5LJsHnzZnTo0EEnwbIiLi4O1apVw8qVK7M0f2BgINq2bYsmTZrAz88P48aNw5AhQ3D8+PFcTkpE+kpmrMQX25eiVYVqqGJgjrIbTyLyxw28ESRRAZXjR4L8L1EU8ddff6F27dq4fv06PDw8dLn6TAmCgP3796Njx46ZzvPtt9/i8OHDGvdf6tmzJz58+IBjx45laTt8JAhR4aRSqfBq7q9IXbkTAGAxqDNsF46BTC6XOBkRZUWuPRLkvwiCgNq1awMAunXrpuvV58jVq1fRrFkzjWktW7bE1atXM10mKSkJ0dHRGi8iKnxkMhlcZ4+Cnc84QBCw6ddV6FbOHUmxcVJHIyIdMsjpCrp3757hdFEUERkZmdPV61RISAgcHBw0pjk4OCA6OhoJCQkwNjZOt4yPjw/mzJmTVxGJKJ+zHNIFEXIVZgzuiYRnKixr1AGTzh+EzMxE6mhEpAM5LkanTp3C5s2bYWZmpjFdFEVcuHAhp6uX3NSpU+Ht7a3+OTo6Gi4uLhImIiKplRzYDdvfBWPHvMXo/DIRbzuPg9P2xZDbWkkdjYhyKMfFqHHjxjA3N0fDhg3TvVe1atWcrl6nHB0dERoaqjEtNDQUFhYWGR4tAgClUpnhYHMiKtw6TB6Dls2bI7jXJCTdeYzXX46E0ZrpcKhcQepoRJQDOR5jtG/fvgxLEQCcPHkyp6vXKU9PT5w+fVpj2smTJ+Hp6SlRIiLSZ0bVK6DooV9hUNQeC+9eQI0vauDBibNSxyKiHMh2MerVq5fGVV1Sio2NhZ+fn/qmkoGBgfDz80NQUBCAf06D9evXTz3/iBEj8Pz5c0yePBlPnjzBr7/+il27dmH8+PFSxCeiAkBRujjMd3yPU2IU3qQk4Fj/8Uj0eyJ1LCLSUraL0c6dO9G0adNMy5EoioiNjc1xsKy4efMmqlevjurVqwMAvL29Ub16dcycORMAEBwcrC5JAODm5obDhw/j5MmTqFatGn766SesXbsWLVu2zJO8RFQw2ZQvjUt3bmFJpUbolGqOtx3HIP7iLaljEZEWsn0fI5lMhubNm+POnTs4c+YMKleurPF+aGgoihYtitTUVJ0GzS94HyMiyowqNh4h/ach4cItJCvkCBrbBS0nj5Y6FhEhF+9jJAgCfH194eXlBS8vL9y/fz/dPCqVKrurJSLSezIzEzhtWwxF63oYFX4fbb8dg/Vjp0gdi4iyQauHyMrlcmzbtg1NmzbNsBwJgqCzgERE+kRQKuC4ejZs3YrDADIYbzyCqDV7pI5FRFmk9VVpMpkMW7duRbNmzeDl5YV79+7pMhcRkd4yNDLC9gd/4cSQSahtaInwacsR+ZMvn69GpAe0OpWmXvhjOWrevDmaNm3KckRE9JHcwAANVvvAevIgAID/wt8wq1lHDjUgyue0OpWmsQKZDFu2bFGXo0+XzhMRFXaCIMBm0kCYz/4GA6IeYN6Zg5hctwXEtDSpoxFRJrJdjA4fPgxLS0vNlXwsRy1atECXLl10Fo6IqCCwH9kLowYOhqNMgbZPoxA2Yi7ElIJ55S6Rvsv25fqfo1Kp0LdvX2zfvr3AHi7m5fpEpK3QnUcQO/4HICUVJs094bBuHmTGfOQQUV7Ilcv1Q0JCkJSUlPnKPh45unbtGgDg+fPn2Vk9EVGB5tCjDRw3+UAwUuDG0ZNoW6oiokPCpI5FRP+SrWK0Z88e2NjYoFOnTtiwYQPevXuXbp4bN27gwIEDqFSpEqpVq6azoEREBYFpszqw27oIY2L9cSz4OcbXaYq0DzFSxyKij7JVjEaNGoW7d++iQYMG8PX1RbFixVC/fn0sXLgQQ4cOhZOTEzp27IiwsDAsWrQow+JERFTYWTSshS3rN6ChiR1Gx5jhbaexSAt/L3UsIkIOxxhFRETg0KFDOHLkCEqUKIEOHTrA09OzQN/gkWOMiEhXkh49Q3DX8Uh79x6GZVxht/17mLgWlToWUYGU1e9vnQ6+LgxYjIhIl5KfBeFt5/E4GPgQy5Lf4Pjp0yhTr7bUsYgKnFx7VhoREemOolRx2O9biuUpbxGYFIulnfoi+dkrqWMRFVosRkREEjMpVRxnrlzGyKKVME5VBG/bj0Lyk0CpYxEVSixGRET5gGv1Klh25yKMK5dBWlgkXrcfhecnL0odi6jQYTEiIsonDIpYw3n/ciirV8DS1/dRvZUXLvhulzoWUaHCYkRElI/IrS1gt/17XDdKQbQqFWcnzEXC1btSxyIqNAxysvDp06dx+vRphIWFpXsEyPr163MUjIiosDKytcbpJ/ex5cu+aBEYi+CeE+G42QcmDWtKHY2owNP6iNGcOXPQokULnD59GuHh4Xj//r3Gi4iItGdub4fh5w/A2MsDYnwignpNwrkV66SORVTgaX0fIycnJyxevBh9+/bVdaZ8jfcxIqK8JCYl49Wg6Ri6Zz3OJkdiy8wF6D5nqtSxiPROrt/HKDk5GXXr1tV2cSIiygJBqYDjmjlQOttDAJCwYjti/zgrdSyiAkvrYjRkyBBs27ZNl1mIiCgDChNj7H18G392HY76ckuEDpuNmL0npY5FVCBpPfg6MTERq1evxqlTp1C1alUYGhpqvL9kyZIchyMion8YGhmh+Y5f8G78YsRsP4LHI2bi7ysX0OeneVJHIypQtC5G9+7dg7u7OwDgwYMHGu8V5IfIEhFJRZDLUWTZt4hVpaL/qkXwX3IN8fHxGPrbT1JHIyowtC5GZ8/yHDcRUV4TZDK4Lp+GulePI+LODZTYcQ5RtffDcmAnqaMRFQi8wSMRkZ6Ry+VY89cFnBkzEyUNTBA+eQmi1uyROhZRgZCjGzx++PAB69atw+PHjwEAFStWxODBg2FpaamTcERElDGZTIZKS79DpI0tPqzYhsuTF+LxmWOYsH2t1NGI9JrWR4xu3ryJUqVKYenSpYiMjERkZCSWLl2KUqVK4fbt27rMSEREGRAEATYzRiB5SAf0jXqAiTvWYVX/kVLHItJrWt/gsUGDBihdujTWrFkDA4N/DjylpqZiyJAheP78OS5cuKDToPkFb/BIRPmNSqXCt8074Oj5s9hkWQVus0bBekwfqWMR5StZ/f7WuhgZGxvjzp07KF++vMb0R48eoWbNmoiPj9dmtfkeixER5VdvfH5H4pItAACbaUNhPb6fxImI8o9cv/O1hYUFgoKC0k1/9eoVzM3NtV0tERFpqejU4bCZOhQAsHPWIsxo01XiRET6R+ti1KNHDwwePBg7d+7Eq1ev8OrVK+zYsQNDhgxBr169dJmRiIiyyNq7H6KGd8CYmCeYf3QvNvYfJXUkIr2i9VVpP/74IwRBQL9+/ZCamgoAMDQ0xNdff41FixbpLCAREWXPF/MnYtq9m7h1/DTqHPZD5OL1sJk8SOpYRHpB6zFGn8THx+PZs2cAgFKlSsHExEQnwfIrjjEiIn0RuWIr3s9dBQCwntAf1t8O5pMJqNDK9cHXhRWLERHpkw+/7kDErJX4Nf4VBI/KWHzqIGQy3tuXCp+sfn9n61Sat7c35s2bB1NTU3h7e392Xj5ElohIelbf9MTtoOdY8v13wNmXaDTiW3z5+2IeOSLKRLaK0Z07d5CSkqL+c2b4fzgiovzDa9E0LHj2BBFHLqDi/mt4X3IdT6sRZULrU2lBQUEoVqxYukOyoiji1atXKF68uE4C5jc8lUZE+urDqp2ImPELAMBifF/YThnC02pUaOT6fYzc3NwQHh6ebnpkZCTc3Ny0XS0REeUSqxE9YDtvFFJFEYPmfIdJTdtBpVJJHYsoX9G6GGV2oCk2NhZGRkZaByIiotxjNaIH7vZsgEPJ4Vhx7iiuT1ogdSSifCXb9zH6NOhaEATMnDlT4/L8tLQ0XL9+He7u7joLSEREutX9Fx88D3sLq2N/wX7TCUQ6FOV9jog+ynYx+jToWhRF3L9/HwqFQv2eQqFAtWrVMHHiRN0lJCIinZuya6P6Uv73P2xAYmoqnKcNkzoWkeS0Hnw9cOBALF++vNANQObgayIqSD6s3I7nM39G/+gHaOvVFIuOH5A6ElGuyPXB1xs2bGAxICLSc1Yje+FGG3c8SI3F6pOH8XTOL1JHIpKU1s9K++TRo0cICgpCcnKyxvT27dvndNVERJQHRmz+DdEJ8ahy+j7kv+zEeysrWI/9SupYRJLQ+lTa8+fP0alTJ9y/fx+CIKivUvt0w7C0tDTdpcxHeCqNiAqq90s2IdJnDQBAPqkfSkweKnEiIt3J9VNpY8eOhZubG8LCwmBiYoKHDx/iwoULqFmzJs6dO6ftaomISCLW3v1g/e0gPEuNh8fUUZjXmUeNqPDRuhhdvXoVc+fOhZ2dHWQyGWQyGerXrw8fHx+MGTNGlxmJiCiP2EwciL/qlUaYKhlbDu5H8IqtUkciylNaF6O0tDSYm5sDAOzs7PD27VsAgKurK/z9/XWTLgtWrlyJEiVKwMjICB4eHrhx40am8/r6+kIQBI0Xb0ZJRKRp2p878EP7PthiWQXxc1chat0+qSMR5Rmti1HlypVx9+5dAICHhwcWL16My5cvY+7cuShZsqTOAn7Ozp074e3tjVmzZuH27duoVq0aWrZsibCwsEyXsbCwQHBwsPr18uXLPMlKRKQvBEHAhAObUXLcAABA+JSleLJsg7ShiPKI1sVo+vTp6mfszJ07F4GBgWjQoAGOHDmC5cuX6yzg5yxZsgRDhw7FwIEDUbFiRaxatQomJiZYv359pssIggBHR0f1y8HBIU+yEhHpE0EQYDN9OCxH9sT55PeoPn4Ilg8aJXUsolyndTFq2bIlOnfuDAAoXbo0njx5gvDwcISFhaFp06Y6C5iZ5ORk3Lp1C82aNVNPk8lkaNasGa5evZrpcrGxsXB1dYWLiws6dOiAhw8ffnY7SUlJiI6O1ngRERUGgiDAdtY3uFvZCYlQ4fDWHYjadljqWES5Suti5OXlhTlz5mhMs7GxwYcPH+Dl5ZXjYP8lPDwcaWlp6Y74ODg4ICQkJMNlypUrh/Xr1+OPP/7Ali1boFKpULduXbx+/TrT7fj4+MDS0lL9cnFx0ennICLKzwRBwNKLx/Fzm55Ybl4O4eO+R8yeE1LHIso1Whejc+fO4ZdffkHHjh0RFxennp6cnIzz58/rJJyueXp6ol+/fnB3d0ejRo2wb98+FClSBL///numy0ydOhVRUVHq16tXr/IwMRGR9GQyGUYd2ga7gZ0AUUTYyAW4++smqWMR5QqtixEAnDp1CiEhIahTpw5evHiho0hZY2dnB7lcjtDQUI3poaGhcHR0zNI6DA0NUb16dQQEBGQ6j1KphIWFhcaLiKiwEQQBdt97w7xPW6yLe4UaI/tj85Q5/70gkZ7JUTFycnLC+fPnUaVKFdSqVStPb+yoUChQo0YNnD59Wj1NpVLh9OnT8PT0zNI60tLScP/+fTg5OeVWTCKiAkOQyWD30yQEulgjDcCNX9Yj7tglqWMR6ZTWxejToz+USiW2bduGsWPHolWrVvj11191Fu6/eHt7Y82aNdi4cSMeP36Mr7/+GnFxcRg4cCAAoF+/fpg6dap6/rlz5+LEiRN4/vw5bt++ja+++govX77EkCFD8iwzEZE+k8nl2Hz3Kta36ImxRi4IGTQDcaeuSR2LSGe0fojs/z5ibfr06ahQoQL69++f41BZ1aNHD7x79w4zZ85ESEgI3N3dcezYMfWA7KCgIMhk/9/93r9/j6FDhyIkJATW1taoUaMGrly5gooVK+ZZZiIifWegUGDA4c0IHT4XcQfPIrj/NETOH4baA3tKHY0ox7R+iOzLly/h4uKiUTwA4MGDB7h161aeFqS8xIfIEhH9Q0xJxdtB0zFm9wYcTg7HvqUr0WbsCKljEWUo1x8i6+rqmq4UAf/cEbugliIiIvp/gqEBivw2AzG25kgVVQiY9ysSrt+TOhZRjmTriJG3tzfmzZsHU1NTeHt7f3beJUuW5DhcfsQjRkREmhKionGkwxC4PwyGYGYC571LYfQFhyhQ/pLV7+9sjTG6c+cOUlJS1H/OzKeB2UREVPAZW1qg05FNCO49GYmX7+DvrmOR/MMY1OjSTupoRNmm9RijoKAgFCtWLN3pNFEU8erVKxQvXlwnAfMbHjEiIsqYKjYe/p1Ho8/Z3XiuSsTJPftRq1NbqWMRAciDMUZubm4IDw9PNz0yMhJubm7arpaIiPSUzMwEzr4LIDM1BkQRb72/R/LfL6WORZQtWhejzA40xcbGwsjISOtARESkvyydHXHi7i3sqfElKseKeNtpLFKeZ/48SqL8Jtv3Mfo06FoQBMycORMmJibq99LS0nD9+nW4u7vrLCAREekXG9diaHJsA950HIOUJ4G4/uUQFFk9G+Xq15E6GtF/ynYx+jToWhRF3L9/HwqFQv2eQqFAtWrVMHHiRN0lJCIivSO3tYLz3mW40nIAet47AVnTprhw6RJK1qoudTSiz8p2MTp79iwAYODAgVi+fDkHIBMRUYYM7G1Q0nchjDzOQpaahojhc1D86DoYONhKHY0oU1pflVZY8ao0IqLsCbxxGxGDZsI6NAqG5d1Q9MDPkNtaSR2LCplcvyoNAC5evIivvvoKnp6eePPmDQBg8+bNuHSJT1smIqJ/uNX+AtX+/B1yRzukPAnEoRZfIez5C6ljEWVI62K0d+9etGzZEsbGxrhz5w6SkpIAAFFRUVi4cKHOAhIRkf4zdCsK533LcMMkDb1uH0Uz91qIfPVG6lhE6WhdjObPn49Vq1ZhzZo1MDQ0VE+vV68ebt++rZNwRERUcCjKuKLCipkwkRvAOjENkcPnQRWXIHUsIg1aFyN/f380bNgw3XRLS0t8+PAhJ5mIiKiAcm/fCuf3HcTvxWpB+OshQvpPgyoxSepYRGpaFyNHR0cEBASkm37p0iWULFkyR6GIiKjgqtS+JUrs/AmCiTESzt/EtlZ9kBgTK3UsIgA5KEZDhw7F2LFjcf36dQiCgLdv32Lr1q2YOHEivv76a11mJCKiAsaoVmU4bfse21Pfoe/5vehY6QukJCZKHYso+/cx+mTKlClQqVRo2rQp4uPj0bBhQyiVSkycOBGjR4/WZUYiIiqAjOtVR/XvRkExaSyKvotDxLjv4bByOgS5XOpoVIjl+D5GycnJCAgIQGxsLCpWrAgzMzNdZcuXeB8jIiLdurN2G8yn/w4hTQXzr75EkSWTIQiC1LGogMnq9zdv8JhNLEZERLoXu/80QkfMhZiWhvMNymDg3nWQyXJ0qz0iDVn9/tb6VBoAnD59GqdPn0ZYWBhUKpXGe+vXr8/JqomIqBAx69QUqqRkfD1wELYduIxbDV5h5aWTPHJEeU7rOj5nzhy0aNECp0+fRnh4ON6/f6/xIiIiyg6Lnq3h2asL5ADc7gXh/ZKNUkeiQkjrI0arVq2Cr68v+vbtq8s8RERUiI1a/wvqlSgDixV78H7ROshMjGD1dU+pY1EhovURo+TkZNStW1eXWYiIiFB95lhYTxkMAHg942dsGT1F4kRUmGhdjIYMGYJt27bpMgsREREAwNq7P4y/7o5BUQ/R95fvsXL4eKkjUSGh9am0xMRErF69GqdOnULVqlU1npcGAEuWLMlxOCIiKpwEQYDj7JGofXw/Hv91Afa7zyK2zVmYdWgidTQq4LQuRvfu3YO7uzsA4MGDB7rKQ0REBACQyWT4+cpJDBgyDdaHriB0xBwIxgqYtqgndTQqwHgfo2zifYyIiPKWmJaGsG/mI3bfKYQYqPBhch+0Hf+N1LFIz+T6fYy8vb0znC4IAoyMjFC6dGl06NABNjY22m6CiIgIglwO+1++w5uId+i5fy3eTbiGP42N0XzEQKmjUQGk9RGjJk2a4Pbt20hLS0O5cuUAAE+fPoVcLkf58uXh7+8PQRBw6dIlVKxYUaehpcQjRkRE0kiOjcOXZaviaehbbClaG7X+XA1ltXJSxyI9kdXvb62vSuvQoQOaNWuGt2/f4tatW7h16xZev36N5s2bo1evXnjz5g0aNmyI8eN5JQEREeWcwswUBx7cwoFmveCUoMLbbt5Ievxc6lhUwGh9xKho0aI4efJkuqNBDx8+RIsWLfDmzRvcvn0bLVq0QHh4uE7C5gc8YkREJC1VbDzedhmHpNuPcc9MgNuvM+HeupnUsSify/UjRlFRUQgLC0s3/d27d4iOjgYAWFlZITk5WdtNEBERpSMzM4HTzp/w0MUSfV9eQYv2X+Lp5RtSx6ICIken0gYNGoT9+/fj9evXeP36Nfbv34/BgwejY8eOAIAbN26gbNmyuspKREQEAJBbmaPOjp/hYmyOsoIx0kYvRmpIwTk7QdLR+lRabGwsxo8fj02bNiE1NRUAYGBggP79+2Pp0qUwNTWFn58fAKjvd1QQ8FQaEVH+EfzgMaL7TYfBqzAYliuBon+sgNzWSupYlA9l9fs7x/cxio2NxfPn/wx+K1myJMzMzHKyunyPxYiIKH9JefkWb9qNQlrwO5x1VKDdH+vgULKE1LEon8mzYlTYsBgREeU/yQFBWNGwAyYF+6GyhR0u+T+EhaO91LEoH8n1Gzx+8ujRIwQFBaUbZN2+ffucrpqIiChLFKWLo/nvi2DTqT2qJRsi+uv5MNv+A2RGSqmjkZ7R+ojR8+fP0alTJ9y/fx+CIODTagRBAACkpaXpLmU+wiNGRET517NjZ4HhC4H4RJg094Sj7wIICsP/XpAKvFy/XH/s2LFwc3NDWFgYTExM8PDhQ1y4cAE1a9bEuXPntF0tERGR1kq1agLnbYshGCkQd+IKVjTriuTERKljkR7RuhhdvXoVc+fOhZ2dHWQyGWQyGerXrw8fHx+MGTNGlxmJiIiyzLhedTj6LoRP4kuMvXgQ3avUhqqAnsUg3dO6GKWlpcHc3BwAYGdnh7dv3wIAXF1d4e/vr5t0REREWjBp6oEvJ4+BEjLUCklAxLSfwWuNKCu0HnxduXJl3L17F25ubvDw8MDixYuhUCiwevVqlCxZUpcZiYiIsq3TzEm46+AMgxm/I3r9PshMjWEzY7h6LCxRRrQ+YjR9+nSoVCoAwNy5cxEYGIgGDRrgyJEj+Pnnn3UWkIiISFvlhveB3Q8TAAChP2/Gqj7DeeSIPkvrI0YtW7ZU/7l06dJ48uQJIiMjYW1tzTZORET5hmX/DkiNjUP78d/gyvYrCEmIxZz926SORflUju5jlJiYiHv37iEsLEx99OgT3seIiIjyC9uRvdHq2B+4e3gvyp19gOjNf8KibzupY1E+pHUxOnbsGPr27YuIiIh07wmCUGDvY0RERPpp+sEd6DRxIYw3HcO7CT9AMDWGeedmUseifEbrMUajR49G9+7dERwcDJVKpfFiKSIiovxGEARU+nEaLAZ0AEQRD4bPwO7Zi6SORfmM1keMQkND4e3tDQcHB13mISIiyjWCIMDue2+ERUSg58ZleDPnOuRKJTpPHS91NMontD5i1LVrV97hmoiI9I4gk6H8qjmoXaIMnGUK2K7Yg4Rr96SORfmE1s9Ki4+PR7du3VCkSBFUqVIFhoaaz6LJq7tfr1y5Ej/88ANCQkJQrVo1rFixArVr1850/t27d2PGjBl48eIFypQpg++//x5t2rTJ8vb4rDQiooIhJT4Bj3tPgOnl+xDMTOC8fzmM3MtLHYtySVa/v7UuRuvWrcOIESNgZGQEW1tbjUv0BUHA8+fPtVlttuzcuRP9+vXDqlWr4OHhgWXLlmH37t3w9/eHvb19uvmvXLmChg0bwsfHB19++SW2bduG77//Hrdv30blypWztE0WIyKigkOVkITgnhOReMUPD41F2P00EXW7dZQ6FuWCXC9Gjo6OGDNmDKZMmQKZTOszcjni4eGBWrVq4ZdffgEAqFQquLi4YPTo0ZgyZUq6+Xv06IG4uDgcOnRIPa1OnTpwd3fHqlWrsrRNFiMiooJFFRuP482/QvfrB2Egk+Hc4WOo1tJL6likY1n9/ta60SQnJ6NHjx6SlaLk5GTcunULzZr9/6WWMpkMzZo1w9WrVzNc5urVqxrzA//cqDKz+QEgKSkJ0dHRGi8iIio4ZGYm8Ny5AmXMrFFWMIbBxGVIeR0qdSySiNatpn///ti5c6cus2RLeHg40tLS0l0V5+DggJCQkAyXCQkJydb8AODj4wNLS0v1y8XFJefhiYgoX7EqXhTHb17HJveWMAp5j+DO45AaEi51LJKA1pfrp6WlYfHixTh+/DiqVq2abvD1kiVLchwuP5g6dSq8vb3VP0dHR7McEREVQEXKloT1gRV4024UUgJfw9erG1rtWYViFctJHY3ykNbF6P79+6hevToA4MGDBxrv5cWz0uzs7CCXyxEaqnm4MzQ0FI6Ojhku4+jomK35AUCpVEKpVOY8MBER5XsGRR3gvHcZfm/YEWMf30Tp2nVw5dF92BYvJnU0yiNaF6OzZ8/qMke2KRQK1KhRA6dPn0bHjh0B/DP4+vTp0xg1alSGy3h6euL06dMYN26cetrJkyfh6emZB4mJiEgfGLoVRbMNP6JI65b4Is0I8V8vhPWunyAzNZY6GuUBaUZO64i3tzfWrFmDjRs34vHjx/j6668RFxeHgQMHAgD69euHqVOnqucfO3Ysjh07hp9++glPnjzB7NmzcfPmzUyLFBERFU7lmzXCtWOnsMCpGpJv3EfIgO8gJiVLHYvygF4Xox49euDHH3/EzJkz4e7uDj8/Pxw7dkw9wDooKAjBwcHq+evWrYtt27Zh9erVqFatGvbs2YMDBw5k+R5GRERUeJRoWh/OO3+EYGKM+LM3sNyrExJiYqWORblM6/sYFVa8jxERUeESf/EWJrbpgt9iX6JF8bI4EvAA8v+54Ijyv1y/jxEREVFhYNKgBtrNnAgjyOAZkYqIyUvAYwoFl9aDr4mIiAqL1pNG4b6dI4QpKxCz5RBkpsawnTc6T67CprzFI0ZERERZUHpgVxRZ/s/jpsJW7cQv3QbyyFEBxCNGREREWWTRszXSYuLQaeQwnN97BUGto/DDsf1SxyId4hEjIiKibLAe2hXtO3WECWSoejUAUev2SR2JdIhHjIiIiLLJe+d6tCpVEco1fyB8ylIIJkaw6NVG6likAzxiREREpIUKCybAckR3AMDjMfOwZfIsiRORLvCIERERkRYEQYDt3FF4HxmJ3r9/j8AfrkFUGKDv/BlSR6Mc4BEjIiIiLQmCgFLLv0P9cpXgKFPCee0RxJ+/KXUsygEWIyIiohyQGRhg/Z3LON51OEqkGSCk31QkXL8ndSzSEosRERFRDskVClTe9AOMm9SGGJ+Is51H4NT6LVLHIi2wGBEREemAoFTA0XcBXlQqhl7BN9BxyABc23tQ6liUTSxGREREOiIzMYLnnpWoYu2AcnITmExbheRnQVLHomxgMSIiItIhEzsbHL57E1vrtIdpZAyCu4xHyqsQqWNRFrEYERER6ZhlMSeU3rcChmVckfomDBu9usL/2l9Sx6IsYDEiIiLKBQZFrOG8dynOWIoYEXAZzRo3wdtH/lLHov/AYkRERJRLDJyKoNWOlXBRmMEDpkj+xgdpH2KkjkWfwWJERESUi1xqVsPlSxex2M0DaQ+fIbjnRKhi46WORZlgMSIiIsplzrXcUXTfMsisLZB48yF+rP8lIt9yQHZ+xGJERESUB5QVSsJ59xKsUoXh27tn0aLKF0iOjZM6Fv0PFiMiIqI8oqxWDt1+XgArmSFaJhkh4pv5EFNSpY5F/8JiRERElIc8vuqGu/uPYLCVG+KPXkLYyPkQ09KkjkUfsRgRERHlseLtm8Fhw3zA0ACR+05iXuP2SE5KkjoWgcWIiIhIEqbNPeHw+yx4xz7FrEtH0K9GfYiiKHWsQo/FiIiISCJm7Rpj4LjRMBPkaPkqARGzf2U5khiLERERkYS6L5qJB79uRkOFNaJ+3YH3i9dLHalQYzEiIiKSmOuIXrBbOBYA8GzxGvh07ssjRxIxkDoAERERAZZDuyIhJhZtJo/E0/3XkdhFhTn7tkodq9DhESMiIqJ8wtF7AAZ92REOMgXqnX2C6E0HpY5U6LAYERER5SOT92/B1ckLUcrABO8m/oiYXcekjlSosBgRERHlI4IgoMRCb1gO6QKIIi59PR2/jZ4sdaxCg2OMiIiI8hlBEGC7YAzehIehz7of8P6XW7C0tEDv+dOljlbg8YgRERFRPiTIZKjy21x0rVwTVQ3MUH7dccSfvi51rAKPxYiIiCifkhkYYNWtC9jf8xtYpgIhA6Yh/uItqWMVaCxGRERE+ZjM0BBu6+bDpGU9iInJ2NJ5MPb88LPUsQosFiMiIqJ8TlAYwnHdXDysWgwjw++hz+RxuLJ1t9SxCiQWIyIiIj0gKBVotX8NWjiVRCulHYrMXIuku/5SxypwWIyIiIj0hMLCDHsf3MSKJp0gi47H227eSHr0TOpYBQqLERERkR4xsrFC0Z0/QVm9AlTvozG3cTtc2r1f6lgFBosRERGRnpFbmMFp10/4ww5Y+O4x2vTsjpdXb0odq0BgMSIiItJDcitzDD62HbUs7DHCqBjEET5IeflW6lh6j8WIiIhIT1m6FsP5x/cxqlo9pL0Nw9vO45DyOlTqWHqNxYiIiEiPGTvbw3nfMhi6FUPiy7cYWqM+Hl68KnUsvcViREREpOcMHO3gvH8ZlhtGYGNYAFo1b464IJ5W0waLERERUQFgUNQB04/sRhVjK8xUuiKi97dIC38vdSy9w2JERERUQDhXr4y/7t9Dc9dySPF/gbddxyMtMkrqWHqFxYiIiKgAUZZygfP+ZZDb2yDs/hN0rOCOoEe8Q3ZWsRgREREVMIpSxeG8bxm+S36JQ2Ev0LVeI6RGxUgdSy/obTGKjIxEnz59YGFhASsrKwwePBixsbGfXaZx48YQBEHjNWLEiDxKTERElHcU5dzw8/5dcDeywhw4I6TnJKhi4qSOle/pbTHq06cPHj58iJMnT+LQoUO4cOEChg0b9p/LDR06FMHBwerX4sWL8yAtERFR3ivbrAGuXr+O8naOSLr5EME9J0EVGy91rHxNL4vR48ePcezYMaxduxYeHh6oX78+VqxYgR07duDt289fnmhiYgJHR0f1y8LCIo9SExER5T2jqmXhvGcpZBZmeHjlOrxKVkDYy1dSx8q39LIYXb16FVZWVqhZs6Z6WrNmzSCTyXD9+vXPLrt161bY2dmhcuXKmDp1KuLjP9+ck5KSEB0drfEiIiLSJ8pq5eC460d4x/+N8++CMKJ+c6jiE6WOlS/pZTEKCQmBvb29xjQDAwPY2NggJCQk0+V69+6NLVu24OzZs5g6dSo2b96Mr7766rPb8vHxgaWlpfrl4uKik89ARESUl4xrVMLWLVvQ0MgWU+OtENJvKlQJSVLHynfyVTGaMmVKusHR//t68uSJ1usfNmwYWrZsiSpVqqBPnz7YtGkT9u/fj2fPnmW6zNSpUxEVFaV+vXrFw49ERKSfqnVrj2Nnz8DWzAIJ528ipP80pCXwyNG/GUgd4N8mTJiAAQMGfHaekiVLwtHREWFhYRrTU1NTERkZCUdHxyxvz8PDAwAQEBCAUqVKZTiPUqmEUqnM8jqJiIjyM+M6VeG0fTGCe03CsePHsNZ1F448uA0r+yJSR8sX8lUxKlKkCIoU+e+/GE9PT3z48AG3bt1CjRo1AABnzpyBSqVSl52s8PPzAwA4OTlplZeIiEgfGdd1h9WGuZjWoglCo5Mwx6sDltw6B0GpkDqa5PLVqbSsqlChAlq1aoWhQ4fixo0buHz5MkaNGoWePXvC2dkZAPDmzRuUL18eN27cAAA8e/YM8+bNw61bt/DixQscPHgQ/fr1Q8OGDVG1alUpPw4REVGes/HyxP7f16G7iROGhwgIGTILYnKK1LEkp5fFCPjn6rLy5cujadOmaNOmDerXr4/Vq1er309JSYG/v7/6qjOFQoFTp06hRYsWKF++PCZMmIAuXbrgzz//lOojEBERScpzcB9sOPwHFEZGiD92CaFDZyGlkI85EkRRFKUOoU+io6NhaWmJqKgo3gOJiIgKhPgz1xHSbxrWvH+OM2ZpOPXkHsytraSOpVNZ/f7W2yNGREREpBsmXh6QLZuIXxNe40bYK6xt9xXElFSpY0mCxYiIiIhQomtr/PnzKkwwL4l2Tz4gdPicQlmOWIyIiIgIANBw1GDM2bMZglKBuD/P4e2w2YiLKlxPfGAxIiIiIjXTFnXhuGE+0gwNMGb7GjQrXwUx7z9IHSvPsBgRERGRBtMWdZGwYDhOJEfgr5AgHO01CmJq4TitxmJERERE6VQb2BN/LvsVy60qocatlwgdPrdQjDliMSIiIqIMNR4zDAN2/g4YGiDu4FkEDJxW4E+rsRgRERFRpkxb1IOj73zEGAjovnMVmpWviujI91LHyjUsRkRERPRZpi3qIXHmYDxLS4D/u2DcGTSlwJ5WYzEiIiKi/1Tn6/449PMqbLJzR7HLjxA6tGA+W43FiIiIiLKk4ajBaLr1Z0BhiLjDF3Cjxxh8eBcudSydYjEiIiKiLDNt7gmnTT4Ikqei48H18KpQFe9Dw6SOpTMsRkRERJQtJk09YDx/JBKhQvSHD3g1Yg5UiUlSx9IJFiMiIiLKtjpDvsLxtZux1aEGTC/dQ+iA6QWiHLEYERERkVZqD+yJKjuWQTBWIv70NRxs3Q9hr95IHStHWIyIiIhIayYNasBp+w+4IUtAr3N70KhyNYQFvZY6ltZYjIiIiChHjOtVR4XlM2AhN4Bjggqx3yyEKjZe6lhaYTEiIiKiHHPv2QEX9h7Eb841IF6/j+Cek/SyHLEYERERkU6U69ASJfYth8zSDInX72F53bZ48fCx1LGyhcWIiIiIdMboi4pw3rsMB+XR8L5/Do1qeeDd8xdSx8oyFiMiIiLSKWW1cmi35Re4GpqgBSyQMGgO0iI+SB0rS1iMiIiISOdKN2uA6xcvY5rrF0h5GIA3HccgNSxS6lj/icWIiIiIcoWDhzuKHlwBuYMtkh4/xyj3+vA7e0HqWJ/FYkRERES5RlHGFUUP/oL1iij8HvwYzVu0ROTjAKljZYrFiIiIiHKVYcliGH98F6qb2mCKUXHE9J6ClMD8eYdsFiMiIiLKdfZVKuDq4wfoVvELpL4OxZv2o5D09IXUsdJhMSIiIqI8oXRxgvMfK2BYrgRigkPR4ova+GvfIaljaWAxIiIiojxj4GiHogd+xjqzeBinqGD17Uok3fWXOpYaixERERHlKbmdNRZeP4VfG3aA7EMs3nYai4Tr96SOBYDFiIiIiCRg4mCHEvt+hlGdalDFxCG4+wTEX7wldSwWIyIiIpKGzNwUTjt/hHHjWhDjExHSazLiTl6VNpOkWyciIqJCTWZiBKcti2DSqj7EpGSE9J+GuCPS3QSSxYiIiIgkJSgVcFw/D2YdvSAzMYJBMUfJshhItmUiIiKijwRDA9ivmomUF2+gKFVcshw8YkRERET5giCXS1qKABYjIiIiIjUWIyIiIqKPWIyIiIiIPmIxIiIiIvqIxYiIiIjoIxYjIiIioo9YjIiIiIg+YjEiIiIi+ojFiIiIiOgjFiMiIiKij1iMiIiIiD5iMSIiIiL6iMWIiIiI6CMDqQPoG1EUAQDR0dESJyEiIqKs+vS9/el7PDMsRtkUExMDAHBxcZE4CREREWVXTEwMLC0tM31fEP+rOpEGlUqFt2/fwtzcHIIg6Gy90dHRcHFxwatXr2BhYaGz9VJ63Nd5g/s5b3A/5w3u57yRm/tZFEXExMTA2dkZMlnmI4l4xCibZDIZihUrlmvrt7Cw4P/p8gj3dd7gfs4b3M95g/s5b+TWfv7ckaJPOPiaiIiI6CMWIyIiIqKPWIzyCaVSiVmzZkGpVEodpcDjvs4b3M95g/s5b3A/5438sJ85+JqIiIjoIx4xIiIiIvqIxYiIiIjoIxYjIiIioo9YjIiIiIg+YjHKQytXrkSJEiVgZGQEDw8P3Lhx47Pz7969G+XLl4eRkRGqVKmCI0eO5FFS/Zad/bxmzRo0aNAA1tbWsLa2RrNmzf7z74X+X3Z/pz/ZsWMHBEFAx44dczdgAZHd/fzhwweMHDkSTk5OUCqVKFu2LP/9yILs7udly5ahXLlyMDY2houLC8aPH4/ExMQ8SqufLly4gHbt2sHZ2RmCIODAgQP/ucy5c+fwxRdfQKlUonTp0vD19c3dkCLliR07dogKhUJcv369+PDhQ3Ho0KGilZWVGBoamuH8ly9fFuVyubh48WLx0aNH4vTp00VDQ0Px/v37eZxcv2R3P/fu3VtcuXKleOfOHfHx48figAEDREtLS/H169d5nFz/ZHdffxIYGCgWLVpUbNCggdihQ4e8CavHsrufk5KSxJo1a4pt2rQRL126JAYGBornzp0T/fz88ji5fsnuft66dauoVCrFrVu3ioGBgeLx48dFJycncfz48XmcXL8cOXJE/O6778R9+/aJAMT9+/d/dv7nz5+LJiYmore3t/jo0SNxxYoVolwuF48dO5ZrGVmM8kjt2rXFkSNHqn9OS0sTnZ2dRR8fnwzn7969u9i2bVuNaR4eHuLw4cNzNae+y+5+/l+pqamiubm5uHHjxtyKWGBos69TU1PFunXrimvXrhX79+/PYpQF2d3Pv/32m1iyZEkxOTk5ryIWCNndzyNHjhS9vLw0pnl7e4v16tXL1ZwFSVaK0eTJk8VKlSppTOvRo4fYsmXLXMvFU2l5IDk5Gbdu3UKzZs3U02QyGZo1a4arV69muMzVq1c15geAli1bZjo/abef/1d8fDxSUlJgY2OTWzELBG339dy5c2Fvb4/BgwfnRUy9p81+PnjwIDw9PTFy5Eg4ODigcuXKWLhwIdLS0vIqtt7RZj/XrVsXt27dUp9ue/78OY4cOYI2bdrkSebCQorvQj5ENg+Eh4cjLS0NDg4OGtMdHBzw5MmTDJcJCQnJcP6QkJBcy6nvtNnP/+vbb7+Fs7Nzuv8jkiZt9vWlS5ewbt06+Pn55UHCgkGb/fz8+XOcOXMGffr0wZEjRxAQEIBvvvkGKSkpmDVrVl7E1jva7OfevXsjPDwc9evXhyiKSE1NxYgRIzBt2rS8iFxoZPZdGB0djYSEBBgbG+t8mzxiRPTRokWLsGPHDuzfvx9GRkZSxylQYmJi0LdvX6xZswZ2dnZSxynQVCoV7O3tsXr1atSoUQM9evTAd999h1WrVkkdrUA5d+4cFi5ciF9//RW3b9/Gvn37cPjwYcybN0/qaJRDPGKUB+zs7CCXyxEaGqoxPTQ0FI6Ojhku4+jomK35Sbv9/MmPP/6IRYsW4dSpU6hatWpuxiwQsruvnz17hhcvXqBdu3bqaSqVCgBgYGAAf39/lCpVKndD6yFtfqednJxgaGgIuVyunlahQgWEhIQgOTkZCoUiVzPrI23284wZM9C3b18MGTIEAFClShXExcVh2LBh+O677yCT8biDLmT2XWhhYZErR4sAHjHKEwqFAjVq1MDp06fV01QqFU6fPg1PT88Ml/H09NSYHwBOnjyZ6fyk3X4GgMWLF2PevHk4duwYatasmRdR9V5293X58uVx//59+Pn5qV/t27dHkyZN4OfnBxcXl7yMrze0+Z2uV68eAgIC1MUTAJ4+fQonJyeWokxos5/j4+PTlZ9PZVTkI0h1RpLvwlwb1k0aduzYISqVStHX11d89OiROGzYMNHKykoMCQkRRVEU+/btK06ZMkU9/+XLl0UDAwPxxx9/FB8/fizOmjWLl+tnQXb386JFi0SFQiHu2bNHDA4OVr9iYmKk+gh6I7v7+n/xqrSsye5+DgoKEs3NzcVRo0aJ/v7+4qFDh0R7e3tx/vz5Un0EvZDd/Txr1izR3Nxc3L59u/j8+XPxxIkTYqlSpcTu3btL9RH0QkxMjHjnzh3xzp07IgBxyZIl4p07d8SXL1+KoiiKU6ZMEfv27aue/9Pl+pMmTRIfP34srly5kpfrFyQrVqwQixcvLioUCrF27dritWvX1O81atRI7N+/v8b8u3btEsuWLSsqFAqxUqVK4uHDh/M4sX7Kzn52dXUVAaR7zZo1K++D66Hs/k7/G4tR1mV3P1+5ckX08PAQlUqlWLJkSXHBggViampqHqfWP9nZzykpKeLs2bPFUqVKiUZGRqKLi4v4zTffiO/fv8/74Hrk7NmzGf6b+2nf9u/fX2zUqFG6Zdzd3UWFQiGWLFlS3LBhQ65mFESRx/yIiIiIAI4xIiIiIlJjMSIiIiL6iMWIiIiI6CMWIyIiIqKPWIyIiIiIPmIxIiIiIvqIxYiIiIjoIxYjIiIioo9YjIiowGncuDHGjRsndQwi0kO88zUR6bXGjRvD3d0dy5YtU0+LjIyEoaEhzM3N8zzP+PHj8fLlS+zbty/Pt01EOccjRkRU4NjY2EhSigDgxo0bqFmzpiTbJqKcYzEiIr01YMAAnD9/HsuXL4cgCBAEAS9evEh3Kq1x48YYPXo0xo0bB2trazg4OGDNmjWIi4vDwIEDYW5ujtKlS+Po0aPqZVQqFXx8fODm5gZjY2NUq1YNe/bsyTRLcnIyDA0NceXKFXz33XcQBAF16tTJzY9PRLmAxYiI9Nby5cvh6emJoUOHIjg4GMHBwXBxcclw3o0bN8LOzg43btzA6NGj8fXXX6Nbt26oW7cubt++jRYtWqBv376Ij48HAPj4+GDTpk1YtWoVHj58iPHjx+Orr77C+fPnM1y/gYEBLl++DADw8/NDcHAwjh07ljsfnIhyDccYEZFey2iM0f9Oa9y4MdLS0nDx4kUAQFpaGiwtLdG5c2ds2rQJABASEgInJydcvXoV1atXh42NDU6dOgVPT0/1eocMGYL4+Hhs27YtwywHDhzAkCFDEB4enjsflohynYHUAYiI8kLVqlXVf5bL5bC1tUWVKlXU0xwcHAAAYWFhCAgIQHx8PJo3b66xjuTkZFSvXj3Tbdy5cwfVqlXTcXIiykssRkRUKBgaGmr8LAiCxjRBEAD8M7YoNjYWAHD48GEULVpUYzmlUpnpNvz8/FiMiPQcixER6TWFQoG0tDSdrrNixYpQKpUICgpCo0aNsrzc/fv30aVLF51mIaK8xWJERHqtRIkSuH79Ol68eAEzMzPY2NjkeJ3m5uaYOHEixo8fD5VKhfr16yMqKgqXL1+GhYUF+vfvn+FyKpUK/v7+ePv2LUxNTWFpaZnjLESUt3hVGhHptYkTJ0Iul6NixYooUqQIgoKCdLLeefPmYcaMGfDx8UGFChXQqlUrHD58GG5ubpkuM3/+fPj6+qJo0aKYP3++TnIQUd7iVWlEREREH/GIEREREdFHLEZEREREH7EYEREREX3EYkRERET0EYsRERER0UcsRkREREQfsRgRERERfcRiRERERPQRixERERHRRyxGRERERB+xGBERERF9xGJERERE9NH/Abme0vH6p4e/AAAAAElFTkSuQmCC",
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A3p1YCwsLidMQERFRekRHR8PFxUX7PZ4WFkYZ9OHymYWFBQsjIiIiPfNfw2A4+JqIiIjoPRZGRERERO+xMCIiIiJ6j2OMiIiIcpFarUZycrLUMfIdQ0NDyOXyLO+HhREREVEuEEURQUFBePv2rdRR8i0rKys4Ojpm6T6DLIyIiIhywYeiyN7eHiYmJrxJcDYSRRHx8fEICQkBADg5OWV6XyyMiIiIcphardYWRba2tlLHyZeMjY0BACEhIbC3t8/0ZTUOviYiIsphH8YUmZiYSJwkf/twfrMyhouFERERUS7h5bOclR3nl4URERER0Xt6Wxh5eXnh66+/hrm5Oezt7dG+fXs8fvz4P7fbs2cPypQpAyMjI1SsWBHHjh3LhbRERESkD/S2MDp//jyGDx+Oq1ev4tSpU0hOTkazZs0QFxeX5jaXL19Gjx49MGjQINy+fRvt27dH+/btce/evVxMTkREpD/69+8PQRAgCAIUCgVKliyJ2bNnY9euXZDL5fD39091u1KlSsHT01P7/v79++jatSsKFSoEpVKJ0qVLY/r06YiPj9fZrlixYtrjGRsbo1ixYujatSvOnDmTo5/zA70tjI4fP47+/fujfPnyqFy5MjZv3oxXr17h5s2baW6zfPlytGjRAuPGjUPZsmUxZ84cfPXVV1i1alUuJk9dSnA4Anzuwe/v24h6/hIpIRFQh79FSnQsVPEJ0Gg0UkckIqICqkWLFggMDMTTp08xZswYzJw5E0+ePIGtrS22bNnyyfoXLlyAr68vBg0aBAC4evUqatSoAZVKhaNHj+LJkyeYN28eNm/ejKZNm0KlUulsP3v2bAQGBuLx48fYunUrrKys0KRJE8ybNy/HP2u+ma4fFRUFALCxsUlznStXruhUrwDQvHlzHDx4MM1tkpKSkJSUpH0fHR2dtaBpCBn5IwYe2oITqnDMMi2BXsbv7sHwQp2AJpE3YSEY4PYX9SEYKSEzM8HKCF9cjA5C/y+roWPF6pBZW0JlboQd92+gkKMDOrVtB6WTPeT2NpBZW3DAHxERZZpSqYSjoyMA4IcffsCBAwdw7Ngx9OnTB5s3b8bkyZN11t+4cSNq1KiB8uXLQxRFDBo0CGXLlsX+/fshk73rkylatChKly6NqlWrYunSpZgwYYJ2e3Nzc+3xihQpgnr16sHJyQnTp09H586d8eWXX+bYZ80XhZFGo4G7uztq166NChUqpLleUFAQHBwcdNocHBwQFBSU5jZeXl6YNWtWtmVNi6B4dytzQwiQy/7fkZckvuspMoQAMSEJYkISNJHReBT9BtdVYWh+9yFifd8Vha/ViRgfeQNKyFBn2wVtMeSV8AJnUt5iaOnq6OfWAAaFHSA42+FGTBhK16iGItUqQ6ZU5PhnJCKi/xNFEWJ8oiTHFkyMsvQPZmNjY4SHh2PQoEFYsmQJLly4gHr16gEAYmNjsXfvXixduhQA4OPjgwcPHmDHjh3aouiDypUro0mTJvj99991CqPUjB49GnPmzMGhQ4cwfvz4TGf/L/miMBo+fDju3buHv/76K9v3PWnSJJ1epujoaLi4uGT7cZx+m4+jmK/TJmo0KJKQiLCwcCTFxKGQhSXEhERoYuMx6dZt9Hj+HOUKOcHWxBKayGhEvfBD65NxkCWnQOFaHOqQCGgiovA6OQF+qlgkPH+N2MDTAAB/dSKaR96AIQTcK1QHyi8cYFj8C/wlT8BbcyXqNmuCUvXcIHewZW8TEVEOEOMT4VesmSTHdn1xEoKpcYa3E0UR3t7eOHHiBEaOHIly5cqhZs2a2Lhxo7Yw2r17N0RRRPfu3QEAT548AQCULVs21X2WLVs2Xd/fNjY2sLe3x4sXLzKcOyP0vjAaMWIEjhw5ggsXLuCLL7747LqOjo4IDg7WaQsODtZ216VGqVRCqVRmS9aMEmQyGJqawNb00xuC1a5WHrX/1WYD4Ah0r7+KqmSs+ecunt78B0UNTGCrEpHyJhjP7vigiPcjKNUi5KKIlNdBSHkdhLXRD+CtisD0nUfQ19gZMitzxJUsjC0xr1C1WjV07NcbirIlIDOW5pwQEVHuO3LkCMzMzJCcnAyNRoOePXti5syZAICBAwfCw8MDK1euhLm5OTZu3IguXbrA3NxcZx+iKKa5f4UifVctRFHM8X+s621hJIoiRo4ciQMHDuDcuXNwdXX9z23c3Nzg7e0Nd3d3bdupU6fg5uaWg0mlJSgM4fr1V3D9+iud9mYAXmIT1Go1xLC3SH7hj+Rnr1Ft3S+Ie3AHFW2LAeEp0LyNwe3LV7Eo+h6KXLmAGjsuAnI5FGWK4aSxCgrXwmjctSOcalWHoDCU5DMSEekjwcQIri9OSnbsjGjYsCF++eUXKBQKODs7w8Dg/+VD9+7d4eHhgd27d6NevXq4dOkSvLy8tMtLlSoFAHj48CGqVq36yb4fPnyI0qVL/2eG8PBwhIaGpuv7Piv0tjAaPnw4duzYgUOHDsHc3Fw7TsjS0lL7vJS+ffuicOHC2j+g0aNHo379+li8eDFat26NnTt34saNG1i7dq1kn0NqcrkccLCFgYMtjGtUwoKerbXLNIlJSH7yEsWOnUT3Xb/BOj4FMpkVNGFvobr/DAsib+GpOh5rfjuKphZOUFYqjeiyRRBW2AZfd+8AI2d7CT8ZEVHeJghCpi5nScHU1BQlS5ZMdZm5uTm6dOmCjRs34tmzZyhdujTq1q2rXV61alWUKVMGS5cuRffu3XXGGf3zzz84ffp0umaHL1++HDKZDO3bt8/y5/ksUU8BSPW1adMm7Tr169cX+/Xrp7Pd7t27xdKlS4sKhUIsX768ePTo0QwdNyoqSgQgRkVFZcOn0D8ajUZMfhMkxhw9L/b/pp5Y1rKQeL1YI9HXro7oa1dHnGtWUgQg1jW0El/W6CEGj/YSo34/JqpeB0odnYhIMgkJCeKDBw/EhIQEqaNkWL9+/cR27dp9dp2LFy+KAERra2tx/vz5nyz/66+/RBMTE7F9+/bitWvXxJcvX4q7d+8WXVxcxBYtWogpKSnadYsWLSrOnj1bDAwMFF+9eiWeP39eHDJkiCgIQqr7/tjnznN6v78FUfzMRT/6RHR0NCwtLREVFQULCwup4+QJoigi+flrJN18gGVrfsGiC3+ir9IR7iZFAQBqUUSDyBsoZ26LlT0Gw6VZPRjX/QpyO2uJkxMR5Y7ExET4+fnB1dUVRkYZu4wltf79++Pt27efvbUNAJQpUwa+vr54/fo1nJycPll+9+5dzJo1C2fPnkVERASAd+OEly5dqnNprlixYnj58iWAd2OPHB0dUbNmTQwdOhQNGzb8bIbPnef0fn+zMMogFkb/Ta1WIy4oFPL7z5B49Q4uHzuBttcOwVyQ44ZNTcgFARAEXHQ2hqZsMbQc2AeODWtCMNDbK7tERJ+lz4VRTtBoNBg0aBBOnDiB8+fPa8chZRULIwmwMMo4jUaDW5eu4Onp82iqMUfCxZtQ3X+Grm//wa2UGMwxLYFeDqVg3PAbmDStCdOmtSC3sZQ6NhFRtmFh9CmNRqOdyTZw4MBs2Wd2FEb8JzrlOJlMhup1a6N63f/fYCA5MBR1vvsBMX+dRwProtBExyLu0Bkc3r0H8+NfoGepKhgzfATMWtWDYfHP34aBiIj0j0wmw+jRo6WO8QkWRiQJQ6dCWP7HXiwHIKrVSPJ5hLgTl3Fu1SL4xSTg1YsXiJj1CyJm/QJFueJ4UOEL1PmuD0wqfckbThIRUY5hYUSSE+RyGFUrD6Nq5bFmRDe02bELRYJjYXzvNRL+uoUnd++jxYWtKPTLT/irenvYdWwG03aNoCxbXOroRESUz7AwojzFwsICPYYO0b5XR0bj9oJlsF56H18KxpD7BSJy8RZELt6CozYiKrZqgtqjhkDhWljC1ERElF/I/nsVIunIrS3Qef50BMdGY+e9v2H/yzSYNK+NBAMBk59eRaNls3Ckamv4t/oBUZsOQB0RJXVkIiLSYyyMSC8YGhqicMkSMO/cDE6/zYfl2fVoVqMWyljYopLCAol/30PY+CVYXdwNq+u2Rdghb4jJKVLHJiIiPcNLaaSXXMqUxv4rF9496y00ErEHTiN69wmsuHgTz/56iGifh+jhUg5mXZrBomcrKL7M2WfrEBFR/sAeI9JrcrkcBo52sPqhOxxP/Iru3w/Bl4Uc0ca5FNShEYj6eSf212iHBRUbIHDLAWjiE6WOTEREeRgLI8o3FAoF5q5ejofBASh/7zAct3nBtFVdrE3wx8R75zHzh5F4WbEDQicuheqRn9RxiYgoD2JhRPmOIAgQDA1g2qIOHDbPQ+spHihZyBE9ileEJjoW0Rv243KtrlhTvQUi953iWCQios/o37//u9+r/3q1aNEiV44/c+ZMVKlSJVeOBbAwonxOEASMmT4VT4IDUP/OUTjtWQLT1vWxJSkQP9w8gYG9euHlV10QsWgTUkIipI5LRJQntWjRAoGBgTqv33//XepYOYKFERUIgiBAkMlg0uBrOG6eizKeg1DI1Bzt7UtAHRSGyAUb8ahyB1zs5Y6ku0+ljktElKcolUo4OjrqvKytrXHu3DkoFApcvHhRu+7ChQthb2+P4OBgAMDx48dRp04dWFlZwdbWFt9++y2ePXums/83b96gR48esLGxgampKapXr45r165h8+bNmDVrFv755x9tT9XmzZtz9LOyMKICacKcWXgdHoreT87Bfs10KKuVw/4Yf9TbsRwDvqkH/3YjEXf8L4gajdRRiSifi4uLQ1xcHD5+prtKpUJcXBySkpJSXVfz0e+m5ORkxMXFITExMV3rZqcGDRrA3d0dffr0QVRUFG7fvo1p06Zh/fr1cHBw0Obw9PTEjRs34O3tDZlMhg4dOmhzxcbGon79+vD398fhw4fxzz//YPz48dBoNOjWrRvGjBmD8uXLa3uqunXrlq2f4RMiZUhUVJQIQIyKipI6CmWzUT37iQIgTjcvKfra1RF97eqIL2r2FMM3HRDVCYlSxyMiPZaQkCA+ePBATEhI+GQZABGAGBISom2bO3euCEAcPHiwzromJiYiANHPz0/btnTpUhGA2LNnT5117ezsRADivXv3tG1r167NcPZ+/fqJcrlcNDU11XnNmzdPFEVRTEpKEqtUqSJ27dpVLFeunDhkyJDP7i80NFQEIN69e1cURVH89ddfRXNzczE8PDzV9WfMmCFWrlw5XVk/d57T+/3NHiOi95Zv34ynvr7wuHECViN7QmZuiosP7qDyoO5Y6FoDkUu2Qh0VI3VMIqJc17BhQ/j4+Oi8hg4dCuDdjODt27dj3759SExMxNKlS3W2ffr0KXr06IHixYvDwsICxYoVAwC8evUKAODj44OqVavCxsYmVz9TWniDR6KPlChR4t3/TP8B1h59MaZBE7y5lYR7b0MQ4bUOkSu3w3JAB1h+3wUGDrbShiWifCE2NhYAYGJiom0bN24c3N3dYWCg+zUdEhICADA2Nta2DR8+HEOGDIFcLtdZ98WLF5+s279//0xlNDU1RcmSJdNcfvnyZQBAREQEIiIiYGpqql3Wpk0bFC1aFOvWrYOzszM0Gg0qVKgAlUr1Sb68gD1GRGmQmZvit4veWLViBcb/9CMUZYtDjI3Hw2UbMaZUdTwZOQfJrwKljklEes7U1BSmpqYQBEHbplAoYGpqCqVSmeq6Mtn/v74NDQ1hamoKIyOjdK2b3Z49ewYPDw+sW7cONWrUQL9+/bTjh8LDw/H48WNMnToVjRs3RtmyZREZGamzfaVKleDj44OIiNRnBisUCqjV6mzPnRYWRkSfYWJiguEjR6LSiP744twmOP42H5stErAi5gW++3UpXtXogRD3+Uh+ESB1VCKiHJOUlISgoCCdV1hYGNRqNXr37o3mzZtjwIAB2LRpE+7cuYPFixcDAKytrWFra4u1a9fC19cXZ86cgaenp86+e/ToAUdHR7Rv3x6XLl3C8+fPsW/fPly5cgUAUKxYMfj5+cHHxwdhYWGfDEjPbiyMiNJJkMlg2rw2Wi+djYolS2NojQZAihox24/Ct0Z3PB48Bapnr6WOSUSU7Y4fPw4nJyedV506dTBv3jy8fPkSv/76KwDAyckJa9euxdSpU/HPP/9AJpNh586duHnzJipUqAAPDw/89NNPOvtWKBQ4efIk7O3t0apVK1SsWBHz58/XXhrs1KkTWrRogYYNG6JQoUI5fv8kQRQ/mh9I/yk6OhqWlpaIioqChYWF1HFIIh/+2iT9fQ8Ri7dg07FD8IrzwyjTonDvPwjWY/rDsJizxCmJKK9ITEyEn58fXF1dP7nkRdnnc+c5vd/f7DEiyoQPNxoz+qYinHctwtVqLogT1ZCJImJ2/olXbj0R4rkQyW+CpY5KREQZwMKIKBscOnMaBw8exNiTe2Hc8BsgRY3LG3dgbvk68B+3iI8bISLSE5yuT5QNZDIZ2rVrBwCwqvUVEq7ewYC23+Ji6Gu8WrUU03afhNXQrrAc3h1yCzOJ0xIRUVrYY0SUA4xqVESvOVNQzMkZ31WvBzE+AZFLtuB5tS6IXLUDmsScnVVBRESZw8KIKAcIgoDvv/8evq9foeaFHXDYPA+GpYtiof9dfOs5DKeqfIuY3cf5LDaiAobznXJWdpxfFkZEOUgul0MQBJi1rgero6uwG5G4nByF14EBCBk+D28aD0b8ub+ljklEOezDjRXj4+MlTpK/fTi/WbmRJccYEeUSCysr+Ny/h+1btqKjVXFErdgO1b2nON/he5RqVBdF5rlDUcZV6phElAPkcjmsrKy0j/QwMTHRudM1ZY0oioiPj0dISAisrKw+eTxKRvA+RhnE+xhRdlGHv0XQwo1wWzwFKlGDtVYV4DaoJ2zGD4TczlrqeESUzURRRFBQEN6+fSt1lHzLysoKjo6OqRad6f3+ZmGUQSyMKDs9fvwYzZs0RWJ4JE6aVISpIIfM3BRWnn1hNaQzBKVC6ohElM3UajWSk5OljpHvGBoafraniIVRDmFhRNktMTERT58+RcnoFIRNXwXVnSf4LSEQrUtVQNkF42HarJbUEYmI9B7vfE2kJ4yMjFCxYkUY166KL06tw6OhrTEz7hma/HMUT3qMQWD3cVD5vpI6JhFRgcDCiCgPEWQyOHdsjupffYVO1WvBQmmEeO+reF23L8JmroYmljNaiIhyEgsjojymatWquPb331h57k+4XNwKk2a1EK5KxPD5c3CjeifEHDjNe6EQEeUQFkZEeZBMJoOJiQkUJVzgtH0BVlW3w+6kYIx4dhkh381CQEd3qB77SR2TiCjfYWFEpAdG/zgLNb7+BnOGjoRgpEDiX7fwusEAhM9ZA018otTxiIjyDc5KyyDOSiOpiKIIQRCQ/DIAYVNX4veD++GTEoPxpb+B60/jYNqsttQRiYjyLE7XzyEsjCgviIuLQ7HCXyAs6i0mm7pioHFhmLauB7t5o2BQ2EHqeEREeQ6n6xPlY6ampvh97x60+7YNRo7xBAzkiDt6Aa9q90HUur0Q1WqpIxIR6SX2GGUQe4woL0p6+Byhngvx3Zl9cDO0RL9aDeGwdAKU5UtKHY2IKE9gjxFRAaIsWxw3v2+BP1VhmBvvB78bPnjTZPC7wdkJSVLHIyLSG+wxyiD2GFFepVarsXr1aiS/jUYvv0TEHTkPADAs4YJCSyfA2K2yxAmJiKTDwdc5hIUR6Yu4Py/ilvscjPO7jhmmxVF9SG/YTvseMnNTqaMREeU6XkojKuBMW9bF4lJKXEuOwrw4P0RvOoDXdfsi3vua1NGIiPIsFkZE+djqX9egc+fOWLN1MwyKOSPFPwSB3cciZPR8qKNipI5HRJTn8FJaBvFSGukrTVwCIn5ch1UrViBeo8b3xavAcekEmDapKXU0IqIcx0tpRKRDZmqM+O/bY4HqDX6Kf4FjLx8hqMc4hIzygjo6Vup4RER5AgsjogLExcUFv/y6Bp07dkT3kUMBQUDM78fwpl4/xF+4IXU8IiLJ6XVhdOHCBbRp0wbOzs4QBAEHDx787Prnzp2DIAifvIKCgnInMJHEBEFA//79sWffPhSaNxrOh1cBRR0x+/FV3G4/DKETlkITlyB1TCIiyeh1YRQXF4fKlStj9erVGdru8ePHCAwM1L7s7e1zKCFR3mZcsxJ+b10eGxMD0CPqDiI27MObhgOR+Pc9qaMREUnCQOoAWdGyZUu0bNkyw9vZ29vDysoq+wMR6aEuPXvg0LGj+KFZGyj/8EGy3xv4fzsc1u59YD22PwRDvf41QUSUIXrdY5RZVapUgZOTE5o2bYpLly59dt2kpCRER0frvIjyky+//BJXrlzBgLnT4HJxC8y6NMMTVQxOLFgB/5ZDoXr6UuqIRES5pkAVRk5OTlizZg327duHffv2wcXFBQ0aNMCtW7fS3MbLywuWlpbal4uLSy4mJsodBgYGEAQBcktzWCwei7EWb9E76i52X7uIN40GImr9PvDOHkRUEOSb+xgJgoADBw6gffv2Gdqufv36KFKkCLZt25bq8qSkJCQl/f8hnNHR0XBxceF9jCjfiouLw8iRI3H0jz9wqmYnmF69DwAwaVwThVZMgoG9jcQJiYgyjvcxSqdvvvkGvr6+aS5XKpWwsLDQeRHlZ6ampti4cSPu3LuHiod+ht2PoyEoFbh9whuv6/dD3KkrUkckIsoxBb4w8vHxgZOTk9QxiPIcBwcHCDIZLId0xssF36PNWx+4+13D6x5jETZpGTQJSf+9EyIiPaPX001iY2N1env8/Pzg4+MDGxsbFClSBJMmTYK/vz+2bt0KAFi2bBlcXV1Rvnx5JCYmYv369Thz5gxOnjwp1Ucg0gsPo8IgyGVQlioKwyAZotbvQ8Kl23BYOxOKMq5SxyMiyjZ6XRjduHEDDRs21L739PQEAPTr1w+bN29GYGAgXr16pV2uUqkwZswY+Pv7w8TEBJUqVcLp06d19kFEnxo2bBiqVq2K8uXLw+DvhwgZ+SPiHvjiVdPBsJ83GuZ92kAQBKljEhFlWb4ZfJ1b+BBZIiAlJAJD6zTB8xcvsMC8NFzaNkGhpRMgtzKXOhoRUao4+JqIckxgUhy2v36Is8mRuCvGI+7IebxpOACJ1+9KHY2IKEtYGBFRhrm4uODq1atYvHgxep7ZDUPXL5DyJhj+bUcicsV2iBqN1BGJiDKFl9IyiJfSiD6liY3Hs5FzMH3nZniaFEXhxrVhv3oqDApZSx2NiAgAL6URUS6SmZlgdsob7EwMwg+xjxB/5hreNByAhEu3pY5GRJQhLIyIKFuMHz8eFStWxNItG6H4shjUweEI6OiOyKVbeWmNiPQGL6VlEC+lEaVNo9FAJpNBE5eAsAlLcHX7XhSRGcG+SW04/DwVclsrqSMSUQHFS2lElOtksne/UmSmxkie1B+DNH5oH/0PHp86j9eNBnHWGhHleSyMiChHhIeHw8TKEpYliqJwSVeoA0Lg324k3q7ZBXZUE1FexUtpGcRLaUTpFx4ejtjYWLjYFkKoxwLEHjwDtSjCom1D2C+fCJm5qdQRiaiA4KU0IpKcra0tihYtCpmZCezXzoR326roGn0HvgdP4E3TIUh6+FzqiEREOlgYEVGuSEpKwuwT+/FPcgwOKeOQ/Ow1/Ft8j5h9p6SORkSkxcKIiHKFkZERLly4AHd3d8zzOQ/j+tUhxiciZOhshE1eDjE5ReqIREQcY5RRHGNElD1EtRphXuuxYv4CdDNyhHXNqnBYPwsGjnZSRyOifIhjjIgoTxPkcqwVwjA3zg99Yh8g7uo/eNNkMBKu3ZE6GhEVYCyMiEgydevWhb29PYbOmAyjssXf3S27/ShEbdjPKf1EJAleSssgXkojyl6RkZGwtraGJjYeoR4LELT/JEwFOSx6tobdQk/IjJRSRySifICX0ohIL1hbWwN49yBaixUTMcg8FOPiniJsxxEEtB2JFP9giRMSUUHCwoiI8oxLly7B580LnDdMRJi5Akm3H+JN0yFIuOwjdTQiKiBYGBFRntGkSROcOHECew8egNu57VCULwl1aCQCOrkjavNBqeMRUQHAMUYZxDFGRLlHE5+I8308cPPkGXQwsodFv3aw+3E0BIWh1NGISM+k9/vbIBczERFlSFRSAgbeOo4XsS+QDBFdtxyC6pEfHDbOgYG9jdTxiCgf4qU0IsqzLC0t0atXL5QqVQq9N6+EzNwUidfuwL/ZECTdeSJ1PCLKh3gpLYN4KY0o98XGxsLMzAwq31cI6jMJUU/9YGpiAvuVU2DWrqHU8YhID3C6PhHlG2ZmZgAARckiuDOmIxrH/YO/o0MQPHg6IhZsgKjRSJyQiPILFkZEpDdEUcSSn1cjNDEef5Z49y++yEWbETxoOjRxCRKnI6L8gIUREekNQRBw5MgRzJo1C2uvnEGh5RMBQwPEHTkP/2+H82aQRJRlHGOUQRxjRJS3JFy7gxXt+6B1sinMHArBcZsXjL4qJ3UsIspjOMaIiAqEjTcuYmLQHfRWPUFicBgC2o1EzIHTUsciIj3FwoiI9FrlypVha2uL7mNHw7J5HYiJKoR8N+vdoGx2iBNRBmXLpbSIiAhYWVlBJsv/dRYvpRHlPcHBwbC3twc0GoTPWYO3q36HIAgw69AYhVZMgsxIKXVEIpJYjl9Ke/DgAebPn49atWqhUKFCsLe3R9++fbFv3z7ExcVldrdERBnm4OAAQRAgyOWwmvo9RhZRYbsqGLEHvBHQ0R0poZFSRyQiPZGhwujx48cYM2YMSpUqhZo1a+Lvv//G0KFDERwcjGPHjqFo0aKYPXs27Ozs0LJlS/zyyy85lZuIKFU7d+7E8dt/Y37SKwSZGiDp73vwb/E9VI/8pI5GRHogQ5fSNm3ahGvXrqFdu3Zo3LgxFApFquu9ePEChw4dwh9//IHTp/PXIEheSiPK20RRxI8//ojy5cujVYWvENhjPFJe+ENmbgqHDbNh0vAbqSMSkQTS+/3N6foZxMKISL+ow9/iVrfREG8/hq2hEQr95AmLPm2ljkVEuSxXp+snJCTA39//k/b79+9nx+6JiDItRg4MCLyBbilP8UIVi1DPnxA++xc+RoSIUpXlwmjv3r0oVaoUWrdujUqVKuHatWvaZX369Mnq7omIsiQ8PBzxCQlQmRmh0NDuAIC3K3cgePAMaBKSJE5HRHlNlgujuXPn4ubNm/Dx8cGmTZswaNAg7NixAwB4DxEiklyJEiVw9epVnDhxApV+HAv7n6e+e4zIH+cQ0GEUZ6wRkQ6DrO4gOTkZDg4OAIBq1arhwoUL6NChA3x9fSEIQpYDEhFllb29/bv7HAEw79Ict9+G4MxkL/S5+QD+rYbCaedPUJQoInFKIsoLstxjZG9vjzt37mjf29jY4NSpU3j48KFOOxFRXhASEoLOU8ZgVthD7DdNRMqLAPi3/AEJ1/j7ioiyoTDatm2b9l9iHygUCvz+++84f/58VndPRJStChUqBE9PT1SvXh1Dzu6DsmpZaCKjEdjJA7GHz0odj4gkluHp+j169MCUKVNQoUKFnMqUp3G6PlH+oFKpoFAooIlLQPDQ2Yj+8yIMZDLYzhoGqx+6Sx2PiLJZjk3X37VrFxo3box79+6lulwURcTGxmZ0t0REuerDDWplpsY417IiOsn9EJSSiPDpqxE2ZQVEtVrihEQkhUxdSqtSpQoaNWqUanEUEhICKyurrOYiIsoVSUlJmDR5Mu6HBOCI27sB2FFr93A6P1EBleHCSBAEbN68GY0aNUKjRo1w9+7dT9bR8MZpRKQnlEolzp07h3HjxmHeH7thv3YGoDBE3JHzCOzsAXVktNQRiSgXZbgwEkURcrkcO3bsQOPGjVMtjjhNn4j0SbFixbBw4ULIZDKYd2gCp12L8EipQeL1u/BvPQzJrwKljkhEuSTTs9JkMhm2b9+OJk2aoFGjRpyaT0T5xvyTB9Eu6Br2GcUj+elL+Lf6AUn3fKWORUS5IFOX0rQbvy+OmjZtisaNG7M4IiK9J4oiXr9+DbVaDfNRPaEoWxzq4HAEtB2B+Is3pY5HRDksU5fSdHYgk+G3337TFkc+Pj7ZlY2IKNcJgoCNGzfi9OnT+GHcGDj/sQpGbpWhiYlDYLexiDlwWuqIRJSDMlwYHT16FJaWlro7eV8cNWvWDJ06dcq2cEREUhAEAY0bNwYAyC3NYb11HjYWNYRKpULId7Pwds1uiRMSUU7JcGHUsmVLKJXKT3ckk2Hbtm1o165dtgRLjwsXLqBNmzZwdnaGIAg4ePDgf25z7tw5fPXVV1AqlShZsiQ2b96c4zmJSL8N+mEofrx5FpMKxQMAwqetRPjsNXxQNlE+lOVHgujs7H3P0ZUrVwAA165dy87dfyIuLg6VK1fG6tWr07W+n58fWrdujYYNG8LHxwfu7u4YPHgwTpw4kaM5iUi/9evXD9bW1hi1ejFspn4PAHi7cjtCR/4IMTlF4nRElJ0y/EiQjChSpAhevXqVU7vXIQgCDhw4gPbt26e5zoQJE3D06FGdG1N2794db9++xfHjx9N1HD4ShKhgio6O1v6dj95xFKGePwFqNUya1ITD+tmQmRpLnJCIPie9398GWT1Q165dU20XRRERERFZ3X22unLlCpo0aaLT1rx5c7i7u6e5TVJSEpKS/n/32+ho3uyNqCD6+BdpQuPqGOIchymBhih2+ioCOnvAafsCyG0sP7MHItIHWS6MTp8+jW3btsHMzEynXRRFXLhwIau7z1ZBQUFwcHDQaXNwcEB0dDQSEhJgbPzpv/i8vLwwa9as3IpIRHpgxIgROHv7BuIqVMLvCc5IunEf/m2Gw3nPEhg420sdj4iyIMuFUYMGDWBubo569ep9sqxSpUpZ3b3kJk2aBE9PT+376OhouLi4SJiIiKS2evVqxMXFYcWKFfhCLUdA17FIfvLuRpBOe5ZAUaqo1BGJKJOyXBjt378/zWWnTp3K6u6zlaOjI4KDg3XagoODYWFhkWpvEfDuOUqpzcIjooLL3t4ex44d074vfPRnPO04EvALhP+3w+G08ycYVS0rYUIiyqwMz0rr0aOHzuBlfeLm5gZvb2+dtlOnTsHNzU2iRESUH9wPC0T952fwp6MhNBFRCGg/GvHn/pY6FhFlQoYLo127dqFx48ZpFkeiKCI2NjbLwdIjNjYWPj4+2rtt+/n5wcfHRzsTbtKkSejbt692/aFDh+L58+cYP348Hj16hJ9//hm7d++Gh4dHruQlovxp48aNCAsPx16rFCjrVYMYn4DAnuMRe/is1NGIKIMydR+jKlWqoFGjRqkWRyEhIbCysspqrnS5ceMGqlatiqpVqwIAPD09UbVqVUyfPh0AEBgYqHO7AFdXVxw9ehSnTp1C5cqVsXjxYqxfvx7NmzfPlbxElD8tW7YMCxYswKEjf6DwjoUwbdsQSE5B8JCZiN52WOp4RJQBGb6PkVwux5s3b+Dh4YEzZ87A29sbFStW1C4PDg6Gk5MTNBpNtofNC3gfIyL6L6JaDe9+nih2/CYEQYDNtKGwHtVL6lhEBVp6v78z9RBZuVyOHTt2oHHjxmjUqBHu3r2rs44gCBlPTESUT+w/eBDNf18FrxIG0IgiIuasQfisn/kIESI9kOlHgshkMmzfvh1NmjRBo0aNcOfOnezMRUSktyIiIt4VQRVKwHbGDwCAt6t+R6jnQohqtcTpiOhzMjxd/+PeoA/FUe/evdG4cWN4e3t/cgNFIqKCZsiQIfjyyy9Ru3ZtyOVyGNhYIdRzIWJ+OwJNdBwcfpkGQWEodUwiSkWmLqXp7OD9g2ObNm2Kxo0ba2eIEREVZPXq1YNcLgcAmPdsheNdv0GCgYC4w2cR2HsiNHEJEickotRkuDA6evQoLC11nwf0oThq1qwZOnXqlG3hiIjyAy8vL4xYtQjDHOKgNlYg4ex1BHYdA3VUjNTRiOhfMlwYtWzZMtU7QctkMmzbtg3t2rXjAEMioo/Ur18fFhYW6DSgL4rsXQaZhRkSr99FQLtRSAmNlDoeEX0kQ9P1g4KCYG1t/dlHZIiiiL///hvffPMNnj9/juLFi2dL0LyC0/WJKDOCg4O1YzCT7vkisKsn1KGRMCxZBM57l8CgMMdnEuWkHJmuv3fvXtjY2KBDhw7YtGkTQkNDP1nn+vXrOHjwIMqXL4/KlStnPDkRUT708cQUoXQR/PS1LcLszZHs+wr+bUYg+fkbCdMR0QcZvsGjr68vDh8+jEOHDuHq1av4+uuv0apVK/j5+eHIkSMAgNatW6Ndu3Zo2rQpjIyMciS4VNhjRERZNXLkSKxatQrlSn+JI5aVofELgNzeBk57l0JZNn/1shPlFen9/s5wYfSx8PBwHDlyBMeOHUOxYsXQrl07uLm55esbPLIwIqKsevnyJVq1aoWlS5eiUZXqCOziAdWD55BZW8Bp92IYVSkjdUSifCdXCqOCiIUREWWHlJQUGBi8u5WcOjIagT3GIenmAwhmJnDasRDGbhyKQJSdcuyRIERElHUfiiIAiEhJQp/4R3he4QuIsfEI7DYG8ef+ljAdUcHFwoiISGLjxo3DuYsXMCbsHowa1YCYkITAXhMQ9+dFqaMRFTgsjIiIJLZs2TK0b98ee/buhfPWH2Hauj6gSkbQgGmI2X9a6nhEBQrHGGUQxxgRUU4TU1LwZsRcqPZ5A4KAQksnwKJXa6ljEek1jjEiItJTdx88QJ0/N+FOw/KAKCLUfT6iNuyXOhZRgWDw36ukzdvbG97e3ggJCYFGo9FZtnHjxiwFIyIqqH766Se8evUKK4o/xu7vOiN67V6ETVwKMTEJVsN7SB2PKF/LdGE0a9YszJ49G9WrV4eTk1O+vncREVFuWrduHZycnDB58mRYWlpCZmqCt0u3Inzmz9AkJMJ6TH/+ziXKIZkeY+Tk5ISFCxeiT58+2Z0pT+MYIyKSwpNZqyBftQsAYDWyF2ymfc/iiCgDcnyMkUqlQq1atTK7ORERpdOhQ4dQdeEEXOn0DQDg7crtCJ+2Epw7Q5T9Ml0YDR48GDt27MjOLERElIoDBw4gPj4epxPDYbfQEwAQ9esehI1fDPFf4zuJKGsyPcYoMTERa9euxenTp1GpUiUYGhrqLF+yZEmWwxEREbBhwwbUrFkTgwcPhoGBAQSFAqEeCxC9+RDEpGQUWjoeglwudUyifCHTY4waNmyY9k4FAWfOnMl0qLyMY4yIKC+4tnQt7BbsANRqmHVqCvtVkyEYZGmiMVG+lt7v70z/LTp79mxmNyUioiyYO3cuZsyYgV9HT0DD3y8jdt8piMkpcFgzHYIhiyOirOANHomI9Igoinj9+jU0Gg0iHK3guGkuYGiAuMNnETx4OkRVstQRifRalh4J8vbtW2zYsAEPHz4EAJQrVw6DBg2CpaVltgXMa3gpjYikptFocPLkSbRo0QIAEHfqCoIHTIWYpIJJUzc4bJwDmZFS4pREeUuOT9e/ceMGSpQogaVLlyIiIgIRERFYunQpSpQogVu3bmV2t0RE9B9kMpm2KAIAZcOv8dCjIwQjBeJPXUFQn0nQJCRJmJBIf2W6MPLw8EDbtm3x4sUL7N+/H/v374efnx++/fZbuLu7Z2NEIiJKS0pKCnr16oU240biZO86EEyMkHDubwT1ngBNfKLU8Yj0TpZ6jCZMmACDj2ZBGBgYYPz48bhx40a2hCMios+Ty+Wwt7eHgYEBCteqDqddiyGYGiPhwk0E9hwPTWy81BGJ9EqmCyMLCwu8evXqk/bXr1/D3Nw8S6GIiCh9BEHAihUrcOPGDbRp0wbGNSvBec8SCGYmSLx0G4Hdx7E4IsqATBdG3bp1w6BBg7Br1y68fv0ar1+/xs6dOzF48GD06MGnPxMR5RZBEFCpUiXt+4SShfH38NaQWZgh8dodBHQdA01MnIQJifRHpm94sWjRIgiCgL59+yIlJQUAYGhoiB9++AHz58/PtoBERJR+sbGxaNKkCW7fvo0NcxegwaazSPr7HgI6e8Bp92LILdmjT/Q5WZquDwDx8fF49uwZAKBEiRIwMTHJlmB5FafrE1FeJooiRo0ahV27duHMmTMopVEgoLMHNJHRUFYtC6c9LI6oYErv93eWC6OChoUREeV1oigiICAAhQsXBgAk3fNFQCd3aCKioKz8JZz2LIHcmr+/qGDJkUeCeHp6Ys6cOTA1NYWnp+dn1+VDZImIpCEIgrYoAgA/gxT8PaQp6qw/haR/HiOgkzuc9y6F3Cb/3oyXKLMyVBjdvn0bycnJ2v9PiyAIWUtFRETZIiAgAA0bNkRwcDB2r/gF1VYfgeruUwR0dIfzvqWQ21pJHZEoT8n0pbRXr17hiy++gEymO7Htw3N8ihQpki0B8xpeSiMifaLRaDBs2DBcvXoVp0+fhkVYDAI6joY6NBKK8iXgvG8ZiyMqEHJ8jJFcLkdgYCDs7e112sPDw2Fvbw+1Wp2Z3eZ5LIyISN9oNBrExsZqf2epnrxAQPvRUIdGsDiiAiPHn5WWVj0VGxsLIyOjzO6WiIiymUwm0/kiOPP8Ea4ObQ55IRuo7j9714MU/la6gER5SIbvY/Rh0LUgCJg+fbrO9Hy1Wo1r166hSpUq2RaQiIiyz+3bt9GuXTuo1Wqc2rYTrnO3QfXgOQI6jn7Xc2RnLXVEIklluDD6MOhaFEXcvXsXCoVCu0yhUKBy5coYO3Zs9iUkIqJsU6lSJXTt2hXx8fGo3akt8PU3CGg/6n1x5A7n/SyOqGDL9BijAQMGYPny5QVunA3HGBGRvlOr1dBoNDA0NAQAqHxfIaD9KKiDw6EoV5w9R5Qv5fgYo02bNrEwICLSQ3K5XFsUAcAvRw/g3phOkDvYvus56uQOdVikhAmJpJPpZ6V98ODBA7x69QoqlUqnvW3btlndNRER5bCDBw/C3d0dRkZGuHnkBExGL9EWR877l3O2GhU4mS6Mnj9/jg4dOuDu3bsQBEE7S+3DzR3z63R9IqL8pFWrVmjTpg2++uorlG1UF8kHiyGg3cj/D8hmcUQFTKYvpY0ePRqurq4ICQmBiYkJ7t+/jwsXLqB69eo4d+5cNkYkIqKcolAosH//fsycOROCIEBRsgicD62E3N7m/5fVIqKkjkmUazJdGF25cgWzZ8+GnZ0dZDIZZDIZ6tSpAy8vL4waNSo7MxIRUQ4yMPj/xQO1Wo0xy3/Cm1kD3xVH958hoCOLIyo4Ml0YqdVqmJubAwDs7OwQEBAAAChatCgeP36cPemIiChXLV68GKtWrUL7oYNgtm3u+5tA+iKgkwfUkdFSxyPKcZkujCpUqIB//vkHAFCjRg0sXLgQly5dwuzZs1G8ePFsC0hERLln2LBhaNiwIdasWQO7ryrC+cAyyAtZQ3Xv6bvLaiyOKJ/LdGE0depUaDQaAMDs2bPh5+eHunXr4tixY1i+fHm2Bfwvq1evRrFixWBkZIQaNWrg+vXraa67efNmCIKg8+LjS4iI/s/MzAze3t7o3LkzAEDxpSucDyyHzM4KqrtPEdjFE+q3MRKnJMo5mS6Mmjdvjo4dOwIASpYsiUePHiEsLAwhISFo3LhxtgX8nF27dsHT0xMzZszArVu3ULlyZTRv3hwhISFpbmNhYYHAwEDt6+XLl7mSlYhIX3yYXQwAUVFR6DF5LOKWuENmZ4Wkfx4jsOsYqKNYHFH+lOnCqFGjRpg1a5ZOm42NDd6+fYtGjRplOVh6LFmyBEOGDMGAAQNQrlw5rFmzBiYmJti4cWOa2wiCAEdHR+3LwcEhV7ISEekjd3d37N+/Hz0neMBxzxLIbCyRdPshAruNhSYmTup4RNku04XRuXPn3g3Qa98ecXH//8uhUqlw/vz5bAn3OSqVCjdv3kSTJk20bTKZDE2aNMGVK1fS3C42NhZFixaFi4sL2rVrh/v37+d4ViIifbVgwQLUq1cPW7ZsgXGFUnDetwwyawsk3XyAgG5joYmNlzoiUbbKdGEEAKdPn0ZQUBBq1qyJFy9eZFOk9AkLC4Narf6kx8fBwQFBQUGpbvPll19i48aNOHToEH777TdoNBrUqlULb968SfM4SUlJiI6O1nkRERUU9vb2OHfuHKpWrQoAUFYoCee9SyGzMkfS3/fe9RyxOKJ8JEuFkZOTE86fP4+KFSvi66+/zvM3dnRzc0Pfvn1RpUoV1K9fH/v370ehQoXw66+/prmNl5cXLC0ttS8XF5dcTExEJL2Pxxz5+vqi3fhRMFg7FTJLMyRev4vAHuOhiUuQMCFR9sl0YfThL4pSqcSOHTswevRotGjRAj///HO2hfscOzs7yOVyBAcH67QHBwfD0dExXfswNDRE1apV4evrm+Y6kyZNQlRUlPb1+vXrLOUmItJXoiiiT58+OHHiBDxWL4HTniWQmZsi8eo/COw9EZr4RKkjEmVZpgujD89G+2Dq1KnYvn07Fi9enOVQ6aFQKFCtWjV4e3tr2zQaDby9veHm5paufajVaty9exdOTk5prqNUKmFhYaHzIiIqiARBwNatW9G0aVOsWrUKRlXLwmn3YghmJkj86xaC+k6CJiFJ6phEWZLpwsjPzw92dnY6bZ06dcLVq1c/OyssO3l6emLdunXYsmULHj58iB9++AFxcXEYMGAAAKBv376YNGmSdv3Zs2fj5MmTeP78OW7duoXevXvj5cuXGDx4cK7kJSLSd6VKlcLJkye1PfNG1cvDaedPEEyMkXD+BoL6T4EmkcUR6S+D/14ldUWLFk21vUKFCqhQoUKmA2VEt27dEBoaiunTpyMoKAhVqlTB8ePHtQOyX716BZns/7VfZGQkhgwZgqCgIFhbW6NatWq4fPkyypUrlyt5iYjymzNnzmDx4sXYumkWogZMR8KZawgeOA2Om+ZCUCqkjkeUYYL472tin+Hp6Yk5c+bA1NQUnp6en113yZIlWQ6XF0VHR8PS0hJRUVG8rEZEBVp8fDyKFy+O4OBgTJ8+HRObtUdgj3EQE5Jg0rIOHDfMgWCY6X9/E2Wr9H5/Z+gn9vbt20hOTtb+f1o+nsFARET5k4mJCfbt24fVq1dj8uTJUCqVcNw2H0G9JyD+z78Q/P0sOKydAcGAxRHpjwz1GH3s1atX+OKLL3QuVQHvBmW/fv0aRYoUyZaAeQ17jIiIPi/u9FUE9ZsMqJJh1qEx7H+eyuKIJJfe7+9MD752dXVFWFjYJ+0RERFwdXXN7G6JiEiP/fLLLxix/VfYb5gFGBog9oA3QkZ5QVSrpY5GlC7ZNl3/g9jYWD6xnoioAHr+/DlGjRqFzZs343hMEBzXzwIM5IjdcxKhHgshajRSRyT6Txnu2/ww6FoQBEyfPh0mJibaZWq1GteuXUOVKlWyLSAREemH4sWLY+vWrbh//z46d+4MQRDgsGYGgr+biZjfj0FQGMLupzEch0p5WoYLow+DrkVRxN27d6FQ/H86pkKhQOXKlTF27NjsS0hERHqjR48eOu9N2zaAfcpUhPwwB9FbDkEwNIDtj6NZHFGeleHC6OzZswCAAQMGYPny5RyATEREqdJoNBg+fDhcXV0xdMUkhI7yQtT6fYChAWxnDWdxRHlSpmelFVSclUZElD5HjhxBmzZtIJPJcO/ePRS+8RShnj8BAKxG9YLN1O9ZHFGuyfFZaQBw8eJF9O7dG25ubvD39wcAbNu2DX/99VdWdktERPnAt99+ixkzZuC3335D2bJlYdGnLezmewAA3q7YjshFm6UNSJSKTBdG+/btQ/PmzWFsbIzbt28jKends3GioqLw448/ZltAIiLSXzNnztQZd2QxsANs54wAAEQu3IjI5b9JFY0oVZkujObOnYs1a9Zg3bp1MDQ01LbXrl0bt27dypZwRESUf8TExKB58+a44GwMm6nfAwAi5v6Kt2t2SZyM6P8yfSvSx48fo169ep+0W1pa4u3bt1nJRERE+dDy5ctx6tQp+Pj44Pnz57BWJSNy4UaET1sFwdAQloM6Sh2RKPOFkaOjI3x9fVGsWDGd9r/++gvFixfPai4iIspnJk6cCD8/PwwdOhRmZmYQx/aHmKTC2+W/IWziUggKQ1j0aSN1TCrgMl0YDRkyBKNHj8bGjRshCAICAgJw5coVjB07FtOmTcvOjERElA8YGBhgw4YN2veCIMB68hCIqmRE/bILoWN+gqAwhHm3FhKmpIIu04XRxIkTodFo0LhxY8THx6NevXpQKpUYO3YsRo4cmZ0ZiYgoH3rx4gW6deuGDRs2wCEpGdEb9yNklBcEhSHMOjSWOh4VUFm+j5FKpYKvry9iY2NRrlw5mJmZZVe2PIn3MSIiyh6dOnXC/v37Ubt2bVw4fx5hYxch5rcjgFwOhw2zYdb603GsRJmV3u/vTPcYfaBQKFCuXLms7oaIiAqYdevWwcDAAEuWLIFMLkehxeMgqpIRu/sEgofMgLB5Hkyb1ZI6JhUwWeox8vb2hre3N0JCQqD511OTN27cmOVweRF7jIiIco4mORmhw+Yi9uAZCEoFHLcvgEn96lLHonwgx+98PWvWLDRr1gze3t4ICwtDZGSkzouIiCgjLl68iPqNGsFg7nCYtqoLMUmFoD4TkXDZR+poVIBkusfIyckJCxcuRJ8+fbI7U57GHiMiouyXnJyMsmXL4tmzZxg1ahSWLfwJQf2mIN77KgRTYzjvXQqj6uWljkl6LMd7jFQqFWrV4rVfIiLKOkNDQxw6dAi9e/fG/PnzISgVcNg0F8Z1v4IYl4DAbmOR9M9jqWNSAZDpwmjw4MHYsWNHdmYhIqICrHz58ti2bRuMjY0BADJjJRy2esGoRiVoomMR0MUTSQ+eSZyS8rtMz0pLTEzE2rVrcfr0aVSqVEnneWkAsGTJkiyHIyKigmvDhg04deoUtm77FSE9xiPp5gMEdvaA86GVUJQqKnU8yqcyPcaoYcOGn11+9uzZTAXK6zjGiIgo571+/RqlSpVCUlIStm7dip5t2iOg42io7j6F3NEOhQ+vgqFrYaljkh5J7/d3lm/wWNCwMCIiyh0HDhzA1atX3405EgSow9/Cv/0oJD/yg8EXDnD+YzUMv3CQOibpiRwvjDw9PVPfoSDAyMgIJUuWRLt27WBjY5OZ3edZLIyIiKQhiiLUIREIaDcSyc9ew6BYYRT+YxUMHO2kjkZ6IMcLo4YNG+LWrVtQq9X48ssvAQBPnjyBXC5HmTJl8PjxYwiCgL/++itf3RmbhRERUe7TaDTw8PBA4cKF4dm7P/zbjkDKy0AYlioK50MrYVDIWuqIlMfl+HT9du3aoUmTJggICMDNmzdx8+ZNvHnzBk2bNkWPHj3g7++PevXqwcPDI7OHICIiAgAcP34cK1aswMSJE/E4MhTO+5dD7myP5KcvEdjFA+rIaKkjUj6R6R6jwoUL49SpU5/0Bt2/fx/NmjWDv78/bt26hWbNmiEsLCxbwuYF7DEiIpLG1KlTUapUKfTr1w8AoHr2GgFtR0AdEgFllTJw2rcUcov8/SBzyrwc7zGKiopCSEjIJ+2hoaGIjn5XuVtZWUGlUmX2EERERFpz587VFkUAoCjhAuf9yyCztUSSzyMEdR8HTWy8hAkpP8jSpbSBAwfiwIEDePPmDd68eYMDBw5g0KBBaN++PQDg+vXrKF26dHZlJSIiAgDExcWhXbt2uPjmOZz3LIXM0gyJf99DYJ9J0CQkSR2P9FimL6XFxsbCw8MDW7duRUpKCgDAwMAA/fr1w9KlS2FqagofHx8AQJUqVbIrr+R4KY2ISHozZszA7NmzYW9vDz8/P8gevUBAJw+IsfEwblQDTlt/hKBUSB2T8pBcu49RbGwsnj9/DgAoXrw4zMzy9/VdFkZERNJLSkpC37594e7uDjc3NwBAwtU7COw2BmJ8Ikxb1YXD+tkQDDP9gAfKZ3iDxxzCwoiIKO+KP38DQb0mQExSwaxDY9j/Mg2CXC51LMoD0vv9neVS+sGDB3j16tUng6zbtm2b1V0TERGly+vXrzFw4EBs2LABDhvnIKj/FMQe8IagVKDQ8okQZJkeUksFTKYLo+fPn6NDhw64e/cuBEHAh44nQRAAAGq1OnsSEhER/YchQ4bg9OnTGDRoEE6dOgWHX2cgeMhMxOz8E4KxEnYLPLXfT0Sfk+kSevTo0XB1dUVISAhMTExw//59XLhwAdWrV8e5c+eyMSIREdHnrV+/Hs2bN8eGDRsAAGZtGsB+1WRAEBC96SDCZ6wGR45QemS6x+jKlSs4c+YM7OzsIJPJIJPJUKdOHXh5eWHUqFG4fft2duYkIiJK0xdffIHjx4/rtJl3bgYxUYVQjwWI+mUXZCZGsJk4WKKEpC8y3WOkVqthbm4OALCzs0NAQAAAoGjRonj8+HH2pCMiIsqEq1evokuXLlB0bgI7L3cAQOTiLYhctk3aYJTnZbrHqEKFCvjnn3/g6uqKGjVqYOHChVAoFFi7di2KFy+enRmJiIjSLSEhAR06dEBQUBDKlCmDOXPmQJOYhIhZvyBi3loIxkaw+r6L1DEpj8p0j9HUqVOh0WgAALNnz4afnx/q1q2LY8eOYcWKFdkWkIiIKCOMjY2xY8cOtG3bFhMnTgQAWI/oCevxAwEA4VNXIHrrYSkjUh6WrfcxioiIgLW1db4e+c/7GBER6SdRFBExZw3ertwBCALsV02GedcWUseiXJIr9zFKTEzEnTt3EBISou09+oD3MSIiorxi27ZtePLkCWbPng0xIQlR6/chZKQXBKUSZu0aSh2P8pBMF0bHjx9Hnz59EB4e/skyQRB4HyMiIsoTHj58iH79+kEURdSoUQOt542CJiERMduPInjoLAhGCpg2ry11TMojMj3GaOTIkejatSsCAwOh0Wh0XiyKiIgoryhbtiwWLFiAUaNGoVWrVhBkMhRaPA5mnZoCKWoEDZyG+HN/Sx2T8ohMjzGysLDA7du3UaJEiezOlKdxjBERkX4SRVFnDKyYkoLgwTMRd/Q8BGMlnHYugnGtKtIFpByV3u/vTPcYde7cmXe4JiIivfGhKBJFEZMmTcIfx47BYe0MmDSpCTEhCYE9xyPx5n2JU5LUMt1jFB8fjy5duqBQoUKoWLEiDA0NdZaPGjUqWwLmNewxIiLSb1u2bEH//v1hZGQEX19fONnYIajXeCRcvAWZpRmc9y+HslJpqWNSNkvv93emC6MNGzZg6NChMDIygq2trU73pCAIeP78eWZ2m+exMCIi0m8pKSno3r07WrZsiUGDBgEANLHxCOw2FonX70Jma4nCh1ZC8aWrxEkpO+V4YeTo6IhRo0Zh4sSJkMkyfUVO77AwIiLSf/8ebwQA6uhYBHbyQJLPI8jtbeB8eBUUJVwkSkjZLcfHGKlUKnTr1k3yomj16tUoVqwYjIyMUKNGDVy/fv2z6+/ZswdlypSBkZERKlasiGPHjuVSUiIiyis+LooSEhIwaNAg+IUEwWn3YijKl4A6JAKBndyR/DpIwpQkhUxXNf369cOuXbuyM0uG7dq1C56enpgxYwZu3bqFypUro3nz5ggJCUl1/cuXL6NHjx4YNGgQbt++jfbt26N9+/a4d+9eLicnIqK8YsyYMdi4ceO7GxNbmMJpz1IYliqKFP8QBHQcjZTAUKkjUi7K9KW0UaNGYevWrahcuTIqVar0yeDrJUuWZEvAz6lRowa+/vprrFq1CgCg0Wjg4uKCkSNHap+P87Fu3bohLi4OR44c0bbVrFkTVapUwZo1a9J1TF5KIyLKX4KDg/Htt99iyZIlqFu3LgAgJTAU/m1HIOVFAAxLFYXzoZUwKGQtcVLKihx/JMjdu3dRtWpVAPikxyU3npWmUqlw8+ZNTJo0Sdsmk8nQpEkTXLlyJdVtrly5Ak9PT5225s2b4+DBg2keJykpCUlJSdr30dHRWQtORER5ioODA65fv67z3WXgVAjO+5YhoO0IJD99icAuHnA+sAJya/6DOL/LdGF09uzZ7MyRYWFhYVCr1XBwcNBpd3BwwKNHj1LdJigoKNX1g4LSvobs5eWFWbNmZT0wERHlWR8XRQEBAZgxYwaWL18Op/fFker+MwR2HQOnfUshtzCTMCnltIIznSyTJk2ahKioKO3r9evXUkciIqIcIooi2rRpg/Xr12P48OFQlHCB8/5lkNlaIsnnEYJ6jIcmLkHqmJSD9LYwsrOzg1wuR3BwsE57cHAwHB0dU93G0dExQ+sDgFKphIWFhc6LiIjyJ0EQsHLlSlSsWBHTp08HACi+dIXz7iWQWZoh8fpdBPWZCE1C0n/sifSV3hZGCoUC1apVg7e3t7ZNo9HA29sbbm5uqW7j5uamsz4AnDp1Ks31iYio4KlVqxZ8fHzg6vr/GzwqK5WG065FEEyNkXDxFoIHTIWYpJIwJeUUvS2MAMDT0xPr1q3Dli1b8PDhQ/zwww+Ii4vDgAEDAAB9+/bVGZw9evRoHD9+HIsXL8ajR48wc+ZM3LhxAyNGjJDqIxARUR708T36bty4gZkzZ0L5VTk47VgIwViJeO+rCP5+FsSUFAlTUk7I9ODrvKBbt24IDQ3F9OnTERQUhCpVquD48ePaAdavXr3S+eGuVasWduzYgalTp2Ly5MkoVaoUDh48iAoVKkj1EYiIKA8LDQ1F48aNER0djS+++AKDBw+G41YvBPaagLijFxAyfB7sf54KQS6XOiplk0zfx6ig4n2MiIgKlhUrVuDAgQM4fPgwzM3NAQBxJy8hqN8UIEUN8x6tUGjZBAgF6PFY+ijHn5VWULEwIiIqeFJSUmBgoHuRJfbwWQQPmQloNLAY0AF2Czxy5T5+lDk5foNHIiKiguLjomjXrl2Qy+Xo3LkzxCQVQobPQ/SmAxCMlbCdOYzFkZ5jYURERJROFy5cQI8ePWBgYIASJUqgapfmEBNVCPVciKifd0JmpITNpMFSx6QsYGFERESUTrVr10bXrl1hbW2NypUrAwAs+rSBmKRC2KRliFyyBYKRAtYefSVOSpnFwoiIiCid5HI5fvvtN8jlcp1LZpaDO0GTmISIWb8g4sd1EIyVsBraTcKklFkcQk9ERJQBBgYG2qJIFEV4eXnh4cOHsB7RE9YTBgIAwqetQtTmgxKmpMxiYURERJRJy5Ytw+TJk9G0aVNER0fDekx/WI3sBQAIG7cY0b8fkzghZRQLIyIiokzq27cvKlSogClTpsDCwgKCIMBm2vewHNIZABDqvgAxB05LnJIygmOMiIiIMsnW1hY3b96EQqHQtgmCANt5oyAmqRC99TBCfpgLQaGAWet6Eial9GKPERERURZ8XBQlJSVh8uTJiImJgd1PY2DWtTmgViN4yAzEnboiYUpKLxZGRERE2WTAgAHw8vJCly5dAEGA/fKJMG3XCEhOQfCAqYg/f0PqiPQfWBgRERFlk7Fjx8LZ2Rnjx4+HIAgQDAzg8Ms0mLSsAzFJhaA+E5Fw5R+pY9Jn8FlpGcRnpRER0eckJibCyMhIp01MUiGo3xTEe1+FYGoM571LYVS9vEQJC6b0fn+zx4iIiCgbfVwUBQcHY86cORANDeCwaS6M61WDGJeAwG5jkfTPYwlTUlpYGBEREeUAlUqFhg0bYvr06Zg9ezZkxko4bvWCUY1K0ETHIqCLJ5IePJM6Jv0LCyMiIqIcoFAoMGHCBBQrVgy9e/cGAMhMjeH0+0Ioq5WDJjIaAZ3coXryQtqgpINjjDKIY4yIiCgjEhISYGxsrNOmjopBQEd3qO48gdzBFoUPr4Jh8S8kSlgwcIwRERFRHvBxUeTj44MtW7ZAbmkO5z1LoChXHOrgcAR0HI3kV4ESpqQPWBgRERHlghcvXqBBgwYYMGAAjhw5ArmNJZz2LIVhqaJI8Q9BQIfRSPEPljpmgcfCiIiIKBcULVoUPXv2RO3atVG3bl0AgIG9DZz3L4Oh6xdIeRWIgA7uSAkKkzhpwcYxRhnEMUZERJRZGo0GSUlJn4w5SvEPhn/bkUh5FQjD0kXhfHAlDApZS5Qyf+IYIyIiojxGJpPpFEUHDhzA5cuXYVDYAc77l0HubI/kJy8R2Nkd6ogoCZMWXCyMiIiIJHDixAl07twZrVq1gq+vLwyLOr8rjhxsoXrwHAGdPaB+GyN1zAKHhREREZEE6tati1q1aqFTp05wdXUFAChKuLwrjgpZQ3X3KQK7jYEmJk7ipAULxxhlEMcYERFRdomLi4OxsTFkMt1+iqQHzxDQYTQ0EVEw+roCnHYvhszMRKKU+QPHGBEREeVxpqam2qJIFEUsX74c/v7+UJYrAec9SyCzNEPi3/cQ2GsCNPGJEqctGFgYERER5QGLFi2Cu7s7mjRpgoSEBCgrlYbTniWQmZsi8bIPgvpMhCYhSeqY+R4LIyIiojyga9euKFKkCIYOHaqduWZUtSycdi2CYGqMhAs3EdR/CjSJLI5yEscYZRDHGBERUU6JjY2FmZnZJ+0Jl30Q2GMcxPhEmDSrBcdNcyEoDCVIqL84xoiIiEjPfFwUqVQqzJ07991DaGtVgeNv8yEYKRB/8jKCv5sJMTlFwqT5FwsjIiKiPKhfv36YNm0aevfuDQAwqVsNjlu9ICgViDt6AcFDZ0NMYXGU3VgYERER5UE//PADbG1t8d1332nbTBp+A4dNcwFDA8QdPouQ4fMgqtUSpsx/OMYogzjGiIiIcktaY47ijv+FoAFTgRQ1zLo2h/2KSRDkcgkS6g+OMSIiItJzHxdFoaGhWLhwIURRhGmLOnBYNwuQyxG7+wRCPRZC1GgkTJp/sDAiIiLK41QqFRo1aoQJEyZgzpw5AACzb+vDYc10QCZDzO/HEDp2EYujbMDCiIiIKI9TKBQYPXo0XFxc0K1bN227WftGsP956rviaNsfCJu4FBwhkzUcY5RBHGNERERSSWvMUczu4wgZ8SMgirAc3Am2P46GIAgSJMy7OMaIiIgon/m4KLp79y42bNgAADDv2gKFlk8EBAFR6/chfNpK9hxlkoHUAYiIiChj3rx5gwYNGiAiIgLW1tbo2LEjLHq0AtQahHosQNSvewADOWxnDGPPUQaxx4iIiEjPFC5cGH379kWNGjXQsGFDbbtF729RaPE4AEDU6p2ImPMre44yiD1GREREekYQBCxZsgQJCQkwMTHRWWbRty3EFDXCJizB25XbAbkMNpOHsOcondhjREREpIcEQdApig4fPowTJ04AACwHdoCdlzsA4O2ybYicv4E9R+nEHiMiIiI9d/HiRXTs2BGGhoa4evUqKleuDMvBnSCqNQifugKRS7a86zkaP1DqqHkeCyMiIiI9V6NGDbRs2RJWVlYoX768tt3q+y6ARo3w6asR+dMmQCbAZuwACZPmfSyMiIiI9JxCocDevXthYGAA+b+emWb1Q3dAIyJ85s+IXLARgiCD9Zh+EiXN+zjGiIiIKB9QKpU6RdHSpUtx584dAIDV8B6wmTYUABAxfz0il2yVJKM+YI8RERFRPrNu3Tp4enqiUKFCePDgAezs7GA9qheg0SBi3lpEeK0DBMDao6/UUfMc9hgRERHlM126dEHVqlUxduxY2NnZadut3fvAZsp3AICIH9chctk2qSLmWewxIiIiymesrKxw5coVKJXKT5ZZu/cBRBERP65DxLy1/28jAHrcYxQREYFevXrBwsICVlZWGDRoEGJjYz+7TYMGDSAIgs5r6NChuZSYiIgo93xcFCUnJ2PMmDF48+YNgHeX0GwmDQEARMxby56jj+htYdSrVy/cv38fp06dwpEjR3DhwgV89913/7ndkCFDEBgYqH0tXLgwF9ISERFJZ9y4cViyZAlatmyJlJQUAIC1Z1/YTBwMgMXRx/TyUtrDhw9x/Phx/P3336hevToAYOXKlWjVqhUWLVoEZ2fnNLc1MTGBo6NjbkUlIiKSnKenJ44fP4758+fDwOD/X/0fpu1HzF/Py2rv6WWP0ZUrV2BlZaUtigCgSZMmkMlkuHbt2me33b59O+zs7FChQgVMmjQJ8fHxn10/KSkJ0dHROi8iIiJ9UqRIEdy7dw+tW7f+ZJn1mH68rPYRvSyMgoKCYG9vr9NmYGAAGxsbBAUFpbldz5498dtvv+Hs2bOYNGkStm3bht69e3/2WF5eXrC0tNS+XFxcsuUzEBER5aaPe4oiIiIwaNAgvH37FsD7y2ofF0dLC+59jvLUpbSJEydiwYIFn13n4cOHmd7/x2OQKlasCCcnJzRu3BjPnj1DiRIlUt1m0qRJ8PT01L6Pjo5mcURERHqte/fuOHXqFEJCQvDHH38AeFccQXg3jT/ix3WARiyQd8jOU4XRmDFj0L9//8+uU7x4cTg6OiIkJESnPSUlBRERERkaP1SjRg0AgK+vb5qFkVKpTHW6IxERkb5atGgRevbsifnz5+u0W3v0BQTh3U0g56+HKGoK3LPV8lRhVKhQIRQqVOg/13Nzc8Pbt29x8+ZNVKtWDQBw5swZaDQabbGTHj4+PgAAJyenTOUlIiLSR5UqVcKdO3cgk306osbavQ8gkyFizhpELtgIaETYjB8oQUpp6OUYo7Jly6JFixYYMmQIrl+/jkuXLmHEiBHo3r27dkaav78/ypQpg+vXrwMAnj17hjlz5uDmzZt48eIFDh8+jL59+6JevXqoVKmSlB+HiIgo131cFD18+BC9e/dGQkICAMB6VC/YzPgBABD506b3vUeiJDlzW57qMcqI7du3Y8SIEWjcuDFkMhk6deqEFStWaJcnJyfj8ePH2llnCoUCp0+fxrJlyxAXFwcXFxd06tQJU6dOleojEBERSS45ORnffvstnj9/Djs7OyxbtgwAYD2iJwSZDOEzViNy8RaIGhE2kwZDEARpA+cwQSwoJWA2iY6OhqWlJaKiomBhYSF1HCIioiy7cOECpk6digMHDsDW1lZn2ds1uxA+bRUAwGpkL9hM+14vi6P0fn+zMMogFkZERJQfiaKYZsETtW4vwiYvBwBYDusO25nD9K44Su/3t16OMSIiIqLs9XGhc+zYMfTu3RvJyckAAMshnWG34N2ta6J+3onwaSvz7ZgjvR1jRERERNkvIiIC3bt3R0xMDL7++muMHj0aAGA5sAMEAzlCx/yEqF/3QExRw87LXe96jv4Le4yIiIhIy8bGBjt27ECvXr0wbNgwnWUWfdui0NIJgCAgesN+hI1bDFGjkShpzuAYowziGCMiIiqIPh6DFL3zT4SO8gJEEeY9W6PQknEQ5HKJE34exxgRERFRtvjpp58wcOBAaN73Dll0bwn7X6YBMhlidhxFyCgviGq1xCmzB8cYERERUZqePHmCSZMmQa1Wo3379mjXrh0AwLxTUwhyOYKHzkbs7hNAihr2q6dAMNDv0kK/0xMREVGOKl26NHbs2IHHjx9ri6IPzNo3AgzkCB4yA7H7T0NUJcPh1xkQFIYSpc06jjHKII4xIiKigk6tVkMQBO1jReJOXELQwGmAKhkmzWvDccNsCEqFxCl1cYwRERERZTu1Wo3+/ftj2LBh2jFHps1rw2mrFwQjBeJPXEJg38nQJCRJnDRzWBgRERFRul2+fBnbt2/H+vXrcfv2bW27SeMacNyxEIKJERLOXENQr/HQxCVImDRzWBgRERFRutWtWxdbtmzBrl27UK1aNZ1lJnWrwWnnIgimxki4eAuB3cZCExMnUdLM4RijDOIYIyIiIl1xcXEwNjbWjjlKvHH/XVEUHQvlV2XhtGsx5FbmkmbkGCMiIiLKcVFRUWjcuDFGjBihHXNkVL08nPcvg8zGEkm3HiKgw2iowyIlTpo+LIyIiIgo0y5cuIDr169j165dePPmjbZdWflLOB9cAXkha6juPYV/+1FICQqTMGn6sDAiIiKiTGvTpg1+++03eHt7o0iRIjrLlGWLw/nwKsidCiH58QsEtBuJFP9giZKmDwsjIiIiypKePXuiSpUq2veBgYHay2qKkkVQ+PAqGBRxQvLzN/BvMwLJfv4SJf1vLIyIiIgo2zx9+hTVq1fH999/ry2ODIs5o/DhlTAs/gVSXgfBv81wqJ68kDZoGlgYERERUbbx8fFBUFAQLl++jKioKG27QWEHOB9eBUXZ4lAHh8O/3Ugk3X0qYdLUsTAiIiKibNOlSxfs378fZ86cgbW1tc4yAwdbOB9cAWXlL6EJe4uADqOQeOO+RElTx8KIiIiIslW7du3g4OCgff/PP/9ArVYDAOQ2lnDavwxGX1eAJioWAZ08kPDXLamifoKFEREREeWYM2fOoGbNmujbty9SUlIAAHILMzjtWQLjetUgxicgsMc4xJ28LHHSd1gYERERUY6JiopCSkoKoqOjtYOxAUBmagzH7Qtg0qIOxEQVgvpNRuyhsxImfZ9L6gBERESUf3Xo0AFnz57F3r17oVAodJbJjJRw3DgHZh2bAClqBH83E9Hbj0qU9H0mSY9ORERE+V6dOnWgVCq17w8fPoykpCQAgGBoAPufp8K8TxtAo0Go+3xE/35MqqgsjIiIiCj3rFmzBu3atUPHjh2RnJwMABDkchRaPA6WP3SD3NkexrWrSpaPhRERERHlmpIlS8LY2BgVK1aEgYGBtl0QBNjOGg6XMxtgWMRJsnwG/70KERERUfZo0qQJ7ty5gxIlSkAQBJ1lgiBAbmslTbD3WBgRERFRripZsqTUEdLES2lERERE77EwIiIiInqPhRERERHReyyMiIiIiN5jYURERET0HgsjIiIiovdYGBERERG9x8KIiIiI6D0WRkRERETvsTAiIiIieo+FEREREdF7LIyIiIiI3mNhRERERPQeCyMiIiKi91gYEREREb1nIHUAfSOKIgAgOjpa4iRERESUXh++tz98j6eFhVEGxcTEAABcXFwkTkJEREQZFRMTA0tLyzSXC+J/lU6kQ6PRICAgAObm5hAEIdv2Gx0dDRcXF7x+/RoWFhbZtl/6FM917uB5zh08z7mD5zl35OR5FkURMTExcHZ2hkyW9kgi9hhlkEwmwxdffJFj+7ewsOBfulzCc507eJ5zB89z7uB5zh05dZ4/11P0AQdfExEREb3HwoiIiIjoPRZGeYRSqcSMGTOgVCqljpLv8VznDp7n3MHznDt4nnNHXjjPHHxNRERE9B57jIiIiIjeY2FERERE9B4LIyIiIqL3WBgRERERvcfCKBetXr0axYoVg5GREWrUqIHr169/dv09e/agTJkyMDIyQsWKFXHs2LFcSqrfMnKe161bh7p168La2hrW1tZo0qTJf/650P9l9Gf6g507d0IQBLRv3z5nA+YTGT3Pb9++xfDhw+Hk5ASlUonSpUvz90c6ZPQ8L1u2DF9++SWMjY3h4uICDw8PJCYm5lJa/XThwgW0adMGzs7OEAQBBw8e/M9tzp07h6+++gpKpRIlS5bE5s2bczakSLli586dokKhEDdu3Cjev39fHDJkiGhlZSUGBwenuv6lS5dEuVwuLly4UHzw4IE4depU0dDQULx7924uJ9cvGT3PPXv2FFevXi3evn1bfPjwodi/f3/R0tJSfPPmTS4n1z8ZPdcf+Pn5iYULFxbr1q0rtmvXLnfC6rGMnuekpCSxevXqYqtWrcS//vpL9PPzE8+dOyf6+PjkcnL9ktHzvH37dlGpVIrbt28X/fz8xBMnTohOTk6ih4dHLifXL8eOHROnTJki7t+/XwQgHjhw4LPrP3/+XDQxMRE9PT3FBw8eiCtXrhTlcrl4/PjxHMvIwiiXfPPNN+Lw4cO179Vqtejs7Cx6eXmlun7Xrl3F1q1b67TVqFFD/P7773M0p77L6Hn+t5SUFNHc3FzcsmVLTkXMNzJzrlNSUsRatWqJ69evF/v168fCKB0yep5/+eUXsXjx4qJKpcqtiPlCRs/z8OHDxUaNGum0eXp6irVr187RnPlJegqj8ePHi+XLl9dp69atm9i8efMcy8VLablApVLh5s2baNKkibZNJpOhSZMmuHLlSqrbXLlyRWd9AGjevHma61PmzvO/xcfHIzk5GTY2NjkVM1/I7LmePXs27O3tMWjQoNyIqfcyc54PHz4MNzc3DB8+HA4ODqhQoQJ+/PFHqNXq3IqtdzJznmvVqoWbN29qL7c9f/4cx44dQ6tWrXIlc0EhxXchHyKbC8LCwqBWq+Hg4KDT7uDggEePHqW6TVBQUKrrBwUF5VhOfZeZ8/xvEyZMgLOz8yd/EUlXZs71X3/9hQ0bNsDHxycXEuYPmTnPz58/x5kzZ9CrVy8cO3YMvr6+GDZsGJKTkzFjxozciK13MnOee/bsibCwMNSpUweiKCIlJQVDhw7F5MmTcyNygZHWd2F0dDQSEhJgbGyc7cdkjxHRe/Pnz8fOnTtx4MABGBkZSR0nX4mJiUGfPn2wbt062NnZSR0nX9NoNLC3t8fatWtRrVo1dOvWDVOmTMGaNWukjpavnDt3Dj/++CN+/vln3Lp1C/v378fRo0cxZ84cqaNRFrHHKBfY2dlBLpcjODhYpz04OBiOjo6pbuPo6Jih9Slz5/mDRYsWYf78+Th9+jQqVaqUkzHzhYye62fPnuHFixdo06aNtk2j0QAADAwM8PjxY5QoUSJnQ+uhzPxMOzk5wdDQEHK5XNtWtmxZBAUFQaVSQaFQ5GhmfZSZ8zxt2jT06dMHgwcPBgBUrFgRcXFx+O677zBlyhTIZOx3yA5pfRdaWFjkSG8RwB6jXKFQKFCtWjV4e3tr2zQaDby9veHm5pbqNm5ubjrrA8CpU6fSXJ8yd54BYOHChZgzZw6OHz+O6tWr50ZUvZfRc12mTBncvXsXPj4+2lfbtm3RsGFD+Pj4wMXFJTfj643M/EzXrl0bvr6+2sITAJ48eQInJycWRWnIzHmOj4//pPj5UIyKfARptpHkuzDHhnWTjp07d4pKpVLcvHmz+ODBA/G7774TraysxKCgIFEURbFPnz7ixIkTtetfunRJNDAwEBctWiQ+fPhQnDFjBqfrp0NGz/P8+fNFhUIh7t27VwwMDNS+YmJipPoIeiOj5/rfOCstfTJ6nl+9eiWam5uLI0aMEB8/fiweOXJEtLe3F+fOnSvVR9ALGT3PM2bMEM3NzcXff/9dfP78uXjy5EmxRIkSYteuXaX6CHohJiZGvH37tnj79m0RgLhkyRLx9u3b4suXL0VRFMWJEyeKffr00a7/Ybr+uHHjxIcPH4qrV6/mdP38ZOXKlWKRIkVEhUIhfvPNN+LVq1e1y+rXry/269dPZ/3du3eLpUuXFhUKhVi+fHnx6NGjuZxYP2XkPBctWlQE8MlrxowZuR9cD2X0Z/pjLIzSL6Pn+fLly2KNGjVEpVIpFi9eXJw3b56YkpKSy6n1T0bOc3Jysjhz5kyxRIkSopGRkeji4iIOGzZMjIyMzP3geuTs2bOp/s79cG779esn1q9f/5NtqlSpIioUCrF48eLipk2bcjSjIIrs8yMiIiICOMaIiIiISIuFEREREdF7LIyIiIiI3mNhRERERPQeCyMiIiKi91gYEREREb3HwoiIiIjoPRZGRERERO+xMCKifKdBgwZwd3eXOgYR6SHe+ZqI9FqDBg1QpUoVLFu2TNsWEREBQ0NDmJub53oeDw8PvHz5Evv378/1YxNR1rHHiIjyHRsbG0mKIgC4fv06qlevLsmxiSjrWBgRkd7q378/zp8/j+XLl0MQBAiCgBcvXnxyKa1BgwYYOXIk3N3dYW1tDQcHB6xbtw5xcXEYMGAAzM3NUbJkSfz555/abTQaDby8vODq6gpjY2NUrlwZe/fuTTOLSqWCoaEhLl++jClTpkAQBNSsWTMnPz4R5QAWRkSkt5YvXw43NzcMGTIEgYGBCAwMhIuLS6rrbtmyBXZ2drh+/TpGjhyJH374AV26dEGtWrVw69YtNGvWDH369EF8fDwAwMvLC1u3bsWaNWtw//59eHh4oHfv3jh//nyq+zcwMMClS5cAAD4+PggMDMTx48dz5oMTUY7hGCMi0mupjTH6d1uDBg2gVqtx8eJFAIBarYalpSU6duyIrVu3AgCCgoLg5OSEK1euoGrVqrCxscHp06fh5uam3e/gwYMRHx+PHTt2pJrl4MGDGDx4MMLCwnLmwxJRjjOQOgARUW6oVKmS9v/lcjlsbW1RsWJFbZuDgwMAICQkBL6+voiPj0fTpk119qFSqVC1atU0j3H79m1Urlw5m5MTUW5iYUREBYKhoaHOe0EQdNoEQQDwbmxRbGwsAODo0aMoXLiwznZKpTLNY/j4+LAwItJzLIyISK8pFAqo1eps3We5cuWgVCrx6tUr1K9fP93b3b17F506dcrWLESUu1gYEZFeK1asGK5du4YXL17AzMwMNjY2Wd6nubk5xo4dCw8PD2g0GtSpUwdRUVG4dOkSLCws0K9fv1S302g0ePz4MQICAmBqagpLS8ssZyGi3MVZaUSk18aOHQu5XI5y5cqhUKFCePXqVbbsd86cOZg2bRq8vLxQtmxZtGjRAkePHoWrq2ua28ydOxebN29G4cKFMXfu3GzJQUS5i7PSiIiIiN5jjxERERHReyyMiIiIiN5jYURERET0HgsjIiIiovdYGBERERG9x8KIiIiI6D0WRkRERETvsTAiIiIieo+FEREREdF7LIyIiIiI3mNhRERERPQeCyMiIiKi9/4HuBesQJbRxkwAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -769,7 +759,8 @@
     "plt.xlabel(\"time $t$\")\n",
     "plt.ylabel(r\"magnetization $\\langle Z_1 Z_2 \\rangle$\")\n",
     "plt.title(\"Magnetization over time\")\n",
-    "plt.legend(loc=\"best\")"
+    "plt.legend(loc=\"best\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -867,7 +858,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -916,7 +907,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
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@@ -952,7 +943,7 @@
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-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><td><code>qiskit_aer</code></td><td>0.13.3</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.0</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td colspan='2'>Mon Feb 19 11:21:43 2024 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>2.0.2</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.4.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.13.3</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td colspan='2'>Tue Jun 17 10:31:27 2025 CEST</td></tr></table>"
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@@ -964,7 +955,7 @@
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      "data": {
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-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -999,7 +990,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/11_VarQTE.ipynb b/docs/tutorials/11_VarQTE.ipynb
index dd6ba1b9..a00d104d 100644
--- a/docs/tutorials/11_VarQTE.ipynb
+++ b/docs/tutorials/11_VarQTE.ipynb
@@ -115,7 +115,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 538.128x200.667 with 1 Axes>"
       ]
@@ -126,10 +126,10 @@
     }
    ],
    "source": [
-    "from qiskit.circuit.library import EfficientSU2\n",
+    "from qiskit.circuit.library import efficient_su2\n",
     "\n",
-    "ansatz = EfficientSU2(hamiltonian.num_qubits, reps=1)\n",
-    "ansatz.decompose().draw(\"mpl\")"
+    "ansatz = efficient_su2(hamiltonian.num_qubits, reps=1)\n",
+    "ansatz.draw(\"mpl\")"
    ]
   },
   {
@@ -222,10 +222,10 @@
    "outputs": [],
    "source": [
     "from qiskit_algorithms import VarQITE\n",
-    "from qiskit.primitives import Estimator\n",
+    "from qiskit.primitives import StatevectorEstimator\n",
     "\n",
-    "var_qite = VarQITE(ansatz, init_param_values, var_principle, Estimator())\n",
-    "# an Estimator instance is necessary, if we want to calculate the expectation value of auxiliary operators.\n",
+    "var_qite = VarQITE(ansatz, init_param_values, var_principle, StatevectorEstimator())\n",
+    "# an EstimatorV2 instance is necessary, if we want to calculate the expectation value of auxiliary operators.\n",
     "evolution_result = var_qite.evolve(evolution_problem)"
    ]
   },
@@ -305,7 +305,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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6aw4cOMD777/Phg0bAGjRogUHDx4kKSmJ48ePU1RUVOf7X1EqT24qrEkbAKLsGTidTpPTiIiIJ1i5ciWxsbFllt69e/PEE0/w008/8dprrwEQGxvL66+/zkMPPcT27duxWCwsXbqUrVu30qFDB6ZMmcIzzzxTZtu+vr589tlnREVFMWjQIDp27MiTTz6Jl1fpacshQ4YwcOBArrnmGiIjI/n3v/9d5/tfUYZTn7w1Ljc3l9DQUHJycggJCamV9zidfwr/Z0rvcZcz6UdCG0fXyvuIiEjFFRYWcvDgQVq2bImfn9+FXyB17ny/o4p+fuuCcTflHxjMa5Zb+KkoiP/LKSa0sdmJREREGgadtnNjn0WOZom9L4fyvC48WERERGqEypMba/7zbVpSssqZTE1ERERqnE7bubHWIXa6GXsgJQcof14OERERqTk68uTGLiv6lvess7kq9TWzo4iIyK/ou1j1V038blSe3FhQbOnRpvDi88xcKyIidcbHxweAggJdTlFfnfndnPldVYVO27mxyPgEAKIdx7AVF+HjazU5kYhIw+bl5UVYWBiZmZkABAQEYBiGyakESo84FRQUkJmZSVhYmGt+qapQeXJjjWPiKXT64GfYSE/ZS9PWHcyOJCLS4MXExAC4CpTUL2FhYa7fUVWpPLkxw2IhwyuGFo7DnEz9UeVJRKQeMAyD2NhYoqKisNlsZseRX/Hx8anWEacz3LY8ZWVlMWnSJD766CMsFgtDhgzhhRdeICgoqNzxjzzyCJ999hkpKSlERkYyePBgHnvsMUJDQ13jznV49d///jfDhg2rtX2pjmxrUzh9mIKj+82OIiIiv+Ll5VUjH9RS/7hteRoxYgTp6emsWrUKm83GmDFjGD9+PEuWLDnn+LS0NNLS0nj22Wdp3749P/30E3fddRdpaWm89957ZcYuXLiQgQMHuh6HhYXV5q5US2FQPJzegDProNlRREREGgS3LE+7d+9m5cqVbN68mW7dugHw0ksvMWjQIJ599lni4uLOek2HDh14//33XY8vuuginnjiCUaOHElJSQne3r/8KGrifGhdOdH8Wh5KC8Tf53IuNzuMiIhIA+CWUxVs2LCBsLAwV3EC6NevHxaLhY0bN1Z4O2du/Pfr4gRw9913ExERQffu3VmwYMEF54QoKioiNze3zFJX/C66gn/Zf8/XeU3q7D1FREQaMrc88pSRkUFUVFSZdd7e3oSHh5ORkVGhbRw/fpzHHnuM8ePHl1k/e/Zsfve73xEQEMBnn33Gn//8Z/Ly8rjnnnvK3dbcuXOZNWtW5XekBjT7+RYth7MKcDqd+kqsiIhILatXR56mT5+OYRjnXfbs2VPt98nNzeW6666jffv2PProo2Wee/jhh7niiivo3LkzDzzwAPfffz/PPPPMebc3Y8YMcnJyXMvhw4ernbGi4sMD6GL8SD/bWrKzT9bZ+4qIiDRU9erI07Rp0xg9evR5x7Rq1YqYmJiz5s8oKSkhKyvrgtcqnTp1ioEDBxIcHMzy5csvOMNojx49eOyxxygqKsJqPfcklFartdznapufjxfzrS8SRRY/HhxIo0Z9TMkhIiLSUNSr8hQZGUlkZOQFx/Xs2ZPs7Gy2bt1K165dAVizZg0Oh4MePXqU+7rc3FwGDBiA1Wrlww8/xM/P74LvlZSURKNGjUwrRxVxwieWKFsWuel7gT5mxxEREfFo9ao8VVS7du0YOHAg48aNY/78+dhsNiZOnMiwYcNc37RLTU2lb9++vP3223Tv3p3c3Fz69+9PQUEB//rXv8pc2B0ZGYmXlxcfffQRR48e5fLLL8fPz49Vq1YxZ84c7rvvPjN394JOBcRDzk5Kjmm6AhERkdrmluUJYPHixUycOJG+ffu6Jsl88cUXXc/bbDaSk5NdNwDctm2b65t4rVu3LrOtgwcP0qJFC3x8fHjllVeYMmUKTqeT1q1b89xzzzFu3Li627EqsIc2gxyw5BwyO4qIiIjHM5wX+h6+VFpubi6hoaGuqRBq2+b/vspl301np++lXPLg+lp/PxEREU9U0c/vevVtO6ma4Lg2AIQXp5mcRERExPOpPHmAxvEXAxDlPE5xUaHJaURERDybypMHiIhqyqOO27nddj+p2SpPIiIitUnlyQMYFgvfNBrMOselpOTYzI4jIiLi0VSePMSZ27SkZBWYnERERMSzqTx5iMTAHAZbvsLrwFqzo4iIiHg0lScP0d22mXm+f6dd6jKzo4iIiHg0lScP4R/dCoCQwlSTk4iIiHg2lScPEdakdLqCmJJ0nA6HyWlEREQ8l8qTh4hudjEOp0GgUUj2iQyz44iIiHgslScP4ecfyDEjHIDMn/aYnEZERMRzqTx5kBM+cQCcSt9rchIRERHPpfLkQfIC4wGwHT9gchIRERHP5W12AKk5h1oN5+/fdqCFbw96mh1GRETEQ+nIkwfxb96NLxyd+CE3wOwoIiIiHkvlyYO0aBwIwE8ndIsWERGR2qLTdh6kWbg/N1q+ofnpDPJOdSMoOMzsSCIiIh5HR548SGiAL7N832aaz3scPbjT7DgiIiIeSeXJw2R6l05XkJP6o8lJREREPJPKk4c5FVA6XUFR5j6Tk4iIiHgmlScPYwtrCYBX9kGTk4iIiHgmlScP4x1xEQCB+SkmJxEREfFMKk8eJjjuYgAiitNMTiIiIuKZVJ48THTzdqX/5QSFBXkmpxEREfE8mufJw4Q1jubPTCe5qDGv5pRwsSYbFxERqVE68uRhDIuFw42vZL+zCYeyCs2OIyIi4nFUnjxQ88alh5t0mxYREZGap/Lkgbr5pfNnrw8I2/u+2VFEREQ8jsqTB7rE2M/9Pu+SkPmJ2VFEREQ8jsqTBwqMLZ2uoHFxqslJREREPI/KkweKavbzdAWOTIqLdNG4iIhITXLb8pSVlcWIESMICQkhLCyMsWPHkpd3/nmN+vTpg2EYZZa77rqrzJiUlBSuu+46AgICiIqK4i9/+QslJSW1uSs1rnFMPAVOK16Gk4wU3SBYRESkJrntPE8jRowgPT2dVatWYbPZGDNmDOPHj2fJkiXnfd24ceOYPXu263FAwC8TIdntdq677jpiYmL45ptvSE9P57bbbsPHx4c5c+bU2r7UNMNi4ahXLC0dhzh5OJlmbRLNjiQiIuIx3PLI0+7du1m5ciX/+Mc/6NGjB7179+all15i6dKlpKWd/7YkAQEBxMTEuJaQkBDXc5999hm7du3iX//6F506deLaa6/lscce45VXXqG4uLi2d6tGZfs3BeD00b0mJxEREfEsblmeNmzYQFhYGN26dXOt69evHxaLhY0bN573tYsXLyYiIoIOHTowY8YMCgp+mQtpw4YNdOzYkejoaNe6AQMGkJuby86dO8vdZlFREbm5uWUWsxWFNC/9Q9ZBc4OIiIh4GLc8bZeRkUFUVFSZdd7e3oSHh5ORkVHu6/7v//6P5s2bExcXx/fff88DDzxAcnIy//nPf1zb/XVxAlyPz7fduXPnMmvWrKruTq04mnAb/Q5dSiu/9lxudhgREREPUq/K0/Tp03nqqafOO2b37t1V3v748eNdf+7YsSOxsbH07duX/fv3c9FFF1V5uzNmzGDq1Kmux7m5ucTHx1d5ezUhsklr9jlP4DhpNzWHiIiIp6lX5WnatGmMHj36vGNatWpFTEwMmZmZZdaXlJSQlZVFTExMhd+vR48eAOzbt4+LLrqImJgYNm3aVGbM0aNHAc67XavVitVqrfD71oXmEYEAHD5ZQIndgbeXW56hFRERqXfqVXmKjIwkMjLyguN69uxJdnY2W7dupWvXrgCsWbMGh8PhKkQVkZSUBEBsbKxru0888QSZmZmu04KrVq0iJCSE9u3bV3JvzBUb4sedPv+jpfMwmaltiWtW9SNrIiIi8gu3PBzRrl07Bg4cyLhx49i0aRNff/01EydOZNiwYcTFxQGQmppKQkKC60jS/v37eeyxx9i6dSuHDh3iww8/5LbbbuOqq64iMbH0q/z9+/enffv23HrrrWzfvp1PP/2Uhx56iLvvvrveHVm6EIvF4P981jLM+wtOHPrB7DgiIiIewy3LE5R+ay4hIYG+ffsyaNAgevfuzeuvv+563mazkZyc7Po2na+vL59//jn9+/cnISGBadOmMWTIED766CPXa7y8vFixYgVeXl707NmTkSNHctttt5WZF8qdZFubAJCfoekKREREakq9Om1XGeHh4eedELNFixY4nU7X4/j4eNatW3fB7TZv3pxPPvGMG+oWBjeH0xtxnjhgdhQRERGP4bZHnuTCjPBWAPid+snkJCIiIp5D5cmD+ce0ASCs8LDJSURERDyHypMHa9ys9BuCsfZ07HbN9yQiIlITVJ48WHSzNhQ7vfAzbBw9otu0iIiI1ASVJw/m5e3DnUEvkVC4kH1FoWbHERER8QgqTx7OO7othVg5eDzf7CgiIiIeQeXJw7X6+TYtKk8iIiI1w23neZKK6eR7hDne/yBkXxTwmtlxRERE3J7Kk4drHlDEtd5rOHwqzuwoIiIiHkGn7TxcVPNLAIh1ZFBcVGRyGhEREfen8uThGsc2p8BpxdtwkP7THrPjiIiIuD2VJw9nWCxkeJeesstK2W1yGhEREfen8tQAZPs3A6AwI9nkJCIiIu5P5akBKA4rvUGwkbXf5CQiIiLuT+WpAfCJbF36h4IT5gYRERHxAJqqoAHw6Xgzl2yIIcg3jI1mhxEREXFzOvLUADSPiSQff47mFpFfVGJ2HBEREbem8tQAhAb4EB7oC+g2LSIiItWl8tRA3OO/kn/6zCFv56dmRxEREXFrKk8NREevFK702oE9bbvZUURERNyaylMDUdKodLoC75OarkBERKQ6VJ4aCN+oiwEIzv/J5CQiIiLuTeWpgQhrmgBAdEkqTqfT5DQiIiLuS+WpgYhpeQkAjckh+6QmyxQREakqlacGwj84jGM0AiDj4A6T04iIiLgvlacG5LhvU445QziWmWF2FBEREbel8tSALE14gcuK5rPB6GR2FBEREbel8tSAtIgOB2B/Zp7JSURERNyXylMDclFkEAD7j6k8iYiIVJW32QGk7rQOsfGWz5M0zT2OzfY9Pj4+ZkcSERFxOzry1IDERETR07KLi4w00lL2mh1HRETELbltecrKymLEiBGEhIQQFhbG2LFjycsr/3TUoUOHMAzjnMuyZctc4871/NKlS+til2qdxdubNO8mAJw4+IPJaURERNyT2562GzFiBOnp6axatQqbzcaYMWMYP348S5YsOef4+Ph40tPTy6x7/fXXeeaZZ7j22mvLrF+4cCEDBw50PQ4LC6vx/GY56d+CFnk/cTp9j9lRRERE3JJblqfdu3ezcuVKNm/eTLdu3QB46aWXGDRoEM8++yxxcXFnvcbLy4uYmJgy65YvX86f/vQngoKCyqwPCws7a+z5FBUVUVRU5Hqcm5tbmd2pU8WNWkPeOixZ+8yOIiIi4pbc8rTdhg0bCAsLcxUngH79+mGxWNi4cWOFtrF161aSkpIYO3bsWc/dfffdRERE0L17dxYsWHDBe8HNnTuX0NBQ1xIfH1+5HapDvtFtAQjOO2hyEhEREffkluUpIyODqKioMuu8vb0JDw8nI6Nis2e/+eabtGvXjl69epVZP3v2bN59911WrVrFkCFD+POf/8xLL7103m3NmDGDnJwc13L48OHK7VAdCmvWHoAYW4puECwiIlIF9eq03fTp03nqqafOO2b37t3Vfp/Tp0+zZMkSHn744bOe+/W6zp07k5+fzzPPPMM999xT7vasVitWq7XauepCbKuOZDsD+ckZhTM7l8hGoWZHEhERcSvVKk82m42MjAwKCgqIjIwkPDy8WmGmTZvG6NGjzzumVatWxMTEkJmZWWZ9SUkJWVlZFbpW6b333qOgoIDbbrvtgmN79OjBY489RlFRkdsUpPPxCwrj9wH/5PDJQpaeLCGykdmJRERE3Euly9OpU6f417/+xdKlS9m0aRPFxcU4nU4Mw6Bp06b079+f8ePHc9lll1U6TGRkJJGRkRcc17NnT7Kzs9m6dStdu3YFYM2aNTgcDnr06HHB17/55pvceOONFXqvpKQkGjVq5BHF6YyLooI5fLKQ/cfyuLxVY7PjiIiIuJVKXfP03HPP0aJFCxYuXEi/fv344IMPSEpK4scff2TDhg088sgjlJSU0L9/fwYOHMjevbUzEWO7du0YOHAg48aNY9OmTXz99ddMnDiRYcOGub5pl5qaSkJCAps2bSrz2n379rF+/XruuOOOs7b70Ucf8Y9//IMdO3awb98+Xn31VebMmcOkSZNqZT/M4rpNy1HdpkVERKSyKnXkafPmzaxfv55LLrnknM93796d22+/nfnz57Nw4UK+/PJL2rRpUyNBf2vx4sVMnDiRvn37YrFYGDJkCC+++KLreZvNRnJyMgUFBWVet2DBAtcRst/y8fHhlVdeYcqUKTidTlq3bs1zzz3HuHHjamUfzHKVYwsjfR/n2O6L4cYVZscRERFxK4ZTX7mqcbm5uYSGhpKTk0NISIjZcc6y+6sPaPf5KA4ZTWjxyC6z44iIiNQLFf38rvJUBaNGjWL9+vVVfbmYKKZVIgBNHBmcPl1ochoRERH3UuXylJOTQ79+/WjTpg1z5swhNTW1JnNJLQqLaU4BVnwMO6kHqz/1g4iISENS5fL0wQcfkJqayoQJE3jnnXdo0aIF1157Le+99x42m60mM0oNMyxepHuXzoKe9dMOk9OIiIi4l2rNMB4ZGcnUqVPZvn07GzdupHXr1tx6663ExcUxZcqUWvu2nVRfbmALAIqP6gbBIiIilVEjt2dJT09n1apVrFq1Ci8vLwYNGsQPP/xA+/btef7552viLaSGlYSXfgvSWzcIFhERqZQqlyebzcb777/P9ddfT/PmzVm2bBmTJ08mLS2Nt956i88//5x3332X2bNn12ReqSE+cYlsd7Rib7EmyRQREamMKt+eJTY2FofDwfDhw9m0aROdOnU6a8w111xDWFhYNeJJbQntfBPXrA7Fz2lhhMOJxWKYHUlERMQtVLk8Pf/88wwdOhQ/P79yx4SFhXHw4MGqvoXUovhG/vh6WSi0OUjNPk18eIDZkURERNxClU/b3XrrrectTlK/eXtZaBUZiDcl7Es9ZnYcERERt1HlI09Tp04953rDMPDz86N169bcdNNNhIeHVzmc1K7pLKSX9b9s2j4VOj5kdhwRERG3UOXy9N1337Ft2zbsdjtt27YF4Mcff8TLy4uEhAT+/ve/M23aNL766ivat29fY4Gl5gQGh+KbbcdyPNnsKCIiIm6jyqftbrrpJvr160daWhpbt25l69atHDlyhN///vcMHz6c1NRUrrrqKqZMmVKTeaUG+caW3uA5NE/TFYiIiFRUlW8M3KRJE1atWnXWUaWdO3fSv39/UlNT2bZtG/379+f48eM1EtZd1PcbA59xZPdGmr7Tn2xnICEzj2DxqpFpv0RERNxSrd8YOCcnh8zMzLPWHzt2jNzcXKD023bFxcVVfQupZbEXJWJ3GoQZ+aSlHjI7joiIiFuo1mm722+/neXLl3PkyBGOHDnC8uXLGTt2LIMHDwZg06ZNXHzxxTWVVWqYl68/6V6xABzdv93kNCIiIu6hyheMv/baa0yZMoVhw4ZRUlJSujFvb0aNGuW6JUtCQgL/+Mc/aiap1IqsgFY0zUujIHUH8Aez44iIiNR7VS5PQUFBvPHGGzz//PMcOHAAgFatWhEUFOQac65Zx6V+ORnTixXJJaQXRnCl2WFERETcQJVO29lsNvr27cvevXsJCgoiMTGRxMTEMsVJ3ENh57FMtN3DBwUdzI4iIiLiFqpUnnx8fPj+++9rOouY4OLoYAD2ZeZhd1Tpi5ciIiINSpUvGB85ciRvvvlmTWYREzQLD8DPG6Ls6Rw52rCmlBAREamKKl/zVFJSwoIFC/j888/p2rUrgYGBZZ5/7rnnqh1Oap+XxeAT64O0sh9iy+5GNI/VReMiIiLnU+XytGPHDrp06QKU3pbl1wzDqF4qqVN5/k0h7xCn9Y07ERGRC6pyeVq7dm1N5hATFYdfDHlfYTmhe9yJiIhcSLXux/Hll18ycuRIevXqRWpqKgD//Oc/+eqrr2oknNQN68/3uAs7td/kJCIiIvVflcvT+++/z4ABA/D392fbtm0UFRUBpbdtmTNnTo0FlNrXuGUiAE1KfsJud5icRkREpH6rcnl6/PHHmT9/Pm+88QY+Pj6u9VdccQXbtm2rkXBSN2JadXTd4y71yCGz44iIiNRrVS5PycnJXHXVVWetDw0NJTs7uzqZpI5ZfP3JcN3jLsncMCIiIvVclctTTEwM+/btO2v9V199RatWraoVSured42v5+WSm0guCDY7ioiISL1W5fI0btw47r33XjZu3IhhGKSlpbF48WLuu+8+JkyYUJMZpQ4cvuQuni25hY2nIsyOIiIiUq9VeaqC6dOn43A46Nu3LwUFBVx11VVYrVbuu+8+Jk2aVJMZpQ4kxJYecdqTnmtyEhERkfqtykeeDMPgr3/9K1lZWezYsYNvv/2WY8eO8dhjj9VkvnI98cQT9OrVi4CAAMLCwir0GqfTycyZM4mNjcXf359+/fqxd+/eMmOysrIYMWIEISEhhIWFMXbsWPLy8mphD+qXdtHBRHKSJie+obDYZnYcERGReqta8zwB+Pr60r59e7p3705QUFBNZKqQ4uJihg4dWqlThE8//TQvvvgi8+fPZ+PGjQQGBjJgwAAKCwtdY0aMGMHOnTtZtWoVK1asYP369YwfP742dqFeiQ725iu/ySzyeZKU/bvMjiMiIlJvGU6n01nVF69evZrVq1eTmZmJw1F2fqAFCxZUO1xFLFq0iMmTJ1/wG35Op5O4uDimTZvGfffdB5TOSRUdHc2iRYsYNmwYu3fvpn379mzevJlu3boBsHLlSgYNGsSRI0eIi4urUKbc3FxCQ0PJyckhJCSkWvtXlw480ZVWtn180/V5et1wu9lxRERE6lRFP7+rfORp1qxZ9O/fn9WrV3P8+HFOnjxZZqlvDh48SEZGBv369XOtCw0NpUePHmzYsAGADRs2EBYW5ipOAP369cNisbBx48Zyt11UVERubm6ZxR3lhLQFwJa2w+QkIiIi9VeVLxifP38+ixYt4tZbb63JPLUmIyMDgOjo6DLro6OjXc9lZGQQFRVV5nlvb2/Cw8NdY85l7ty5zJo1q4YTmyC6A5z4GP+Te8xOIiIiUm9V+chTcXExvXr1qsksTJ8+HcMwzrvs2VP/PthnzJhBTk6Oazl8+LDZkaoktEUnAGIK91GNs7kiIiIercpHnu644w6WLFnCww8/XGNhpk2bxujRo887pqoTcMbExABw9OhRYmNjXeuPHj1Kp06dXGMyMzPLvK6kpISsrCzX68/FarVitVqrlKs+iWvbDT6BZhzl2IksIiMamx1JRESk3qlyeSosLOT111/n888/JzExscz97QCee+65Sm8zMjKSyMjIqkY6r5YtWxITE8Pq1atdZSk3N5eNGze6vrHXs2dPsrOz2bp1K127dgVgzZo1OBwOevToUSu56hO/0CiOG+FEOLM4nLyFyIgBZkcSERGpd6pcnr7//ntXCdmxo+wFxoZhVCtURaSkpJCVlUVKSgp2u52kpCQAWrdu7ZoyISEhgblz5/KHP/wBwzCYPHkyjz/+OG3atKFly5Y8/PDDxMXFMXjwYADatWvHwIEDGTduHPPnz8dmszFx4kSGDRtW4W/aubtVkaPYmlpAh4JwupgdRkREpB6qcnlau3ZtTeaotJkzZ/LWW2+5Hnfu3BkozdWnTx+g9ObFOTk5rjH3338/+fn5jB8/nuzsbHr37s3KlSvx8/NzjVm8eDETJ06kb9++WCwWhgwZwosvvlg3O1UPZLUbyXspydhOeDPa7DAiIiL1ULXmefryyy957bXXOHDgAMuWLaNJkyb885//pGXLlvTu3bsmc7oVd53nCWDNnqPcvmgLbaOD+XTKVWbHERERqTO1Ps/T+++/z4ABA/D392fbtm0UFRUBpRNPzpkzp6qbFZMlRAVwmbGHHieWU2QrMTuOiIhIvVPl8vT4448zf/583njjjTIXi19xxRVs27atRsJJ3YsN8WWJ9Qlmey/gpwP1b1oIERERs1W5PCUnJ3PVVWef1gkNDb3grVKk/jK8raR6Nwfg+D6VYBERkd+qcnmKiYlh3759Z63/6quvqjwXk9QPOSEXA1Cc+oPJSUREROqfKpencePGce+997Jx40YMwyAtLY3Fixdz3333ueZNEvfkjLoEAP+Tu01OIiIiUv9UeaqC6dOn43A46Nu3LwUFBVx11VVYrVbuu+8+Jk2aVJMZpY4Ft+gMeyDm9H6zo4iIiNQ71ZqqAErvcbdv3z7y8vJo3769a4LKhsydpyoAOJ2Vjv+LCTicBsfvPUBUeLjZkURERGpdRT+/q3zk6QxfX1/at29f3c1IPeIfHkuWEUo4ORzes5WoXr83O5KIiEi9UalrnlJSUiq18dTU1EqNl/rj/dj7GFL0CJsLYi88WEREpAGpVHm67LLLuPPOO9m8eXO5Y3JycnjjjTfo0KED77//frUDijmcCdex1dmW748WmR1FRESkXqnUabtdu3bxxBNP8Pvf/x4/Pz+6du1KXFwcfn5+nDx5kl27drFz5066dOnC008/zaBBg2ort9SyDnGhAOxIzTU5iYiISP1SpQvGT58+zccff8xXX33FTz/9xOnTp4mIiKBz584MGDCADh061EZWt+HuF4wD5Jwq4LEnH+US4xA3P7CI0OAAsyOJiIjUqop+flf723ZyNk8oTzidnJoVRzAFJF23gk6XXWl2IhERkVpV6zcGFg9nGKT5l840nrN/i8lhRERE6g+VJynX6calM40bGdtNTiIiIlJ/qDxJuXzjOwMQnqvbtIiIiJyh8iTlik24HIBW9oPkndaUBSIiIqDyJOfRKL49p7ESYBRxMFmn7kRERKCGylNWVhYOh6MmNiX1icWLVGtrAI7v32ZyGBERkfqhyuVp165dPPnkk/Tq1YvIyEiioqK47bbbeP/998nPz6/JjGKiby6ZyWWFf2eFvafZUUREROqFSpWn5ORkpk2bRps2bbj88svZvHkzd911F0ePHuWTTz6hefPmzJ49m4iICK699lpeffXV2sotdSSudWeOEcaO1Byzo4iIiNQLlbo9yzfffEN+fj4vvvgiffv2xdfX1/VcREQE3bt357HHHuPQoUP897//5T//+Q8TJkyo8dBSdzo0Kb1Ny75jeZwutuPv62VyIhEREXNphvFa4BEzjP/K3x/7M+2Ld9B46Dw6JnYxO46IiEitqOjnd6WOPAEEBwfTuXNnunbtSpcuXejSpQvt27fHMIxqBZb6q793Eq3tO/liz9eg8iQiIg1cpcvTU089xdatW1mzZg0vv/wyDocDf39/EhMTyxSqSy+9tDbyignyIi6F1J2Qpm/ciYiIVLo8/fnPf3b9+fTp0wQGBjJp0iSysrL49ttv+cc//kFxcTF2u71Gg4p5/JtfBqlLiMzdYXYUERER01W6PP2av78/AMOHDycxMRGAkpISdu3aVf1kUm/EdbgCvoHW9oNk5eYRHhJkdiQRERHT1PgM497e3q4iJZ4hOPZicgnCatg4sHOT2XFERERMpduzyIUZBmmB7QDI3bfR5DAiIiLmqnR5uuOOO3j11VfZvHkzRUWlN4vVN+08X1FUJ045/TmRddzsKCIiIqaq9DVPe/fuZdmyZZw6dQpv79KXz5o1iz59+tClSxc6depEQEBAjQcVczmvnEri7qsJz/Xjj06nCrOIiDRYlT7ytG7dOnJyckhOTubtt9/mvvvuIzs7m5kzZ9K7d29CQ0O55JJLaiNrGU888QS9evUiICCAsLCwC4632Ww88MADdOzYkcDAQOLi4rjttttIS0srM65FixYYhlFmefLJJ2tpL9xHQnw03l5enMgv5sjJ02bHERERMU2Vv23Xpk0b2rRpw7Bhw1zrDh48yJYtW/juu+9qJNz5FBcXM3ToUHr27Mmbb755wfEFBQVs27aNhx9+mEsvvZSTJ09y7733cuONN7Jly5YyY2fPns24ceNcj4ODg2s8v7vx8/EiISaEH1Jz+P5wNvHhOrooIiINU6XKU0ZGBo0aNcJqtZ7z+ZYtW9KyZUuGDh0KwIEDB2jVqlX1U57DrFmzAFi0aFGFxoeGhrJq1aoy615++WW6d+9OSkoKzZo1c60PDg4mJiamxrJ6ijv9PqO97zsc2PxHuPQJs+OIiIiYolKn7d577z3Cw8P5wx/+wMKFCzl27NhZYzZu3MiDDz7IJZdcUu9nGc/JycEwjLNO+z355JM0btyYzp0788wzz1BSUnLe7RQVFZGbm1tm8UTNQrxoZckg6FjtH1kUERGprypVniZOnMj27du58sorWbRoEU2bNqV3797MmTOHcePGERsby+DBg8nMzOTJJ588Z7mqLwoLC3nggQcYPnx4mZv/3XPPPSxdupS1a9dy5513MmfOHO6///7zbmvu3LmEhoa6lvj4+NqOb4qIdlcC0KpwJ8U2zSAvIiINk+F0Op1VffGJEydYsWIFn3zyCS1atOCmm26iZ8+eVf4m1vTp03nqqafOO2b37t0kJCS4Hi9atIjJkyeTnZ1d4fex2WwMGTKEI0eO8MUXX5z3zskLFizgzjvvJC8vr9zTlUVFRa5pG6D0rszx8fEXvCuzu3EWF1Aypyk+2Nk59EsuuUSToYqIiOfIzc0lNDT0gp/f1bo9S+PGjRk1ahSjRo2qzmZcpk2bxujRo887prrXUNlsNv70pz/x008/sWbNmguWmx49elBSUsKhQ4do27btOcdYrdZyi5UnMXwDOGxtQ6uiPWTuUnkSEZGGqVrlqaZFRkYSGRlZa9s/U5z27t3L2rVrady48QVfk5SUhMViISoqqtZyuZNTEZ0hdQ/GkY3A3WbHERERqXNue3uWlJQUkpKSSElJwW63k5SURFJSEnl5ea4xCQkJLF++HCgtTn/84x/ZsmULixcvxm63k5GRQUZGBsXFxQBs2LCBefPmsX37dg4cOMDixYuZMmUKI0eOpFGjRqbsZ30TcFEvAGJyv6caZ3xFRETcVr068lQZM2fO5K233nI97ty5MwBr166lT58+ACQnJ5OTkwNAamoqH374IQCdOnUqs60zr7FarSxdupRHH32UoqIiWrZsyZQpU5g6dWrt75CbaJrYhz1fxLPJ0ZqgkwU0DQ80O5KIiEidqtYF43JuFb3gzF3d9PJXbD+SwwvDOnFTpyZmxxEREakRFf38dtvTdmKeLs1LT2Fu++mkyUlERETqnsqTVFrX5o3woYSjB3eYHUVERKTOue01T2Ke7sHH+cE6lsKTvhQU3UyA1dfsSCIiInVGR56k0qKatQPDIMzIJ3nnNrPjiIiI1CmVJ6k8Lx8O+7cD4MSu9SaHERERqVsqT1Ilp+MuB8Ca+q3JSUREROqWypNUSXj7PgBcVJCkmwSLiEiDovIkVdKkw1XY8CLOOEFysr51JyIiDYfKk1SJYQ0ixVp6o+TMH1abnEZERKTuaKoCqbK01sNYlrSLrNwW9DU7jIiISB3RkSepsvArRjPffiMfpwZQYneYHUdERKROqDxJlSXEhBDi501+sZ2dablmxxEREakTKk9SZV4Wg35Nndxo+ZqDSevMjiMiIlIndM2TVMto4yMSff/Fuh8PATeZHUdERKTW6ciTVEtw2z4ANDv1HXaH09wwIiIidUDlSaolvlNfHE6DlqTx4769ZscRERGpdSpPUi3eQeGkWNsAkLbtfyanERERqX0qT1Jtp+KuAMA3RTcJFhERz6fyJNUWnjgAgIvzt1JYXGJyGhERkdql8iTVFtexD4X4Em2cZOfOJLPjiIiI1CqVJ6k2w8efRc3mcFnh31mVEWR2HBERkVql8iQ1IqrTtRwjjK/3HTc7ioiISK1SeZIa0bt1BAA70nI4mV9schoREZHao/IkNSIqxI8pYV/ylvdcdm1ZY3YcERGRWqPyJDWmr/+PXOX1A6d3fWp2FBERkVqj8iQ1xuuiPgBEZ36N06lbtYiIiGdSeZIa06LHjQC0d/zIgZTDJqcRERGpHSpPUmP8I1tw2KcFXoaTQxs/MjuOiIhIrVB5khqV3eQaAKwHPzc5iYiISO1QeZIaFdH5BgDaF2ziVEGhyWlERERqnsqT1KjYDldz3GjEdsdFbNq93+w4IiIiNc5ty9MTTzxBr169CAgIICwsrEKvGT16NIZhlFkGDhxYZkxWVhYjRowgJCSEsLAwxo4dS15eXi3sgYfy8ubVzh8yxvYAnx7UTYJFRMTzuG15Ki4uZujQoUyYMKFSrxs4cCDp6emu5d///neZ50eMGMHOnTtZtWoVK1asYP369YwfP74mo3u8a9rFAbA2+ZimLBAREY/jbXaAqpo1axYAixYtqtTrrFYrMTEx53xu9+7drFy5ks2bN9OtWzcAXnrpJQYNGsSzzz5LXFxctTI3FJe1bESArxfep9LYfegI7VvGmx1JRESkxrjtkaeq+uKLL4iKiqJt27ZMmDCBEydOuJ7bsGEDYWFhruIE0K9fPywWCxs3bix3m0VFReTm5pZZGjKrtxdvhrzBBr9JpH291Ow4IiIiNapBlaeBAwfy9ttvs3r1ap566inWrVvHtddei91uByAjI4OoqKgyr/H29iY8PJyMjIxytzt37lxCQ0NdS3y8jrQENWkHQMhPulWLiIh4lnpVnqZPn37WBd2/Xfbs2VPl7Q8bNowbb7yRjh07MnjwYFasWMHmzZv54osvqpV7xowZ5OTkuJbDhzW7drNefwLg0uLvOJJ+1OQ0IiIiNadeXfM0bdo0Ro8efd4xrVq1qrH3a9WqFREREezbt4++ffsSExNDZmZmmTElJSVkZWWVe50UlF5HZbVaayyXJwht1pE0rybE2VP58ev/0PSPlbuwX0REpL6qV+UpMjKSyMjIOnu/I0eOcOLECWJjYwHo2bMn2dnZbN26la5duwKwZs0aHA4HPXr0qLNcHsEwOB4/gLhDC/Db9wmg8iQiIp6hXp22q4yUlBSSkpJISUnBbreTlJREUlJSmTmZEhISWL58OQB5eXn85S9/4dtvv+XQoUOsXr2am266idatWzNgwAAA2rVrx8CBAxk3bhybNm3i66+/ZuLEiQwbNkzftKuCmMv/CEDi6U0cO5ljchoREZGa4bblaebMmXTu3JlHHnmEvLw8OnfuTOfOndmyZYtrTHJyMjk5pR/aXl5efP/999x4441cfPHFjB07lq5du/Lll1+WOeW2ePFiEhIS6Nu3L4MGDaJ37968/vrrdb5/niDq4p4cs0QQZBSy66sPzY4jIiJSIwynZjGscbm5uYSGhpKTk0NISIjZcUz1+ZLn+c+OLOwX9eO1sVebHUdERKRcFf38dtsjT+IeWvS7g08cl7P2QAG5hTaz44iIiFSbypPUqtZRwbSOCqLY7uCznZqyQERE3J/Kk9S6YW29meT1H7zWPWl2FBERkWqrV1MViGe6rmkhsT7vcSrHnxPZT9A4LNTsSCIiIlWmI09S62I7XsMxSwTBxml++GKZ2XFERESqReVJap/FQlrTQQBYdy83OYyIiEj1qDxJnYi78lYAuhRuJO1o5gVGi4iI1F8qT1InIltfRqpXU6yGjT1f/NvsOCIiIlWm8iR1wzA43vJGAEL36tSdiIi4L5UnqTMtrxlFntOPHwvD2HHkpNlxREREqkTlSepMSJMEHmrzATNKxvHetjSz44iIiFSJypPUqT90bw3A8u9SKbTZTU4jIiJSeSpPUqd6t44gNsRKfGEy3278xuw4IiIilabyJHXKy2Lwt+iVrLA+hM83z5sdR0REpNJUnqTOtbz8JgC65q8nPSPD5DQiIiKVo/IkdS62fW8OezfHz7Cxe9UCs+OIiIhUisqT1D3DILvd/wHQ/MASbCW6cFxERNyHypOYou2AOzmNlYuch9my7iOz44iIiFSYypOYwjeoET9GXweAZfPrJqcRERGpOJUnMU2T/vcCEFvwI3sOHzU5jYiISMWoPIlpIi7qxLwmz3NN8XO8tTnT7DgiIiIVovIkpurZ9ybseLH8uyPkFNjMjiMiInJBKk9iqu4tw0mICcZuK2bF2nVmxxEREbkgb7MDSMNmGAb3X1pEu5OTKdnsS2G/H/Cz+podS0REpFw68iSmu/Lyywkwiokng02fLDI7joiIyHmpPInpfPyDOdBqJAAx379KiSbNFBGRekzlSeqFhBvv4zRWLnYeYPOa/5gdR0REpFwqT1Iv+IdFsifuZgACN83D6XSanEhEROTcVJ6k3mh543SKnd4kluxg05oPzI4jIiJyTipPUm+ExbRgZ2zp0acDG1dgd+jok4iI1D8qT1KvXPTHRxnJ48zIvZmPtqeZHUdEROQsbluennjiCXr16kVAQABhYWEVeo1hGOdcnnnmGdeYFi1anPX8k08+WUt7Ib8VEtGEnldfC8C8z3/EZneYnEhERKQsty1PxcXFDB06lAkTJlT4Nenp6WWWBQsWYBgGQ4YMKTNu9uzZZcZNmjSppuPLeYzu1YLGgb6cPnGENatXmh1HRESkDLedYXzWrFkALFq0qMKviYmJKfP4v//9L9dccw2tWrUqsz44OPissedTVFREUVGR63Fubm6FXytnC7R683jHY/zuuykc/6YReVdcRVBgkNmxREREADc+8lRdR48e5eOPP2bs2LFnPffkk0/SuHFjOnfuzDPPPENJScl5tzV37lxCQ0NdS3x8fG3FbjB+1/96ciwhNCGTrUsfNzuOiIiIS4MtT2+99RbBwcHcfPPNZdbfc889LF26lLVr13LnnXcyZ84c7r///vNua8aMGeTk5LiWw4cP12b0BsEaEELGZdMB6JaygNSUAyYnEhERKVWvytP06dPLvaj7zLJnz54aea8FCxYwYsQI/Pz8yqyfOnUqffr0ITExkbvuuou//e1vvPTSS2VOy/2W1WolJCSkzCLV13HgHfzo245Ao4jDyx4wO46IiAhQz655mjZtGqNHjz7vmN9en1QVX375JcnJybzzzjsXHNujRw9KSko4dOgQbdu2rfZ7S8UZFgu+1z8N/7mBy099xvcbPiOxZ3+zY4mISANXr8pTZGQkkZGRtf4+b775Jl27duXSSy+94NikpCQsFgtRUVG1nkvO1iLxKrZ+cR1dsz4m+LNpFHbehJ+fv9mxRESkAatXp+0qIyUlhaSkJFJSUrDb7SQlJZGUlEReXp5rTEJCAsuXLy/zutzcXJYtW8Ydd9xx1jY3bNjAvHnz2L59OwcOHGDx4sVMmTKFkSNH0qhRo1rfJzm3i299nqM05kNbN15es8/sOCIi0sDVqyNPlTFz5kzeeust1+POnTsDsHbtWvr06QNAcnIyOTk5ZV63dOlSnE4nw4cPP2ubVquVpUuX8uijj1JUVETLli2ZMmUKU6dOrb0dkQsKbhTNtzev4fklO/H+6jDXdW5Ou1hdVyYiIuYwnLp9fY3Lzc0lNDSUnJwcXTxeg+7651ZW7sygWxN/lt51Jd4+PmZHEhERD1LRz2+3PW0nDc+smy6hs18ajx27h43/mml2HBERaaBUnsRtRIf4MbOLjXaWw3Q/9Bq7t6w1O5KIiDRAKk/iVjrfMIFtIb/Dx7AT/PFdnMrJMjuSiIg0MCpP4l4Mg9a3v0EGkTR1ZrDnzfE4HQ6zU4mISAOi8iRuJyQsguxrX6bEaeGy3FVsfOdJsyOJiEgDovIkbimhx0C2XjwFgG57nmHXtytNTiQiIg2FypO4re7DH2JLcF++c7ZmyqpTZOQUmh1JREQaAJUncVuGxUL7u95iVqMnSc4PYMyizZwqtJkdS0REPJzKk7i1gMBgXh3Vk4ggK7vTc3njjZcpLjxtdiwREfFgKk/i9uLDA1g4+jKm+P6XqSceZcff/w9HSYnZsURExEOpPIlH6Ng0lKt/NxCb04suuWv47u+34nTYzY4lIiIeSOVJPEanPjeztdvT2J0GXbM+Ydsro1SgRESkxqk8iUe5/IY72NT5qdICdeIjvntlFI4SXUQuIiI1R+VJPE7PwXeysdMc7E6DLic+4ocXbsZWoiNQIiJSM1SexCP1+sOf2XzZcxQ5fXg/qyV3vL2VgmJdRC4iItWn8iQe6/Lrb2fbjZ+xzDKIdT8eY8irGzh8Is/sWCIi4uZUnsSj9ezahcXjehAR5Etqehp5L11J8rp3zI4lIiJuTOVJPF6XZo34cGJvHmr0Oe04QNu14/nuH3djtxWZHU1ERNyQypM0CHFh/tx470usazQEgM5H/kXK0704/tNOk5OJiIi7UXmSBsPPz4+r7nmTr7q9SLYziJa2fQQtvJrvlz6Ks6TY7HgiIuImVJ6kQTEMg97XjyJ79Bd859MJP2wk7nme9+dN4eDxfLPjiYiIG1B5kgapRcs2dHxgDWvbzWavsymzj1/FgHnrefbTZE6d1lEoEREpn8qTNFje3l5cc8u9WCdt5NI2LSgucfDy2r1sf2oA29/+C7aCHLMjiohIPWQ4nU6n2SE8TW5uLqGhoeTk5BASEmJ2HKkAp9PJZ7uOsvKjZTxf+BAA2QST0uY22t44FWtwhMkJRUSktlX081vlqRaoPLkvW4mdr1csomXSszQnDYAC/Ngb/0daXX8fwdEtTU4oIiK1ReXJRCpP7q+gsJCNKxbSZOd8LnYeAsDhNHi59Rtc3ef3JDYNxTAMc0OKiEiNUnkykcqT5yi22dm46h2Ctr5KI9tRrin+G04sXBIXwuS4PXRKTCSyTXdQkRIRcXsqTyZSefI8TqeTbfuO8M+tx/lkRwaOkmI2Wf9MuJFHmlcTjscPIKrrTcRcciVYvMyOKyIiVaDyZCKVJ8+WlV/M6s0/0OTbR+hy+lv8DJvruRyCORzeE2enkbS+/Hr8fVWkRETchcqTiVSeGo70zGPsWfcOvgc+o0PBFkKN0ok255XczCvOoXRsEsqV8d4MsGyhScerCW3aTkemRETqKZUnE6k8NUy5BafZuXE1BTs/4Z/ZiXyRFw9Af8tmXvd9HoDTWEm1tiYv/BJ8m3Ym8uLLiGjRAcPH38zoIiKCh5enQ4cO8dhjj7FmzRoyMjKIi4tj5MiR/PWvf8XX17fc1xUWFjJt2jSWLl1KUVERAwYM4O9//zvR0dGuMSkpKUyYMIG1a9cSFBTEqFGjmDt3Lt7e3hXOp/IkTqeTw1mn2XQoi9zvP6bb4UW0tu8nwCg6a+xkx1T2R/alVWQgl/lncKlzF4HRrQmLvYhGca0wfANM2AMRkYanop/fFW8E9ciePXtwOBy89tprtG7dmh07djBu3Djy8/N59tlny33dlClT+Pjjj1m2bBmhoaFMnDiRm2++ma+//hoAu93OddddR0xMDN988w3p6encdttt+Pj4MGfOnLraPfEAhmHQrHEAzRoHQNc7gTvJLSgkac92Tu7fAunbaZSzixYlB9hpi2Fvag4/pOYQ4/URI33+XWZbJwklyyeafP9YtrS6GyPyYhoHWYnjBBGWXEIi4giNiMPLx2rOzoqINDBueeTpXJ555hleffVVDhw4cM7nc3JyiIyMZMmSJfzxj38ESktYu3bt2LBhA5dffjn/+9//uP7660lLS3MdjZo/fz4PPPAAx44dK/eoVlFREUVFvxxRyM3NJT4+Xkee5IKKbXZSsvLZf7yA/cfyCN73EQmZn9DIlkG04xjBxuky4wcVzWGXswUAE7w+5AGfpa7nThFAnhFEgVcwhV7BLI+5h/zQNgT7+XCRbS/NT+/Cyz8UH/8gvKwB+PgF4e0XhG9AEN6NmuHvH4ifjxdWbwsWi6ZeEJGGx6OPPJ1LTk4O4eHh5T6/detWbDYb/fr1c61LSEigWbNmrvK0YcMGOnbsWOY03oABA5gwYQI7d+6kc+fO59z23LlzmTVrVs3tjDQYvj5etI4OoXX0z/8n7TMFmAKUFqsjx46Slbaf/KMHKT7xE1cEdaXlaV+O5xURluVPZlE4jZw5+Bh2gikg2FkAJZlQAvf9eJTdTj8A/uz1Ebf4vFtujqFFM9nsTABgpNcq7vd+hyLDlxJ8KDF+WeyGNwtD7iLFrx3eXgaXFidxVf5KHBZfnBYfHBZf8PLCMLzA4s2OqEGcDGiFl2EQeXo/rU9+DRYLTot36YXzRul/DcNCZkQP8gPj8bIYBBUeJSLneywWC4ZhwTAMDIvl54lJDU6FtaMoMBYD8C0+ScjJXaXPGWBgwWmxYGCAYXA6uDm2gBgMA7xteQTk7Pt5bOm2DIul9L+GheKAKOwBkRiAxX4aa24KP2+2NANn3sOgxL8xjoDS2/YYJYX45v5U9of6q7m/SqyNsAdElj5w2LDmHCoz1GkYruEO3xBKAqJ+flCCb27KL5ukbKm1+wZhPzPW6cDn5wznmnbM4RP4y1jA99RPZw86M9bb3zXWwMD71BH49b+zf/UGTi9fHIG/bNc7Lw2cjnNv2OKDPTD6vGPPbNpp8cYeGONa75WfgeEo+fXIXzIYFuxBcb8aexTDYePcDEqCm/wytiATw17ezcB/O/Y4hr2wnLFQEtTEtQOW0yewlJw+z9g4MCw/j83CUlJQ/tjAWNcXTSyFJ7HY8s8zNgYs3j+PzcZiyyt/bEAUeJUeFLAU5WApPnWesZHgZf15bC6W4txyx9r9I3B6l/7dYxTn4VWUXf5Yv8Y4f77m07Dl41V48jxjw3H6/HIZQ7PwAHy9zblFr0eUp3379vHSSy+d95RdRkYGvr6+hIWFlVkfHR1NRkaGa8yvi9OZ5888V54ZM2YwdepU1+MzR55EqsPXx4umcXE0jYsDrgTg6jIjegLzKCkp4XjWMXJPpJOfk4Ut7wS2gmxGhPYiq8SP3NM24o52YNvJDHxtp/CyF+LrKMTqLF38KMRm8Qd76VaDOU2IUQD8/Je58+flZz9lHOdbxwkAWnjtorvPmnL34R8p0axxlL54iGU9o3znlzt2YvEkVjh6AjDI8i1/932x3LF/sY1nmb0PAH0s37HI95lyx860jeJt+wAALrfsYqnv4+WOfdI2jPn2GwFINPbzofXhcse+UHIzz5eUHsVubRzhc+v95Y59reQ65paMAKAJx/ja795yx/6zpB8Pl9wOQDi5bPO7q9yx79uvZJptAgB+FLHHb0y5Yz+2d+du22TX40N+/1fu2LX2Sxlje8D1eJd1zDmv1wPY6EjgluKZrsdbrHcRYZz7g/V7R0tuLH7C9fhL33uJtxw759i9jib8vviX3+unvvfT1nLknGOPOCO4puiX/7184PsQnSznPguR5QyiS9Hrrsf/9nmcnl67zjn2tNOXdkWLXI/f9HmGvl7fnXMsQIvCxZwpdS/7vMD1XhvLHdu+cAEFlBaMZ7znM9R7fbljuxa+yglCAZjlvZBR3qvKHdu7aB5HnKVldrr3Eu7yXlHu2P5FT/Gjs/Sz6l6v95ni8365Y28qms12Z2sAxnmt4K8+S8odO7z4r2xwXAKU/mPscZ+F5Y69vfg+1ji6AKV/R/ztPH9H3F18Dx87Lnc9XjPtalpFBpU7vjbVq/I0ffp0nnrqqfOO2b17NwkJCa7HqampDBw4kKFDhzJu3LjajnhOVqsVq1XXm4g5vL29iYiKJSIqtsz6HmUetQfK/8D+ALA7nBTa7BTmdSc9+26KC09TVHQah60Iu60QR4kNh62QceFdGOEdhs3uIPCkL1uORUJJMU57MYa9GKejBKe9BBx2ukd0oYVfSxxOJ01ys9l6YhCG047htIPTgcVpB6cdw+mgZdPWDLBGY3dAi4J4krM7gNOJgfPnox5OjJ//2ygsji6+YTiBuKJIDuS1wsDpWs6MNXASFBpBO2sITqeTmJJQ0vOjfx6Da6zl59f5BYXS0icQp9NJlD2QrMLQcn9mXtZAYgNLP/waO/w5aSv/EL+XbyAxAT+PdfqTYyv/L3yLbwDRAaV/n4Q6reTaAssdi48/UX6lY61OyCv59ZcLfnNFhrc/kT+PdTohv8TP9dRvD1Q5vf2IsFpd2ygqsbrGGL/ZrtPiS+PAXy5psNl9KeTsSxycgMPiS/ivxpbYfTl9jrEAJRZfGgX4uB7bHecZa/gQ9quxjvOMLTasvxnrU+7YIsO3zFjnebYLEObv4zryZFxgbKi/N76GT4XGhvj7YP95rNeFxvr5EPbzWO/z7BtA8K/G+jh9KHT6lDs26FdjfZ2+5x0bYP1lrPUC263MWL9fjQWwmHhnh3p1zdOxY8c4ceLEece0atXKde1RWloaffr04fLLL2fRokVYLOUfvluzZg19+/bl5MmTZY4+NW/enMmTJzNlyhRmzpzJhx9+SFJSkuv5gwcP0qpVK7Zt21buabvf0rftRERE3I9bXvMUGRlJZGRkhcampqZyzTXX0LVrVxYuXHje4gTQtWtXfHx8WL16NUOGDAEgOTmZlJQUevYsPVXQs2dPnnjiCTIzM4mKKj3suWrVKkJCQmjfvn019kxEREQ8hTlXWlVTamoqffr0oVmzZjz77LMcO3aMjIyMMtclpaamkpCQwKZNmwAIDQ1l7NixTJ06lbVr17J161bGjBlDz549ufzy0nOo/fv3p3379tx6661s376dTz/9lIceeoi7775bp+VEREQEqGdHnipq1apV7Nu3j3379tG0adMyz505C2mz2UhOTqag4JdvMDz//PNYLBaGDBlSZpLMM7y8vFixYgUTJkygZ8+eBAYGMmrUKGbPnl03OyYiIiL1Xr265slT6JonERER91PRz2+3PG0nIiIiYhaVJxEREZFKUHkSERERqQSVJxEREZFKUHkSERERqQSVJxEREZFKUHkSERERqQSVJxEREZFKUHkSERERqQSVJxEREZFKcMt729V3Z+54k5uba3ISERERqagzn9sXunOdylMtOHXqFADx8fEmJxEREZHKOnXqFKGhoeU+rxsD1wKHw0FaWhrBwcEYhlFj283NzSU+Pp7Dhw/rhsO1SD/nuqGfc93Qz7lu6OdcN2r75+x0Ojl16hRxcXFYLOVf2aQjT7XAYrHQtGnTWtt+SEiI/s9ZB/Rzrhv6OdcN/Zzrhn7OdaM2f87nO+J0hi4YFxEREakElScRERGRSlB5ciNWq5VHHnkEq9VqdhSPpp9z3dDPuW7o51w39HOuG/Xl56wLxkVEREQqQUeeRERERCpB5UlERESkElSeRERERCpB5UlERESkElSe3Mgrr7xCixYt8PPzo0ePHmzatMnsSB5l/fr13HDDDcTFxWEYBh988IHZkTzS3LlzueyyywgODiYqKorBgweTnJxsdiyP8+qrr5KYmOiaTLBnz57873//MzuWx3vyyScxDIPJkyebHcWjPProoxiGUWZJSEgwLY/Kk5t45513mDp1Ko888gjbtm3j0ksvZcCAAWRmZpodzWPk5+dz6aWX8sorr5gdxaOtW7eOu+++m2+//ZZVq1Zhs9no378/+fn5ZkfzKE2bNuXJJ59k69atbNmyhd/97nfcdNNN7Ny50+xoHmvz5s289tprJCYmmh3FI11yySWkp6e7lq+++sq0LJqqwE306NGDyy67jJdffhkovX9efHw8kyZNYvr06San8zyGYbB8+XIGDx5sdhSPd+zYMaKioli3bh1XXXWV2XE8Wnh4OM888wxjx441O4rHycvLo0uXLvz973/n8ccfp1OnTsybN8/sWB7j0Ucf5YMPPiApKcnsKICOPLmF4uJitm7dSr9+/VzrLBYL/fr1Y8OGDSYmE6m+nJwcoPSDXWqH3W5n6dKl5Ofn07NnT7PjeKS7776b6667rszf01Kz9u7dS1xcHK1atWLEiBGkpKSYlkU3BnYDx48fx263Ex0dXWZ9dHQ0e/bsMSmVSPU5HA4mT57MFVdcQYcOHcyO43F++OEHevbsSWFhIUFBQSxfvpz27dubHcvjLF26lG3btrF582azo3isHj16sGjRItq2bUt6ejqzZs3iyiuvZMeOHQQHB9d5HpUnETHN3XffzY4dO0y9dsGTtW3blqSkJHJycnjvvfcYNWoU69atU4GqQYcPH+bee+9l1apV+Pn5mR3HY1177bWuPycmJtKjRw+aN2/Ou+++a8ppaJUnNxAREYGXlxdHjx4ts/7o0aPExMSYlEqkeiZOnMiKFStYv349TZs2NTuOR/L19aV169YAdO3alc2bN/PCCy/w2muvmZzMc2zdupXMzEy6dOniWme321m/fj0vv/wyRUVFeHl5mZjQM4WFhXHxxRezb98+U95f1zy5AV9fX7p27crq1atd6xwOB6tXr9b1C+J2nE4nEydOZPny5axZs4aWLVuaHanBcDgcFBUVmR3Do/Tt25cffviBpKQk19KtWzdGjBhBUlKSilMtycvLY//+/cTGxpry/jry5CamTp3KqFGj6NatG927d2fevHnk5+czZswYs6N5jLy8vDL/ijl48CBJSUmEh4fTrFkzE5N5lrvvvpslS5bw3//+l+DgYDIyMgAIDQ3F39/f5HSeY8aMGVx77bU0a9aMU6dOsWTJEr744gs+/fRTs6N5lODg4LOu1wsMDKRx48a6jq8G3Xfffdxwww00b96ctLQ0HnnkEby8vBg+fLgpeVSe3MQtt9zCsWPHmDlzJhkZGXTq1ImVK1eedRG5VN2WLVu45pprXI+nTp0KwKhRo1i0aJFJqTzPq6++CkCfPn3KrF+4cCGjR4+u+0AeKjMzk9tuu4309HRCQ0NJTEzk008/5fe//73Z0UQq7ciRIwwfPpwTJ04QGRlJ7969+fbbb4mMjDQlj+Z5EhEREakEXfMkIiIiUgkqTyIiIiKVoPIkIiIiUgkqTyIiIiKVoPIkIiIiUgkqTyIiIiKVoPIkIiIiUgkqTyIiIiKVoPIkIvIbo0ePZvDgwWbHEJF6SrdnEZEGxTCM8z7/yCOP8MILL6CbL4hIeVSeRKRBSU9Pd/35nXfeYebMmSQnJ7vWBQUFERQUZEY0EXETOm0nIg1KTEyMawkNDcUwjDLrgoKCzjpt16dPHyZNmsTkyZNp1KgR0dHRvPHGG+Tn5zNmzBiCg4Np3bo1//vf/8q8144dO7j22msJCgoiOjqaW2+9lePHj9fxHotITVN5EhGpgLfeeouIiAg2bdrEpEmTmDBhAkOHDqVXr15s27aN/v37c+utt1JQUABAdnY2v/vd7+jcuTNbtmxh5cqVHD16lD/96U8m74mIVJfKk4hIBVx66aU89NBDtGnThhkzZuDn50dERATjxo2jTZs2zJw5kxMnTvD9998D8PLLL9O5c2fmzJlDQkICnTt3ZsGCBaxdu5Yff/zR5L0RkerQNU8iIhWQmJjo+rOXlxeNGzemY8eOrnXR0dEAZGZmArB9+3bWrl17zuun9u/fz8UXX1zLiUWktqg8iYhUgI+PT5nHhmGUWXfmW3wOhwOAvLw8brjhBp566qmzthUbG1uLSUWktqk8iYjUgi5duvD+++/TokULvL31V62IJ9E1TyIiteDuu+8mKyuL4cOHs3nzZvbv38+nn37KmDFjsNvtZscTkWpQeRIRqQVxcXF8/fXX2O12+vfvT8eOHZk8eTJhYWFYLPqrV8SdGU5NoysiIiJSYfrnj4iIiEglqDyJiIiIVILKk4iIiEglqDyJiIiIVILKk4iIiEglqDyJiIiIVILKk4iIiEglqDyJiIiIVILKk4iIiEglqDyJiIiIVILKk4iIiEgl/D+eSOOL8XuzpgAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -384,7 +384,7 @@
     "\n",
     "var_principle = ImaginaryMcLachlanPrinciple(qgt=ReverseQGT(), gradient=ReverseEstimatorGradient())\n",
     "evolution_problem = TimeEvolutionProblem(hamiltonian, time, aux_operators=aux_ops)\n",
-    "var_qite = VarQITE(ansatz, init_param_values, var_principle, Estimator())\n",
+    "var_qite = VarQITE(ansatz, init_param_values, var_principle, StatevectorEstimator())\n",
     "evolution_result_eff = var_qite.evolve(evolution_problem)"
    ]
   },
@@ -411,7 +411,7 @@
    "outputs": [
     {
      "data": {
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wOMq+xZ2amkrHjh1JTk6mS5curu2JypPbCmvSBoAoeyZOpxPDMExOJCLioXwDzR97DoGBgeWOBv3e999/D0BOTg45OTkEBv763ldffTXNmzfnzTffJC4uDofDQceOHSkpKQHA39+/xnJ6Cl0w7qaim7UDIIQC8nI0XYGIiJzZnj17mDJlCm+++SY9e/Zk1KhRrqNLR48eJSUlhYceeogBAwbQvn17jh07Vu71iYmJJCcnk5OTc8bt+/r6Yrfba30/6hOVJzflHxjM65Y/86BtLAdzS8yOIyIiJiouLiYzM7PccuTIEex2OzfffDODBw9mzJgxLFiwgC1btvC3v/0NgEaNGtG4cWPeeOMNdu/ezerVq5k6dWq5bY8YMYKYmBiGDRvGd999x969e/nggw9Yu3YtAC1atGDfvn0kJydz5MgRiouL63z/65rKkxv7PHI0i+0D2J/vde7BIiLisVasWEFsbGy5pW/fvjzxxBMcOHCA119/HYDY2FjeeOMNHnroITZv3ozFYmHJkiVs3LiRjh07MmXKFJ555ply2/b19eXzzz8nKiqKoUOH0qlTJ5588km8vMo+e4YPH86QIUPo378/kZGRvPvuu3W+/3XNcDqdTrNDeJq8vDxCQ0PJzc0lJCSk1t5n6nvJ/OfHNO4f0o67+lV8rltERM6tqKiIffv20bJlS/z8/MyOI7XkbH/P5/v5rQvG3VjrEDvdjZ2QmguoPImIiNQFnbZzYxcX/8D71tlclva62VFEREQaDJUnNxYUW3a0KbzkLHOQiIiISI1SeXJjkfEJAEQ7DmMr8fxvN4iIiNQHKk9urHFMPEVOH7wMJ1mpu8yOIyLiEfQ9Ks9WE3+/Kk9uzLBYyPQqu5fQsbSfTU4jIuLeTn31/tTM2uKZCgvLbtzs4+NT5W247bftcnJyuPvuu/n444+xWCwMHz6cv//976fdGPG34x955BE+//xzUlNTiYyMZNiwYTz22GPl7gB9ptucvPvuu9x00021ti/VcdzaFE4epDBrj9lRRETcmre3NwEBARw+fBgfHx8sFh1f8CROp5PCwkKys7MJCwtzleWqcNvyNHLkSDIyMli5ciU2m40xY8Ywfvx4Fi9efMbx6enppKen8+yzz9KhQwcOHDjAnXfeSXp6Ou+//365sQsWLGDIkCGux2FhYbW5K9VSFBQPJ9fizNlndhQREbdmGAaxsbHs27ePAwcOmB1HaklYWBgxMTHV2oZbTpK5Y8cOOnTowPr16+nevTtQNrvq0KFDOXToEHFxcee1naVLl3LzzTdTUFCAt3dZjzQMg2XLljFs2LAq56urSTIBPln+H9au/Rr/Vr3469j6eXRMRMSdOBwOnbrzUD4+Pmc94uTRk2SuXbuWsLAwV3ECGDhwIBaLhaSkJK677rrz2s6pX86p4nTKxIkTuf3222nVqhV33nknY8aMOePpvFOKi4vL3csnLy+vkntUdX4XXMK/vrXSIb92S5qISENhsVg0w7iclVuWp8zMTKKiosqt8/b2Jjw8nMzMzPPaxpEjR3jssccYP358ufWzZ8/mD3/4AwEBAXz++efcdddd5Ofnc88991S4rblz5zJr1qzK70gNaBYeAMDBnEKcTudZS56IiIhUX726Gm769OkYhnHWZefOndV+n7y8PK688ko6dOjAo48+Wu65hx9+mEsuuYQuXbrwwAMPcP/99592k8TfmzFjBrm5ua7l4MGD1c54vuLDA+hq/MxA2xqOHz9WZ+8rIiLSUNWrI0/Tpk1j9OjRZx3TqlUrYmJiyM7OLre+tLSUnJycc14EduLECYYMGUJwcDDLli0751cVe/bsyWOPPUZxcTFWq/WMY6xWa4XP1TY/Hy/mWV8kihx+3jeERo36mZJDRESkoahX5SkyMpLIyMhzjuvduzfHjx9n48aNdOvWDYDVq1fjcDjo2bNnha/Ly8tj8ODBWK1WPvroo/M6p52cnEyjRo1MK0fn46hPLFG2HPIydgH9zI4jIiLi0epVeTpf7du3Z8iQIYwbN4558+Zhs9mYNGkSN910k+ubdmlpaQwYMIB33nmHHj16kJeXx6BBgygsLORf//oXeXl5rgu7IyMj8fLy4uOPPyYrK4tevXrh5+fHypUrmTNnDvfdd5+Zu3tOJwLiIXcbpYc1XYGIiEhtc8vyBLBo0SImTZrEgAEDXJNkvvjii67nbTYbKSkprplEN23aRFJSEgCtW7cut619+/bRokULfHx8eOWVV5gyZQpOp5PWrVvz3HPPMW7cuLrbsSqwhzaDXLDk7jc7ioiIiMdzy3me6ru6nOcJYP1/X+PiH6ezzfciLnzw61p/PxEREU90vp/f9erbdlI1wXFtAAgvSTc5iYiIiOdTefIAjePbAhDlPEJJcZHJaURERDybypMHiIhqyqOO27jNdj9px1WeREREapPKkwcwLBa+bzSMrxwXkZprMzuOiIiIR1N58hCnbtOSmlNochIRERHPpvLkIRIDcxlm+RavvWvMjiIiIuLRVJ48RA/bel7wfZX2aUvNjiIiIuLRVJ48hH90KwBCitJMTiIiIuLZVJ48RFiTsukKYkozcDocJqcRERHxXCpPHiK6WVscToNAo4jjRzPNjiMiIuKxVJ48hJ9/IIeNcACyD+w0OY2IiIjnUnnyIEd94gA4kbHL5CQiIiKeS+XJg+QHxgNgO7LX5CQiIiKey9vsAFJz9rcawas/dKSFb096mx1GRETEQ+nIkwfxb96dLx2d+SkvwOwoIiIiHkvlyYO0aBwIwIGjukWLiIhIbdFpOw/SLNyfayzf0/xkJvknuhMUHGZ2JBEREY+jI08eJDTAl1m+7zDN532y9m0zO46IiIhHUnnyMNneZdMV5Kb9bHISERERz6Ty5GFOBJRNV1CcvdvkJCIiIp5J5cnD2MJaAuB1fJ/JSURERDyTypOH8Y64AIDAglSTk4iIiHgmlScPExzXFoCIknSTk4iIiHgmlScPE928fdmfHKWoMN/kNCIiIp5H8zx5mLDG0dzFdFKKG/NabiltNdm4iIhIjdKRJw9jWCwcbHwpe5xN2J9TZHYcERERj6Py5IGaNy473KTbtIiIiNQ8lScP1N0vg7u8PiRs1wdmRxEREfE4Kk8e6EJjD/f7/JuE7E/NjiIiIuJxVJ48UGBs2XQFjUvSTE4iIiLieVSePFBUs1+mK3BkU1Ksi8ZFRERqktuWp5ycHEaOHElISAhhYWGMHTuW/Pyzz2vUr18/DMMot9x5553lxqSmpnLllVcSEBBAVFQUf/nLXygtLa3NXalxjWPiKXRa8TKcZKbqBsEiIiI1yW3neRo5ciQZGRmsXLkSm83GmDFjGD9+PIsXLz7r68aNG8fs2bNdjwMCfp0IyW63c+WVVxITE8P3339PRkYGt956Kz4+PsyZM6fW9qWmGRYLWV6xtHTs59jBFJq1STQ7koiIiMdwyyNPO3bsYMWKFfzjH/+gZ8+e9O3bl5deeoklS5aQnn7225IEBAQQExPjWkJCQlzPff7552zfvp1//etfdO7cmSuuuILHHnuMV155hZKSktrerRp13L8pACezdpmcRERExLO4ZXlau3YtYWFhdO/e3bVu4MCBWCwWkpKSzvraRYsWERERQceOHZkxYwaFhb/OhbR27Vo6depEdHS0a93gwYPJy8tj27ZtFW6zuLiYvLy8covZikOal/2Qs8/cICIiIh7GLU/bZWZmEhUVVW6dt7c34eHhZGZmVvi6//u//6N58+bExcWxZcsWHnjgAVJSUvjPf/7j2u5vixPgeny27c6dO5dZs2ZVdXdqRVbCrQzcfxGt/DrQy+wwIiIiHqRelafp06fz1FNPnXXMjh07qrz98ePHu37u1KkTsbGxDBgwgD179nDBBRdUebszZsxg6tSprsd5eXnEx8dXeXs1IbJJa3Y7j+I4Zjc1h4iIiKepV+Vp2rRpjB49+qxjWrVqRUxMDNnZ2eXWl5aWkpOTQ0xMzHm/X8+ePQHYvXs3F1xwATExMaxbt67cmKysLICzbtdqtWK1Ws/7fetC84hAAA4eK6TU7sDbyy3P0IqIiNQ79ao8RUZGEhkZec5xvXv35vjx42zcuJFu3boBsHr1ahwOh6sQnY/k5GQAYmNjXdt94oknyM7Odp0WXLlyJSEhIXTo0KGSe2Ou2BA/7vD5Hy2dB8lOa0dcs6ofWRMREZFfueXhiPbt2zNkyBDGjRvHunXr+O6775g0aRI33XQTcXFxAKSlpZGQkOA6krRnzx4ee+wxNm7cyP79+/noo4+49dZbueyyy0hMLPsq/6BBg+jQoQO33HILmzdv5rPPPuOhhx5i4sSJ9e7I0rlYLAb/57OGm7y/5Oj+n8yOIyIi4jHcsjxB2bfmEhISGDBgAEOHDqVv37688cYbrudtNhspKSmub9P5+vryxRdfMGjQIBISEpg2bRrDhw/n448/dr3Gy8uL5cuX4+XlRe/evbn55pu59dZby80L5U6OW5sAUJCp6QpERERqSr06bVcZ4eHhZ50Qs0WLFjidTtfj+Ph4vvrqq3Nut3nz5nz6qWfcULcouDmcTMJ5dK/ZUURERDyG2x55knMzwlsB4HfigMlJREREPIfKkwfzj2kDQFjRQZOTiIiIeA6VJw/WuFnZNwRj7RnY7ZrvSUREpCaoPHmw6GZtKHF64WfYyDqk27SIiIjUBJUnD+bl7cMdQS+RULSA3cWhZscRERHxCCpPHs47uh1FWNl3pMDsKCIiIh5B5cnDtfrlNi0qTyIiIjXDbed5kvPT2fcQc7z/QcjuKOB1s+OIiIi4PZUnD9c8oJgrvFdz8ESc2VFEREQ8gk7bebio5hcCEOvIpKS42OQ0IiIi7k/lycM1jm1OodOKt+Eg48BOs+OIiIi4PZUnD2dYLGR6l52yy0ndYXIaERER96fy1AAc928GQFFmislJRERE3J/KUwNQElZ2g2AjZ4/JSURERNyfylMD4BPZuuyHwqPmBhEREfEAmqqgAfDpdD0Xro0hyDeMJLPDiIiIuDkdeWoAmsdEUoA/WXnFFBSXmh1HRETErak8NQChAT6EB/oCuk2LiIhIdak8NRD3+K/gnz5zyN/2mdlRRERE3JrKUwPRySuVS722Yk/fbHYUERERt6by1ECUNiqbrsD7mKYrEBERqQ6VpwbCN6otAMEFB0xOIiIi4t5UnhqIsKYJAESXpuF0Ok1OIyIi4r5UnhqImJYXAtCYXI4f02SZIiIiVaXy1ED4B4dxmEYAZO7banIaERER96Xy1IAc8W3KYWcIh7MzzY4iIiLitlSeGpAlCX/n4uJ5rDU6mx1FRETEbak8NSAtosMB2JOdb3ISERER96Xy1IBcEBkEwJ7DKk8iIiJV5W12AKk7rUNsvO3zJE3zjmCzbcHHx8fsSCIiIm5HR54akJiIKHpbtnOBkU566i6z44iIiLglty1POTk5jBw5kpCQEMLCwhg7diz5+RWfjtq/fz+GYZxxWbp0qWvcmZ5fsmRJXexSrbN4e5Pu3QSAo/t+MjmNiIiIe3Lb03YjR44kIyODlStXYrPZGDNmDOPHj2fx4sVnHB8fH09GRka5dW+88QbPPPMMV1xxRbn1CxYsYMiQIa7HYWFhNZ7fLMf8W9Ai/wAnM3aaHUVERMQtuWV52rFjBytWrGD9+vV0794dgJdeeomhQ4fy7LPPEhcXd9prvLy8iImJKbdu2bJl/OlPfyIoKKjc+rCwsNPGnk1xcTHFxcWux3l5eZXZnTpV0qg15H+FJWe32VFERETckluetlu7di1hYWGu4gQwcOBALBYLSUlJ57WNjRs3kpyczNixY097buLEiURERNCjRw/mz59/znvBzZ07l9DQUNcSHx9fuR2qQ77R7QAIzt9nchIRERH35JblKTMzk6ioqHLrvL29CQ8PJzPz/GbPfuutt2jfvj19+vQpt3727Nn8+9//ZuXKlQwfPpy77rqLl1566azbmjFjBrm5ua7l4MGDlduhOhTWrAMAMbZU3SBYRESkCurVabvp06fz1FNPnXXMjh07qv0+J0+eZPHixTz88MOnPffbdV26dKGgoIBnnnmGe+65p8LtWa1WrFZrtXPVhdhWnTjuDOSAMwrn8TwiG4WaHUlERMStVKs82Ww2MjMzKSwsJDIykvDw8GqFmTZtGqNHjz7rmFatWhETE0N2dna59aWlpeTk5JzXtUrvv/8+hYWF3Hrrrecc27NnTx577DGKi4vdpiCdjV9QGH8M+CcHjxWx5FgpkY3MTiQiIuJeKl2eTpw4wb/+9S+WLFnCunXrKCkpwel0YhgGTZs2ZdCgQYwfP56LL7640mEiIyOJjIw857jevXtz/PhxNm7cSLdu3QBYvXo1DoeDnj17nvP1b731Ftdcc815vVdycjKNGjXyiOJ0ygVRwRw8VsSew/n0atXY7DgiIiJupVLXPD333HO0aNGCBQsWMHDgQD788EOSk5P5+eefWbt2LY888gilpaUMGjSIIUOGsGtX7UzE2L59e4YMGcK4ceNYt24d3333HZMmTeKmm25yfdMuLS2NhIQE1q1bV+61u3fv5uuvv+b2228/bbsff/wx//jHP9i6dSu7d+/mtddeY86cOdx99921sh9mcd2mJUu3aREREamsSh15Wr9+PV9//TUXXnjhGZ/v0aMHt912G/PmzWPBggV88803tGnTpkaC/t6iRYuYNGkSAwYMwGKxMHz4cF588UXX8zabjZSUFAoLC8u9bv78+a4jZL/n4+PDK6+8wpQpU3A6nbRu3ZrnnnuOcePG1co+mOUyxwZu9n2cwzvawjXLzY4jIiLiVgynvnJV4/Ly8ggNDSU3N5eQkBCz45xmx7cf0v6LUew3mtDike1mxxEREakXzvfzu8pTFYwaNYqvv/66qi8XE8W0SgSgiSOTkyeLTE4jIiLiXqpcnnJzcxk4cCBt2rRhzpw5pKWl1WQuqUVhMc0pxIqPYSdtX/WnfhAREWlIqlyePvzwQ9LS0pgwYQLvvfceLVq04IorruD999/HZrPVZEapYYbFiwzvslnQcw5sNTmNiIiIe6nWDOORkZFMnTqVzZs3k5SUROvWrbnllluIi4tjypQptfZtO6m+vMAWAJRk6QbBIiIilVEjt2fJyMhg5cqVrFy5Ei8vL4YOHcpPP/1Ehw4deP7552viLaSGlYaXfQvSWzcIFhERqZQqlyebzcYHH3zAVVddRfPmzVm6dCmTJ08mPT2dt99+my+++IJ///vfzJ49uybzSg3xiUtks6MVu0o0SaaIiEhlVPn2LLGxsTgcDkaMGMG6devo3LnzaWP69+9PWFhYNeJJbQntci39V4Xi57Qw0uHEYjHMjiQiIuIWqlyenn/+eW688Ub8/PwqHBMWFsa+ffuq+hZSi+Ib+ePrZaHI5iDt+EniwwPMjiQiIuIWqnza7pZbbjlrcZL6zdvLQqvIQLwpZXfaYbPjiIiIuI0qH3maOnXqGdcbhoGfnx+tW7fm2muvJTw8vMrhpHZNZwF9rP9l3eap0Okhs+OIiIi4hSqXpx9//JFNmzZht9tp164dAD///DNeXl4kJCTw6quvMm3aNL799ls6dOhQY4Gl5gQGh+J73I7lSIrZUURERNxGlU/bXXvttQwcOJD09HQ2btzIxo0bOXToEH/84x8ZMWIEaWlpXHbZZUyZMqUm80oN8o0tu8FzaL6mKxARETlfVb4xcJMmTVi5cuVpR5W2bdvGoEGDSEtLY9OmTQwaNIgjR47USFh3Ud9vDHzKoR1JNH1vEMedgYTMPITFq0am/RIREXFLtX5j4NzcXLKzs09bf/jwYfLy8oCyb9uVlJRU9S2klsVekIjdaRBmFJCett/sOCIiIm6hWqftbrvtNpYtW8ahQ4c4dOgQy5YtY+zYsQwbNgyAdevW0bZt25rKKjXMy9efDK9YALL2bDY5jYiIiHuo8gXjr7/+OlOmTOGmm26itLS0bGPe3owaNcp1S5aEhAT+8Y9/1ExSqRU5Aa1omp9OYdpW4Dqz44iIiNR7VS5PQUFBvPnmmzz//PPs3bsXgFatWhEUFOQac6ZZx6V+ORbTh+UppWQURXCp2WFERETcQJVO29lsNgYMGMCuXbsICgoiMTGRxMTEcsVJ3ENRl7FMst3Dh4UdzY4iIiLiFqpUnnx8fNiyZUtNZxETtI0OBmB3dj52R5W+eCkiItKgVPmC8Ztvvpm33nqrJrOICZqFB+DnDVH2DA5lNawpJURERKqiytc8lZaWMn/+fL744gu6detGYGBgueefe+65aoeT2udlMfjU+iCt7PvZsKMRzWN10biIiMjZVLk8bd26la5duwJlt2X5LcMwqpdK6lS+f1PI389JfeNORETknKpcntasWVOTOcREJeFtIf9bLEd1jzsREZFzqdb9OL755htuvvlm+vTpQ1paGgD//Oc/+fbbb2sknNQN6y/3uAs7scfkJCIiIvVflcvTBx98wODBg/H392fTpk0UFxcDZbdtmTNnTo0FlNrXuGUiAE1KD2C3O0xOIyIiUr9VuTw9/vjjzJs3jzfffBMfHx/X+ksuuYRNmzbVSDipGzGtOrnucZd2aL/ZcUREROq1KpenlJQULrvsstPWh4aGcvz48epkkjpm8fUn03WPu2Rzw4iIiNRzVS5PMTEx7N69+7T13377La1atapWKKl7Pza+ipdLryWlMNjsKCIiIvValcvTuHHjuPfee0lKSsIwDNLT01m0aBH33XcfEyZMqMmMUgcOXngnz5b+maQTEWZHERERqdeqPFXB9OnTcTgcDBgwgMLCQi677DKsViv33Xcfd999d01mlDqQEFt2xGlnRp7JSUREROq3Kh95MgyDv/71r+Tk5LB161Z++OEHDh8+zGOPPVaT+Sr0xBNP0KdPHwICAggLCzuv1zidTmbOnElsbCz+/v4MHDiQXbt2lRuTk5PDyJEjCQkJISwsjLFjx5Kfn18Le1C/tI8OJpJjNDn6PUUlNrPjiIiI1FvVmucJwNfXlw4dOtCjRw+CgoJqItN5KSkp4cYbb6zUKcKnn36aF198kXnz5pGUlERgYCCDBw+mqKjINWbkyJFs27aNlStXsnz5cr7++mvGjx9fG7tQr0QHe/Ot32QW+jxJ6p7tZscRERGptwyn0+ms6otXrVrFqlWryM7OxuEoPz/Q/Pnzqx3ufCxcuJDJkyef8xt+TqeTuLg4pk2bxn333QeUzUkVHR3NwoULuemmm9ixYwcdOnRg/fr1dO/eHYAVK1YwdOhQDh06RFxc3HllysvLIzQ0lNzcXEJCQqq1f3Vp7xPdaGXbzffdnqfP1beZHUdERKROne/nd5WPPM2aNYtBgwaxatUqjhw5wrFjx8ot9c2+ffvIzMxk4MCBrnWhoaH07NmTtWvXArB27VrCwsJcxQlg4MCBWCwWkpKSKtx2cXExeXl55RZ3lBvSDgBb+laTk4iIiNRfVb5gfN68eSxcuJBbbrmlJvPUmszMTACio6PLrY+OjnY9l5mZSVRUVLnnvb29CQ8Pd405k7lz5zJr1qwaTmyC6I5w9BP8j+00O4mIiEi9VeUjTyUlJfTp06cmszB9+nQMwzjrsnNn/ftgnzFjBrm5ua7l4MGDZkeqktAWnQGIKdpNNc7mioiIeLQqH3m6/fbbWbx4MQ8//HCNhZk2bRqjR48+65iqTsAZExMDQFZWFrGxsa71WVlZdO7c2TUmOzu73OtKS0vJyclxvf5MrFYrVqu1Srnqk7h23eFTaEYWh4/mEBnR2OxIIiIi9U6Vy1NRURFvvPEGX3zxBYmJieXubwfw3HPPVXqbkZGRREZGVjXSWbVs2ZKYmBhWrVrlKkt5eXkkJSW5vrHXu3dvjh8/zsaNG+nWrRsAq1evxuFw0LNnz1rJVZ/4hUZxxAgnwpnDwZQNREYMNjuSiIhIvVPl8rRlyxZXCdm6tfwFxoZhVCvU+UhNTSUnJ4fU1FTsdjvJyckAtG7d2jVlQkJCAnPnzuW6667DMAwmT57M448/Tps2bWjZsiUPP/wwcXFxDBs2DID27dszZMgQxo0bx7x587DZbEyaNImbbrrpvL9p5+5WRo5iY1ohHQvD6Wp2GBERkXqoyuVpzZo1NZmj0mbOnMnbb7/tetylSxegLFe/fv2AspsX5+bmusbcf//9FBQUMH78eI4fP07fvn1ZsWIFfn5+rjGLFi1i0qRJDBgwAIvFwvDhw3nxxRfrZqfqgZz2N/N+agq2o96MNjuMiIhIPVSteZ6++eYbXn/9dfbu3cvSpUtp0qQJ//znP2nZsiV9+/atyZxuxV3neQJYvTOL2xZuoF10MJ9NuczsOCIiInWm1ud5+uCDDxg8eDD+/v5s2rSJ4uJioGziyTlz5lR1s2KyhKgALjZ20vPoMoptpWbHERERqXeqXJ4ef/xx5s2bx5tvvlnuYvFLLrmETZs21Ug4qXuxIb4stj7BbO/5HNhb/6aFEBERMVuVy1NKSgqXXXb6aZ3Q0NBz3ipF6i/D20qad3MAjuxWCRYREfm9KpenmJgYdu/efdr6b7/9tspzMUn9kBvSFoCStJ9MTiIiIlL/VLk8jRs3jnvvvZekpCQMwyA9PZ1FixZx3333ueZNEvfkjLoQAP9jO0xOIiIiUv9UeaqC6dOn43A4GDBgAIWFhVx22WVYrVbuu+8+7r777prMKHUsuEUX2AkxJ/eYHUVERKTeqdZUBVB2j7vdu3eTn59Phw4dXBNUNmTuPFUBwMmcDPxfTMDhNDhy716iwsPNjiQiIlLrzvfzu8pHnk7x9fWlQ4cO1d2M1CP+4bHkGKGEk8vBnRuJ6vNHsyOJiIjUG5W65ik1NbVSG09LS6vUeKk/Poi9j+HFj7C+MPbcg0VERBqQSpWniy++mDvuuIP169dXOCY3N5c333yTjh078sEHH1Q7oJjDmXAlG53t2JJVbHYUERGReqVSp+22b9/OE088wR//+Ef8/Pzo1q0bcXFx+Pn5cezYMbZv3862bdvo2rUrTz/9NEOHDq2t3FLLOsaFArA1Lc/kJCIiIvVLlS4YP3nyJJ988gnffvstBw4c4OTJk0RERNClSxcGDx5Mx44dayOr23D3C8YBck8U8tiTj3KhsZ/rH1hIaHCA2ZFERERq1fl+flf723ZyOk8oTzidnJgVRzCFJF+5nM4XX2p2IhERkVpV6zcGFg9nGKT7l800nrtng8lhRERE6g+VJ6nQycZlM40bmZtNTiIiIlJ/qDxJhXzjuwAQnqfbtIiIiJyi8iQVik3oBUAr+z7yT2rKAhEREVB5krNoFN+Bk1gJMIrZl6JTdyIiIlBD5SknJweHw1ETm5L6xOJFmrU1AEf2bDI5jIiISP1Q5fK0fft2nnzySfr06UNkZCRRUVHceuutfPDBBxQUFNRkRjHR9xfO5OKiV1lu7212FBERkXqhUuUpJSWFadOm0aZNG3r16sX69eu58847ycrK4tNPP6V58+bMnj2biIgIrrjiCl577bXayi11JK51Fw4Txta0XLOjiIiI1AuVuj3L999/T0FBAS+++CIDBgzA19fX9VxERAQ9evTgscceY//+/fz3v//lP//5DxMmTKjx0FJ3OjYpu03L7sP5nCyx4+/rZXIiERERc2mG8VrgETOM/8arj91Fh5KtNL7xBToldjU7joiISK0438/vSh15AggODqZLly5069aNrl270rVrVzp06IBhGNUKLPXXIO9kWtu38eXO70DlSUREGrhKl6ennnqKjRs3snr1al5++WUcDgf+/v4kJiaWK1QXXXRRbeQVE+RHXARp2yBd37gTERGpdHm66667XD+fPHmSwMBA7r77bnJycvjhhx/4xz/+QUlJCXa7vUaDinn8m18MaYuJzNtqdhQRERHTVbo8/Za/vz8AI0aMIDExEYDS0lK2b99e/WRSb8R1vAS+h9b2feTk5RMeEmR2JBEREdPU+Azj3t7eriIlniE4ti15BGE1bOzdts7sOCIiIqbS7Vnk3AyD9MD2AOTtTjI5jIiIiLkqXZ5uv/12XnvtNdavX09xcdnNYvVNO89XHNWZE05/juYcMTuKiIiIqSp9zdOuXbtYunQpJ06cwNu77OWzZs2iX79+dO3alc6dOxMQEFDjQcVczkunkrjjcsLz/LjB6VRhFhGRBqvSR56++uorcnNzSUlJ4Z133uG+++7j+PHjzJw5k759+xIaGsqFF15YG1nLeeKJJ+jTpw8BAQGEhYWdc7zNZuOBBx6gU6dOBAYGEhcXx6233kp6enq5cS1atMAwjHLLk08+WUt74T4S4qPx9vLiaEEJh46dNDuOiIiIaar8bbs2bdrQpk0bbrrpJte6ffv2sWHDBn788ccaCXc2JSUl3HjjjfTu3Zu33nrrnOMLCwvZtGkTDz/8MBdddBHHjh3j3nvv5ZprrmHDhg3lxs6ePZtx48a5HgcHB9d4fnfj5+NFQkwIP6XlsuXgceLDdXRRREQapkqVp8zMTBo1aoTVaj3j8y1btqRly5bceOONAOzdu5dWrVpVP+UZzJo1C4CFCxee1/jQ0FBWrlxZbt3LL79Mjx49SE1NpVmzZq71wcHBxMTE1FhWT3GH3+d08H2PvetvgIueMDuOiIiIKSp12u79998nPDyc6667jgULFnD48OHTxiQlJfHggw9y4YUX1vtZxnNzczEM47TTfk8++SSNGzemS5cuPPPMM5SWlp51O8XFxeTl5ZVbPFGzEC9aWTIJOlz7RxZFRETqq0qVp0mTJrF582YuvfRSFi5cSNOmTenbty9z5sxh3LhxxMbGMmzYMLKzs3nyySfPWK7qi6KiIh544AFGjBhR7uZ/99xzD0uWLGHNmjXccccdzJkzh/vvv/+s25o7dy6hoaGuJT4+vrbjmyKi/aUAtCraRolNM8iLiEjDZDidTmdVX3z06FGWL1/Op59+SosWLbj22mvp3bt3lb+JNX36dJ566qmzjtmxYwcJCQmuxwsXLmTy5MkcP378vN/HZrMxfPhwDh06xJdffnnWOyfPnz+fO+64g/z8/ApPVxYXF7umbYCyuzLHx8ef867M7sZZUkjpnKb4YGfbjd9w4YWaDFVERDxHXl4eoaGh5/z8rtbtWRo3bsyoUaMYNWpUdTbjMm3aNEaPHn3WMdW9hspms/GnP/2JAwcOsHr16nOWm549e1JaWsr+/ftp167dGcdYrdYKi5UnMXwDOGhtQ6vinWRvV3kSEZGGqVrlqaZFRkYSGRlZa9s/VZx27drFmjVraNy48Tlfk5ycjMViISoqqtZyuZMTEV0gbSfGoSRgotlxRERE6pzb3p4lNTWV5ORkUlNTsdvtJCcnk5ycTH5+vmtMQkICy5YtA8qK0w033MCGDRtYtGgRdrudzMxMMjMzKSkpAWDt2rW88MILbN68mb1797Jo0SKmTJnCzTffTKNGjUzZz/om4II+AMTkbaEaZ3xFRETcVr068lQZM2fO5O2333Y97tKlCwBr1qyhX79+AKSkpJCbmwtAWloaH330EQCdO3cut61Tr7FarSxZsoRHH32U4uJiWrZsyZQpU5g6dWrt75CbaJrYj51fxrPO0ZqgY4U0DQ80O5KIiEidqtYF43Jm53vBmbu69uVv2Xwol7/f1JlrOzcxO46IiEiNON/Pb7c9bSfm6dq87BTmpgPHTE4iIiJS91SepNK6NW+ED6Vk7dtqdhQREZE657bXPIl5egQf4SfrWIqO+VJYfD0BVl+zI4mIiNQZHXmSSotq1h4MgzCjgJRtm8yOIyIiUqdUnqTyvHw46N8egKPbvzY5jIiISN1SeZIqORnXCwBr2g8mJxEREalbKk9SJeEd+gFwQWGybhIsIiINisqTVEmTjpdhw4s44ygpKfrWnYiINBwqT1IlhjWIVGvZjZKzf1plchoREZG6o6kKpMrSW9/E0uTt5OS1YIDZYUREROqIjjxJlYVfMpp59mv4JC2AUrvD7DgiIiJ1QuVJqiwhJoQQP28KSuxsS88zO46IiEidUHmSKvOyGAxs6uQay3fsS/7K7DgiIiJ1Qtc8SbWMNj4m0fdffPXzfuBas+OIiIjUOh15kmoJbtcPgGYnfsTucJobRkREpA6oPEm1xHcegMNp0JJ0ft69y+w4IiIitU7lSarFOyicVGsbANI3/c/kNCIiIrVP5Umq7UTcJQD4puomwSIi4vlUnqTawhMHA9C2YCNFJaUmpxEREaldKk9SbXGd+lGEL9HGMbZtSzY7joiISK1SeZJqM3z8WdhsDhcXvcrKzCCz44iIiNQqlSepEVGdr+AwYXy3+4jZUURERGqVypPUiL6tIwDYmp7LsYISk9OIiIjUHpUnqRFRIX5MCfuGt73nsn3DarPjiIiI1BqVJ6kxA/x/5jKvnzi5/TOzo4iIiNQalSepMV4X9AMgOvs7nE7dqkVERDyTypPUmBY9rwGgg+Nn9qYeNDmNiIhI7VB5khrjH9mCgz4t8DKc7E/62Ow4IiIitULlSWrU8Sb9AbDu+8LkJCIiIrVD5UlqVESXqwHoULiOE4VFJqcRERGpeSpPUqNiO17OEaMRmx0XsG7HHrPjiIiI1Di3LU9PPPEEffr0ISAggLCwsPN6zejRozEMo9wyZMiQcmNycnIYOXIkISEhhIWFMXbsWPLz82thDzyUlzevdfmIMbYH+GyfbhIsIiKex23LU0lJCTfeeCMTJkyo1OuGDBlCRkaGa3n33XfLPT9y5Ei2bdvGypUrWb58OV9//TXjx4+vyeger3/7OADWpBzWlAUiIuJxvM0OUFWzZs0CYOHChZV6ndVqJSYm5ozP7dixgxUrVrB+/Xq6d+8OwEsvvcTQoUN59tlniYuLq1bmhuLilo0I8PXC+0Q6O/YfokPLeLMjiYiI1Bi3PfJUVV9++SVRUVG0a9eOCRMmcPToUddza9euJSwszFWcAAYOHIjFYiEpKanCbRYXF5OXl1duacis3l68FfIma/3uJv27JWbHERERqVENqjwNGTKEd955h1WrVvHUU0/x1VdfccUVV2C32wHIzMwkKiqq3Gu8vb0JDw8nMzOzwu3OnTuX0NBQ1xIfryMtQU3aAxByQLdqERERz1KvytP06dNPu6D798vOnTurvP2bbrqJa665hk6dOjFs2DCWL1/O+vXr+fLLL6uVe8aMGeTm5rqWgwc1u3azPn8C4KKSHzmUkWVyGhERkZpTr655mjZtGqNHjz7rmFatWtXY+7Vq1YqIiAh2797NgAEDiImJITs7u9yY0tJScnJyKrxOCsquo7JarTWWyxOENutEulcT4uxp/Pzdf2h6Q+Uu7BcREamv6lV5ioyMJDIyss7e79ChQxw9epTY2FgAevfuzfHjx9m4cSPdunUDYPXq1TgcDnr27FlnuTyCYXAkfjBx++fjt/tTQOVJREQ8Q706bVcZqampJCcnk5qait1uJzk5meTk5HJzMiUkJLBs2TIA8vPz+ctf/sIPP/zA/v37WbVqFddeey2tW7dm8ODBALRv354hQ4Ywbtw41q1bx3fffcekSZO46aab9E27KojpdQMAiSfXcfhYrslpREREaobblqeZM2fSpUsXHnnkEfLz8+nSpQtdunRhw4YNrjEpKSnk5pZ9aHt5ebFlyxauueYa2rZty9ixY+nWrRvffPNNuVNuixYtIiEhgQEDBjB06FD69u3LG2+8Uef75wmi2vbmsCWCIKOI7d9+ZHYcERGRGmE4NYthjcvLyyM0NJTc3FxCQkLMjmOqLxY/z3+25mC/YCCvj73c7DgiIiIVOt/Pb7c98iTuocXA2/nU0Ys1ewvJK7KZHUdERKTaVJ6kVrWOCqZ1VBAldgefb9OUBSIi4v5UnqTW3dTOm7u9/oPXV0+aHUVERKTa6tVUBeKZrmxaRKzP+5zI9efo8SdoHBZqdiQREZEq05EnqXWxnfpz2BJBsHGSn75canYcERGRalF5ktpnsZDedCgA1h3LTA4jIiJSPSpPUifiLr0FgK5FSaRnZZ9jtIiISP2l8iR1IrL1xaR5NcVq2Nj55btmxxEREakylSepG4bBkZbXABC6S6fuRETEfak8SZ1p2X8U+U4/fi4KY+uhY2bHERERqRKVJ6kzIU0SeKjNh8woHcf7m9LNjiMiIlIlKk9Sp67r0RqAZT+mUWSzm5xGRESk8lSepE71bR1BbIiV+KIUfkj63uw4IiIilabyJHXKy2Lwt+gVLLc+hM/3z5sdR0REpNJUnqTOtex1LQDdCr4mIzPT5DQiIiKVo/IkdS62Q18OejfHz7CxY+V8s+OIiIhUisqT1D3D4Hj7/wOg+d7F2Ep14biIiLgPlScxRbvBd3ASKxc4D7Lhq4/NjiMiInLeVJ7EFL5Bjfg5+koALOvfMDmNiIjI+VN5EtM0GXQvALGFP7PzYJbJaURERM6PypOYJuKCzrzQ5Hn6lzzH2+uzzY4jIiJyXlSexFS9B1yLHS+W/XiI3EKb2XFERETOSeVJTNWjZTgJMcHYbSUsX/OV2XFERETOydvsANKwGYbB/RcV0/7YZErX+1I08Cf8rL5mxxIREamQjjyJ6S7t1YsAo4R4Mln36UKz44iIiJyVypOYzsc/mL2tbgYgZstrlGrSTBERqcdUnqReSLjmPk5ipa1zL+tX/8fsOCIiIhVSeZJ6wT8skp1x1wMQuO4FnE6nyYlERETOTOVJ6o2W10ynxOlNYulW1q3+0Ow4IiIiZ6TyJPVGWEwLtsWWHX3am7Qcu0NHn0REpP5ReZJ65YIbHuVmHmdG3vV8vDnd7DgiIiKncdvy9MQTT9CnTx8CAgIICws7r9cYhnHG5ZlnnnGNadGixWnPP/nkk7W0F/J7IRFN6H35FQC88MXP2OwOkxOJiIiU57blqaSkhBtvvJEJEyac92syMjLKLfPnz8cwDIYPH15u3OzZs8uNu/vuu2s6vpzF6D4taBzoy8mjh1i9aoXZcURERMpx2xnGZ82aBcDChQvP+zUxMTHlHv/3v/+lf//+tGrVqtz64ODg08aeTXFxMcXFxa7HeXl55/1aOV2g1ZvHOx3mDz9O4cj3jci/5DKCAoPMjiUiIgK48ZGn6srKyuKTTz5h7Nixpz335JNP0rhxY7p06cIzzzxDaWnpWbc1d+5cQkNDXUt8fHxtxW4w/jDoKnItITQhm41LHjc7joiIiEuDLU9vv/02wcHBXH/99eXW33PPPSxZsoQ1a9Zwxx13MGfOHO6///6zbmvGjBnk5ua6loMHD9Zm9AbBGhBC5sXTAeieOp+01L0mJxIRESlTr8rT9OnTK7yo+9Syc+fOGnmv+fPnM3LkSPz8/Mqtnzp1Kv369SMxMZE777yTv/3tb7z00kvlTsv9ntVqJSQkpNwi1ddpyO387NueQKOYg0sfMDuOiIgIUM+ueZo2bRqjR48+65jfX59UFd988w0pKSm899575xzbs2dPSktL2b9/P+3atav2e8v5MywWfK96Gv5zNb1OfM6WtZ+T2HuQ2bFERKSBq1flKTIyksjIyFp/n7feeotu3bpx0UUXnXNscnIyFouFqKioWs8lp2uReBkbv7ySbjmfEPz5NIq6rMPPz9/sWCIi0oDVq9N2lZGamkpycjKpqanY7XaSk5NJTk4mPz/fNSYhIYFly5aVe11eXh5Lly7l9ttvP22ba9eu5YUXXmDz5s3s3buXRYsWMWXKFG6++WYaNWpU6/skZ9b2lufJojEf2brz8urdZscREZEGrl4deaqMmTNn8vbbb7sed+nSBYA1a9bQr18/AFJSUsjNzS33uiVLluB0OhkxYsRp27RarSxZsoRHH32U4uJiWrZsyZQpU5g6dWrt7YicU3CjaH64fjXPL96G97cHubJLc9rH6royERExh+HU7etrXF5eHqGhoeTm5uri8Rp05z83smJbJt2b+LPkzkvx9vExO5KIiHiQ8/38dtvTdtLwzLr2Qrr4pfPY4XtI+tdMs+OIiEgDpfIkbiM6xI+ZXW20txykx/7X2bFhjdmRRESkAVJ5ErfS5eoJbAr5Az6GneBP7uREbo7ZkUREpIFReRL3Yhi0vu1NMomkqTOTnW+Nx+lwmJ1KREQaEJUncTshYREcv+JlSp0WLs5bSdJ7T5odSUREGhCVJ3FLCT2HsLHtFAC673yG7T+sMDmRiIg0FCpP4rZ6jHiIDcED+NHZmikrT5CZW2R2JBERaQBUnsRtGRYLHe58m1mNniSlIIAxC9dzoshmdiwREfFwKk/i1gICg3ltVG8igqzsyMjjzTdfpqTopNmxRETEg6k8iduLDw9gweiLmeL7X6YefZStr/4fjtJSs2OJiIiHUnkSj9CpaSiX/2EINqcXXfNW8+Ort+B02M2OJSIiHkjlSTxG537Xs7H709idBt1yPmXTK6NUoEREpMapPIlH6XX17azr8lRZgTr6MT++MgpHqS4iFxGRmqPyJB6n97A7SOo8B7vToOvRj/np79djK9URKBERqRkqT+KR+lx3F+svfo5ipw8f5LTk9nc2Uliii8hFRKT6VJ7EY/W66jY2XfM5Sy1D+ernwwx/bS0Hj+abHUtERNycypN4tN7durJoXE8ignxJy0gn/6VLSfnqPbNjiYiIG1N5Eo/XtVkjPprUl4cafUF79tJuzXh+/MdE7LZis6OJiIgbUnmSBiEuzJ9r7n2JrxoNB6DLoX+R+nQfjhzYZnIyERFxNypP0mD4+flx2T1v8W33FznuDKKlbTdBCy5ny5JHcZaWmB1PRETchMqTNCiGYdD3qlEcH/0lP/p0xg8biTuf54MXprDvSIHZ8URExA2oPEmD1KJlGzo9sJo17Wezy9mU2UcuY/ALX/PsZymcOKmjUCIiUjGVJ2mwvL296P/ne7HencRFbVpQUurg5TW72PzUYDa/8xdshblmRxQRkXrIcDqdTrNDeJq8vDxCQ0PJzc0lJCTE7DhyHpxOJ59vz2LFx0t5vughAI4TTGqbW2l3zVSswREmJxQRkdp2vp/fKk+1QOXJfdlK7Xy3fCEtk5+lOekAFOLHrvgbaHXVfQRHtzQ5oYiI1BaVJxOpPLm/wqIikpYvoMm2ebR17gfA4TR4ufWbXN7vjyQ2DcUwDHNDiohIjVJ5MpHKk+cosdlJWvkeQRtfo5Eti/4lf8OJhQvjQpgct5POiYlEtukBKlIiIm5P5clEKk+ex+l0smn3If658Qifbs3EUVrCOutdhBv5pHs14Uj8YKK6XUvMhZeCxcvsuCIiUgUqTyZSefJsOQUlrFr/E01+eISuJ3/Az7C5nsslmIPhvXF2vpnWva7C31dFSkTEXag8mUjlqeHIyD7Mzq/ew3fv53Qs3ECoUTbR5gul1/OK80Y6NQnl0nhvBls20KTT5YQ2ba8jUyIi9ZTKk4lUnhqmvMKTbEtaReG2T/nn8US+zI8HYJBlPW/4Pg/ASaykWVuTH34hvk27ENn2YiJadMTw8TczuoiI4OHlaf/+/Tz22GOsXr2azMxM4uLiuPnmm/nrX/+Kr69vha8rKipi2rRpLFmyhOLiYgYPHsyrr75KdHS0a0xqaioTJkxgzZo1BAUFMWrUKObOnYu3t/d551N5EqfTycGck6zbn0Pelk/ofnAhre17CDCKTxs72TGVPZEDaBUZyMX+mVzk3E5gdGvCYi+gUVwrDN8AE/ZARKThOd/P7/NvBPXIzp07cTgcvP7667Ru3ZqtW7cybtw4CgoKePbZZyt83ZQpU/jkk09YunQpoaGhTJo0ieuvv57vvvsOALvdzpVXXklMTAzff/89GRkZ3Hrrrfj4+DBnzpy62j3xAIZh0KxxAM0aB0C3O4A7yCssInnnZo7t2QAZm2mUu50WpXvZZothV1ouP6XlEuP1MTf7vFtuW8cIJccnmgL/WDa0mogR2ZbGQVbiOEqEJY+QiDhCI+Lw8rGas7MiIg2MWx55OpNnnnmG1157jb17957x+dzcXCIjI1m8eDE33HADUFbC2rdvz9q1a+nVqxf/+9//uOqqq0hPT3cdjZo3bx4PPPAAhw8frvCoVnFxMcXFvx5RyMvLIz4+Xkee5JxKbHZScwrYc6SQPYfzCd79MQnZn9LIlkm04zDBxsly44cWz2G7swUAE7w+4gGfJa7nThBAvhFEoVcwRV7BLIu5h4LQNgT7+XCBbRfNT27Hyz8UH/8gvKwB+PgF4e0XhG9AEN6NmuHvH4ifjxdWbwsWi6ZeEJGGx6OPPJ1Jbm4u4eHhFT6/ceNGbDYbAwcOdK1LSEigWbNmrvK0du1aOnXqVO403uDBg5kwYQLbtm2jS5cuZ9z23LlzmTVrVs3tjDQYvj5etI4OoXX0L/8n7TcFmAKUFatDh7PISd9DQdY+So4e4JKgbrQ86cuR/GLCcvzJLg6nkTMXH8NOMIUEOwuhNBtK4b6fs9jh9APgLq+P+bPPvyvMcWPxTNY7EwC42Wsl93u/R7HhSyk+lBq/LnbDmwUhd5Lq1x5vL4OLSpK5rGAFDosvTosPDosveHlhGF5g8WZr1FCOBbTCyzCIPLmH1se+A4sFp8W77MJ5o+xPw7CQHdGTgsB4vCwGQUVZRORuwWKxYBgWDMPAsFh+mZjU4ERYe4oDYzEA35JjhBzbXvacAQYWnBYLBgYYBieDm2MLiMEwwNuWT0Du7l/Glm3LsFjK/jQslAREYQ+IxAAs9pNY81L5ZbNlGTj1Hgal/o1xBJTdtscoLcI370D5X+pv5v4qtTbCHhBZ9sBhw5q7v9xQp2G4hjt8QygNiPrlQSm+eam/bpLypdbuG4T91FinA59fMpxp2jGHT+CvYwHfEwdOH3RqrLe/a6yBgfeJQ/Db/87+zRs4vXxxBP66Xe/8dHA6zrxhiw/2wOizjj21aafFG3tgjGu9V0EmhqP0tyN/zWBYsAfF/WZsFobDxpkZlAY3+XVsYTaGvaKbgf9+7BEMe1EFY6E0qIlrBywnj2IpPXmWsXFgWH4Zm4OltLDisYGxri+aWIqOYbEVnGVsDFi8fxl7HIstv+KxAVHgVXZQwFKci6XkxFnGRoKX9ZexeVhK8ioca/ePwOld9m+PUZKPV/Hxisf6Ncb5yzWfhq0Ar6JjZxkbjtPn18sYmoUH4Ottzi16PaI87d69m5deeumsp+wyMzPx9fUlLCys3Pro6GgyMzNdY35bnE49f+q5isyYMYOpU6e6Hp868iRSHb4+XjSNi6NpXBxwKQCXlxvRG3iB0tJSjuQcJu9oBgW5Odjyj2IrPM7I0D7klPqRd9JGXFZHNh3LxNd2Ai97Eb6OIqzOssWPImwWf7CXbTWYk4QYhcAv/5g7f1l+cSDzCD84jgLQwms7PXxWV7gP/0iNZrWj7MXDLV8zyndehWMnldzNckdvAIZafuBV3xcrHPsX23iW2vsB0M/yIwt9n6lw7EzbKN6xDwagl2U7S3wfr3Dsk7abmGe/BoBEYw8fWR+ucOzfS6/n+dKyo9itjUN8Yb2/wrGvl17J3NKRADThMN/53Vvh2H+WDuTh0tsACCePTX53Vjj2A/ulTLNNAMCPYnb6jalw7Cf2Hky0TXY93u/3fxWOXWO/iDG2B1yPt1vHnPF6PYAkRwJ/LpnperzBeicRxpk/WLc4WnJNyROux9/43ku85fAZx+5yNOGPJb/+vX7mez/tLIfOOPaQM4L+xb/+7+VD34fobDnzWYgcZxBdi99wPX7X53F6e20/49iTTl/aFy90PX7L5xkGeP14xrEALYoWcarUvezzd67ySqpwbIei+RRSVjCe8Z7Hjd5fVzi2W9FrHCUUgFneCxjlvbLCsX2LX+CQs6zMTvdezJ3eyyscO6j4KX52ln1W3ev1AVN8Pqhw7LXFs9nsbA3AOK/l/NVncYVjR5T8lbWOC4Gy/xh73GdBhWNvK7mP1Y6uQNm/EX87y78RE0vu4RNHL9fj1dMup1VkUIXja1O9Kk/Tp0/nqaeeOuuYHTt2kJCQ4HqclpbGkCFDuPHGGxk3blxtRzwjq9WK1arrTcQc3t7eRETFEhEVW259z3KPOgAVf2B/CNgdTopsdorye5BxfCIlRScpLj6Jw1aM3VaEo9SGw1bEuPCujPQOw2Z3EHjMlw2HI6G0BKe9BMNegtNRitNeCg47PSK60sKvJQ6nkyZ5x9l4dCiG047htIPTgcVpB6cdw+mgZdPWDLZGY3dAi8J4Uo53BKcTA+cvRz2cGL/82Sgsjq6+YTiBuOJI9ua3wsDpWk6NNXASFBpBe2sITqeTmNJQMgqifxmDa6zll9f5BYXS0icQp9NJlD2QnKLQCn9nXtZAYgPLPvwaO/w5Zqv4EL+XbyAxAb+MdfqTa6v4H3yLbwDRAWX/noQ6reTZAisci48/UX5lY61OyC/97ZcLfndFhrc/kb+MdTqhoNTP9dTvD1Q5vf2IsFpd2ygutbrGGL/brtPiS+PAXy9psNl9KeL0SxycgMPiS/hvxpbafTl5hrEApRZfGgX4uB7bHWcZa/gQ9puxjrOMLTGsvxvrU+HYYsO33FjnWbYLEObv4zryZJxjbKi/N76Gz3mNDfH3wf7LWK9zjfXzIeyXsd5n2TeA4N+M9XH6UOT0qXBs0G/G+jp9zzo2wPrrWOs5tluZsX6/GQtgMfHODvXqmqfDhw9z9OjRs45p1aqV69qj9PR0+vXrR69evVi4cCEWS8WH71avXs2AAQM4duxYuaNPzZs3Z/LkyUyZMoWZM2fy0UcfkZyc7Hp+3759tGrVik2bNlV42u739G07ERER9+OW1zxFRkYSGRl5XmPT0tLo378/3bp1Y8GCBWctTgDdunXDx8eHVatWMXz4cABSUlJITU2ld++yUwW9e/fmiSeeIDs7m6iossOeK1euJCQkhA4dOlRjz0RERMRTmHOlVTWlpaXRr18/mjVrxrPPPsvhw4fJzMwsd11SWloaCQkJrFu3DoDQ0FDGjh3L1KlTWbNmDRs3bmTMmDH07t2bXr3KzqEOGjSIDh06cMstt7B582Y+++wzHnroISZOnKjTciIiIgLUsyNP52vlypXs3r2b3bt307Rp03LPnToLabPZSElJobDw128wPP/881gsFoYPH15uksxTvLy8WL58ORMmTKB3794EBgYyatQoZs+eXTc7JiIiIvVevbrmyVPomicRERH3c76f32552k5ERETELCpPIiIiIpWg8iQiIiJSCSpPIiIiIpWg8iQiIiJSCSpPIiIiIpWg8iQiIiJSCSpPIiIiIpWg8iQiIiJSCSpPIiIiIpXglve2q+9O3fEmLy/P5CQiIiJyvk59bp/rznUqT7XgxIkTAMTHx5ucRERERCrrxIkThIaGVvi8bgxcCxwOB+np6QQHB2MYRo1tNy8vj/j4eA4ePKgbDtci/Z7rhn7PdUO/57qh33PdqO3fs9Pp5MSJE8TFxWGxVHxlk4481QKLxULTpk1rbfshISH6P2cd0O+5buj3XDf0e64b+j3Xjdr8PZ/tiNMpumBcREREpBJUnkREREQqQeXJjVitVh555BGsVqvZUTyafs91Q7/nuqHfc93Q77lu1Jffsy4YFxEREakEHXkSERERqQSVJxEREZFKUHkSERERqQSVJxEREZFKUHlyI6+88gotWrTAz8+Pnj17sm7dOrMjeZSvv/6aq6++mri4OAzD4MMPPzQ7kkeaO3cuF198McHBwURFRTFs2DBSUlLMjuVxXnvtNRITE12TCfbu3Zv//e9/ZsfyeE8++SSGYTB58mSzo3iURx99FMMwyi0JCQmm5VF5chPvvfceU6dO5ZFHHmHTpk1cdNFFDB48mOzsbLOjeYyCggIuuugiXnnlFbOjeLSvvvqKiRMn8sMPP7By5UpsNhuDBg2ioKDA7GgepWnTpjz55JNs3LiRDRs28Ic//IFrr72Wbdu2mR3NY61fv57XX3+dxMREs6N4pAsvvJCMjAzX8u2335qWRVMVuImePXty8cUX8/LLLwNl98+Lj4/n7rvvZvr06San8zyGYbBs2TKGDRtmdhSPd/jwYaKiovjqq6+47LLLzI7j0cLDw3nmmWcYO3as2VE8Tn5+Pl27duXVV1/l8ccfp3Pnzrzwwgtmx/IYjz76KB9++CHJyclmRwF05MktlJSUsHHjRgYOHOhaZ7FYGDhwIGvXrjUxmUj15ebmAmUf7FI77HY7S5YsoaCggN69e5sdxyNNnDiRK6+8sty/01Kzdu3aRVxcHK1atWLkyJGkpqaalkU3BnYDR44cwW63Ex0dXW59dHQ0O3fuNCmVSPU5HA4mT57MJZdcQseOHc2O43F++uknevfuTVFREUFBQSxbtowOHTqYHcvjLFmyhE2bNrF+/Xqzo3isnj17snDhQtq1a0dGRgazZs3i0ksvZevWrQQHB9d5HpUnETHNxIkT2bp1q6nXLniydu3akZycTG5uLu+//z6jRo3iq6++UoGqQQcPHuTee+9l5cqV+Pn5mR3HY11xxRWunxMTE+nZsyfNmzfn3//+tymnoVWe3EBERAReXl5kZWWVW5+VlUVMTIxJqUSqZ9KkSSxfvpyvv/6apk2bmh3HI/n6+tK6dWsAunXrxvr16/n73//O66+/bnIyz7Fx40ays7Pp2rWra53dbufrr7/m5Zdfpri4GC8vLxMTeqawsDDatm3L7t27TXl/XfPkBnx9fenWrRurVq1yrXM4HKxatUrXL4jbcTqdTJo0iWXLlrF69WpatmxpdqQGw+FwUFxcbHYMjzJgwAB++uknkpOTXUv37t0ZOXIkycnJKk61JD8/nz179hAbG2vK++vIk5uYOnUqo0aNonv37vTo0YMXXniBgoICxowZY3Y0j5Gfn1/uv2L27dtHcnIy4eHhNGvWzMRknmXixIksXryY//73vwQHB5OZmQlAaGgo/v7+JqfzHDNmzOCKK66gWbNmnDhxgsWLF/Pll1/y2WefmR3NowQHB592vV5gYCCNGzfWdXw16L777uPqq6+mefPmpKen88gjj+Dl5cWIESNMyaPy5Cb+/Oc/c/jwYWbOnElmZiadO3dmxYoVp11ELlW3YcMG+vfv73o8depUAEaNGsXChQtNSuV5XnvtNQD69etXbv2CBQsYPXp03QfyUNnZ2dx6661kZGQQGhpKYmIin332GX/84x/NjiZSaYcOHWLEiBEcPXqUyMhI+vbtyw8//EBkZKQpeTTPk4iIiEgl6JonERERkUpQeRIRERGpBJUnERERkUpQeRIRERGpBJUnERERkUpQeRIRERGpBJUnERERkUpQeRIRERGpBJUnEZHfGT16NMOGDTM7hojUU7o9i4g0KIZhnPX5Rx55hL///e/o5gsiUhGVJxFpUDIyMlw/v/fee8ycOZOUlBTXuqCgIIKCgsyIJiJuQqftRKRBiYmJcS2hoaEYhlFuXVBQ0Gmn7fr168fdd9/N5MmTadSoEdHR0bz55psUFBQwZswYgoODad26Nf/73//KvdfWrVu54oorCAoKIjo6mltuuYUjR47U8R6LSE1TeRIROQ9vv/02ERERrFu3jrvvvpsJEyZw44030qdPHzZt2sSgQYO45ZZbKCwsBOD48eP84Q9/oEuXLmzYsIEVK1aQlZXFn/70J5P3RESqS+VJROQ8XHTRRTz00EO0adOGGTNm4OfnR0REBOPGjaNNmzbMnDmTo0ePsmXLFgBefvllunTpwpw5c0hISKBLly7Mnz+fNWvW8PPPP5u8NyJSHbrmSUTkPCQmJrp+9vLyonHjxnTq1Mm1Ljo6GoDs7GwANm/ezJo1a854/dSePXto27ZtLScWkdqi8iQich58fHzKPTYMo9y6U9/iczgcAOTn53P11Vfz1FNPnbat2NjYWkwqIrVN5UlEpBZ07dqVDz74gBYtWuDtrX9qRTyJrnkSEakFEydOJCcnhxEjRrB+/Xr27NnDZ599xpgxY7Db7WbHE5FqUHkSEakFcXFxfPfdd9jtdgYNGkSnTp2YPHkyYWFhWCz6p1fEnRlOTaMrIiIict70nz8iIiIilaDyJCIiIlIJKk8iIiIilaDyJCIiIlIJKk8iIiIilaDyJCIiIlIJKk8iIiIilaDyJCIiIlIJKk8iIiIilaDyJCIiIlIJKk8iIiIilfD/2vz3ocdc5yMAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -490,7 +490,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -552,7 +552,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 538.128x200.667 with 1 Axes>"
       ]
@@ -563,8 +563,8 @@
     }
    ],
    "source": [
-    "ansatz = EfficientSU2(hamiltonian.num_qubits, reps=1)\n",
-    "ansatz.decompose().draw(\"mpl\")"
+    "ansatz = efficient_su2(hamiltonian.num_qubits, reps=1)\n",
+    "ansatz.draw(\"mpl\")"
    ]
   },
   {
@@ -683,7 +683,7 @@
     "\n",
     "time = 10.0\n",
     "evolution_problem = TimeEvolutionProblem(hamiltonian, time, aux_operators=aux_ops)\n",
-    "var_qrte = VarQRTE(ansatz, init_param_values, var_principle, Estimator())\n",
+    "var_qrte = VarQRTE(ansatz, init_param_values, var_principle, StatevectorEstimator())\n",
     "evolution_result_re = var_qrte.evolve(evolution_problem)"
    ]
   },
@@ -756,7 +756,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -802,7 +802,7 @@
     ")\n",
     "time = 10.0\n",
     "evolution_problem = TimeEvolutionProblem(hamiltonian, time, aux_operators=aux_ops)\n",
-    "var_qrte = VarQRTE(ansatz, init_param_values, var_principle, Estimator())\n",
+    "var_qrte = VarQRTE(ansatz, init_param_values, var_principle, StatevectorEstimator())\n",
     "evolution_result_re_eff = var_qrte.evolve(evolution_problem)"
    ]
   },
@@ -823,7 +823,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -864,7 +864,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -898,7 +898,7 @@
     {
      "data": {
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-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.0</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td colspan='2'>Mon Feb 19 11:24:44 2024 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>2.0.2</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.4.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.13.3</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td colspan='2'>Tue Jun 17 10:49:38 2025 CEST</td></tr></table>"
       ],
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@@ -910,7 +910,7 @@
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-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
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        "<IPython.core.display.HTML object>"
@@ -948,7 +948,7 @@
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diff --git a/docs/tutorials/12_gradients_framework.ipynb b/docs/tutorials/12_gradients_framework.ipynb
index def6562b..3afab8ce 100644
--- a/docs/tutorials/12_gradients_framework.ipynb
+++ b/docs/tutorials/12_gradients_framework.ipynb
@@ -33,8 +33,8 @@
     "The Qiskit Primitives work as an abstraction level between algorithms and (real/simulated) quantum devices. Instead of having to manually deal with tasks such as parameter binding or circuit transpilation, the `primitives` module offers a `Sampler` and an `Estimator` class that take the circuits, the observable Hamiltonians, and the circuit parameters and return the sampling distribution and the computed expectation values respectively.\n",
     "\n",
     "`qiskit.primitives` provides two classes for evaluating the circuit:\n",
-    "- The `Estimator` class allows to evaluate expectation values of observables with respect to states prepared by quantum circuits.\n",
-    "- The `Sampler` class returns quasi-probability distributions as a result of sampling quantum circuits."
+    "- The `StatevectorEstimator` class allows to evaluate expectation values of observables with respect to states prepared by quantum circuits.\n",
+    "- The `StatevectorSampler` class returns quasi-probability distributions as a result of sampling quantum circuits."
    ]
   },
   {
@@ -133,7 +133,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 370.704x117.056 with 1 Axes>"
       ]
@@ -200,11 +200,11 @@
     }
    ],
    "source": [
-    "from qiskit.primitives import Estimator\n",
+    "from qiskit.primitives import StatevectorEstimator\n",
     "from qiskit_algorithms.gradients import ParamShiftEstimatorGradient\n",
     "\n",
     "# Define the estimator\n",
-    "estimator = Estimator()\n",
+    "estimator = StatevectorEstimator(seed=42)\n",
     "# Define the gradient\n",
     "gradient = ParamShiftEstimatorGradient(estimator)\n",
     "\n",
@@ -230,7 +230,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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KFMuLpa4gNRgMKCkpqbW8oqKixlloBoPBfg/ADvr5t0Jl0pgnrlNfO1FzotfrkZaWhilTpshdis2SkpLQp08fGAwGq4I0OzsbWVlZAKyfO7dz586YO3cuXFxcoNFobKrbXhikEouOjsaePXtqLdfr9cjPz4dSqUTnzp1rtffv399yKjkRUWMWFBSEoKAgq7fr2rUrZsyYgQ0bNmDevHlWzZ3r6+tr81ds7I1B6iCXLl2CIAiIjIxsVpNVEzXUb4u+xpdRv0Hlfem+60pNz/PPP49u3bo1q7+DHCN1kAsXqi4p9bRNDUhE9EvNKUQB9kgdhkFK2j9NQPmNbAjGSjj7P4O2M76As4/tXwJvijq9Ngwhg3rCyd0V55b/A3kpafVv1MwEBARg5syZCAgIkLsUkgiD1EEYpBQyeSWcvav+eOq+XozC5PcR+vpnMlflYAKwe+hseLRphaT9S3DrVBb0BT/LXZVD+fn5YcKECXKXQRJikDpI9XRa9PS6ffRL3P5+M8yGcgiV5XDyavjZjs1FzpcHAQD6n27h5o+X0TquI/T/kOaiBU3F/fv3cerUKfTu3fupupRic8YxUiIH0P/rGG7tWY2Id/ei05qLCJm0HGZDudxlye5pPFO9sLAQ8+fPR2FhodylkEQYpEQOYNTfgcrNE06efjAbKvHzgc/lLkkW7cdWTUriERKAVr2jccuGK7wQNTY8tEvkAN494nH78N9x6fUoqDz94NV1MO7dduxlsRoDhUqJpH8uhZO7K069s/6pGx+l5olBSuQACidnhM+peam04Fc/kqkaeVRf8PvsJ1tlroRIWjy0S0TkQK6uroiKiqr3wtnUdLBHSkTkQGFhYdi8ebPcZZCE2CMlIiISgUFKRORA2dnZ6NOnD7Kzs+UuhSTCICUiciBBEGAwGJ7K79A2Vxwjpbq5usLpq01yV2EdCU/eUKuAtETJducQapW0+3Nyc8WEq3+Xdqd25uTGE3ikolKpMHLkSMn2t/TzbSh98ACeLVpg9tSxtW5LQaWS+E3QQAxSqpNCoQDUarnLkI1CAbg95e8OhUIBZ/en9zXwtFMoFHByku5NIAAwC1X/Ojk51brdlPHQLhERkQhN+2MAEVET07ZtWyQnJyM4OFjuUkgiDFIiIgdSq9Vo166d3GWQhHhol4jIgYqKirBo0SIUFRXJXQpJhEFKRORA9+7dwzfffIN79+7JXQpJhEFKREQkAoOUiIhIBAYpERGRCDxrl4hIhNjYWKvWDwkJwXvvvYfBgwdDo9HYqSpyJAYpEZEDaTQavP/++3KXQRLioV0iIiIRGKREREQiMEiJiIhEYJASERGJwCAlIiISgUFKREQkAoOUiIhIBAYpERGRCAxSIiIiERikREREIjBIiYiIRGCQEgDg6NGjGD58OEJDQ6FQKLBo0SK5SyIiqtfevXvRrVs3uLq6om3btli+fLnDa2CQEgBAr9ejY8eO+OSTTxAYGCh3OURE9Tp9+jSGDx+OhIQEZGZm4v3338f8+fPx2WefObQOXv2FAACJiYlITEwEAMydO1fmaoiI6rd8+XLExsbi448/BgBER0fj0qVLWLx4MaZNm+awOtgjJSKiJun48eOIj4+vsSw+Ph7Xrl1DQUGBw+pgj5SIiCSlvV4Eg9FUY5nRZLL8m6MtqHX7UR7uagS19q/3foqKimoNRVXfLioqQkhIiM2PwRoMUiIiklThzRLsPnSizraHZeVY/9Xex95WAJgy/mV7lygpHtolIiJJxfXshIjQYJu27RvbBeFtghq0rkajgU6nq7Hs5s2bljZHYZASEZGklAoFRif2g9rVxartWvv7YOiLvRq8fp8+fXDgwIEay/bv34/Q0FCHHdYFGKT0b3q9HpmZmcjMzERlZSV0Oh0yMzNx5coVuUsjoibI28sDvxrat8Hrq5RKjE0aCGenho84vvXWWzh16hQWLFiArKwsbNq0CWvWrMG8efNsKdlmCkEQBIfeIzVKhw8fxoABA2ot79evHw4fPuz4goioWfhy10Gcz8qrd734fr3R/7luVu//22+/xfz585GVlYXAwEDMnDkTb7/9tg2V2o5BSkREdvOwrBwr13+N+/qHj12nbUgg/mf8y1Aqm+ZB0qZZNcnmYVk5Cop+lrsMImoi3N3UGJXY/7HtLi7OGP1S/yYbogCDlKyUln4Bn/5tB7797qTcpRBRExEZFoK4Hp3qbEsaGAe/ll4OrkhazSZIFQoFFAoFAGD37t144YUX4OXlBX9/f4waNQpXr161rLtnzx7069cPLVu2hJeXF4YPH47c3NzH7ruiogKrVq3C888/j5YtW0KtViMqKgqzZ89GcXFxndv8+OOPmDt3LmJjYxEYGAhXV1cEBwdjzJgxSE9Pf+x97d27F4mJiWjVqhWcnZ3h5+eH6OhoTJo0CcePH7fx2ZHGg7JynDhzEUDVoRgiooZK6P8sAny9ayyLjghFry5RMlUknWYzRlodop9++immT5+OoKAgtG7dGllZWSgrK0NwcDDOnj2LLVu24K233oJGo0FgYKClPTAwEOfPn0dAQECN/d68eRMJCQk4e/YslEolnnnmGXh5eSEnJwcVFRVo06YNvv/+e4SHh9fYLiIiAlevXoWvry80Gg1cXFzw008/oaSkBE5OTti6dStGjhxZY5u1a9fijTfeAAD4+fkhNDQUZWVluH79OvR6PaZOnerwyZgfdeBoOr7/4Sw0rfzw5m9HWJ5zIqKGuF50C3/evAtmQUALdzXemjQaHi3c5C5LPKGZACAAENzd3YVNmzZZlpeUlAixsbECAOHll18W3NzcarQXFxcLPXv2FAAIc+fOrbFPs9ks9OvXTwAgvPTSS4JWq7W0lZaWCpMmTRIACHFxcbXq2bRpk5Cbm1tjmclkElJSUgQPDw+hZcuWQmlpqaXNYDAIPj4+AgBh7dq1gtForFHHkSNHhJSUFJufH7H0D8uEd5evF+Yu/ly4mK2VrQ4iatpS005X/R3J0cpdimSaXY90xowZWL16dY22/fv3IyEh4bHt+/btQ2JiIrp06YJz585Zlu/duxcvvfQSOnfujPT0dKjV6hrbmUwm9O7dGxkZGTh27Bj69OnToFrfeecdLFq0CMnJyRg3bhwAQKfTQaPRwMfHB7dv37buwddjzaYUlOrLRO2jorISFZUGKJVKeLg3g0+QRCQLQRBgMBjh4uIsdyk1eHq4YcbEETZt2+zm2p08eXKtZT169GhQe15eze86bd++HQAwceLEWiEKACqVCsOGDUNGRgYOHz5cK0hzc3OxdetWnDt3DiUlJTAYDACAW7duAQAyMzMtQRoQEAC1Wo27d+8iNTUVQ4YMafBjrk+pvgz39Q8k2ZfZbJZsX0T09CqvrJS7BMk0uyBt165drWWPjnvW1d6qVSsAVbP7POr8+fMAgA0bNmDnzp113l/1vI43btyosfxPf/oT5s2bB6PR+NhaS0pKLL+rVCrMnDkTS5YswdChQ9GjRw8MHjwYffv2Rb9+/eDlZftZbZ4e4nqQ7I0SUXMn6u+kzIeWJYN/j5FK2R4REWFZXt/PxIkTLdsdO3ZMACCoVCph0aJFwoULF4TS0lLBbDYLgiAIX3zxRa1tBKFqDHXNmjVChw4dauzb1dVV+N3vfieUlJRY/8SIxLFRIqIna3Y9Uil5eHgAAL755hskJSU1eLvNmzcDAGbNmoUFCxbUan+0J/oopVKJ6dOnY/r06SgoKEBaWhpSU1Px1VdfYcOGDbh+/TpSU1Otfhxixkgf7Y3uSj2OXanyfgWHiMgeOEZqJ506dUJmZiYuXrxoVZBqtVoAQN++dU/YfPJk/ZMZhISEYPz48Rg/fjxmzZqFmJgYHDx4EFqtFmFhYQ2uBZBmjJRjo0REdWOQPsGoUaOwZcsW/OUvf8GMGTMsPdT6uLlVHWv/5XXygKoTkPbs2WNVHZ06dYK3tzfu3r2LwsJCq4PU1mP/HBsloqcFx0gF+4yRPvo90ri4OOHChQs12k0mk3DixAlh6tSpwtWrVy3Lly1bJgAQwsLChCtXrliWX7x4UYiKihLUanWtMdJLly4JkydPFn744QfLWKogCILRaBRWrFghABDUarVw586d+p4KSXBslIioYZrd90gf93BsbS8uLsbw4cNx4sQJAEBoaCgCAwNRVlaGq1ev4sGDqsOdly9fRocOHQAApaWl6NGjB65cuQJnZ2dERUXBbDbj8uXL0Gg0eP3117Fw4UJMnDgRGzduBFD1VZju3bsDADw9PdGuXTuoVCrk5+dbxlTXrl2L1157zabnx1qcxYiIqGGazVy79uLv748jR45g48aNGDJkCB48eIDTp09Dq9UiIiICM2fOxJEjRxAZGWnZxtPTE8eOHcOkSZPg4+OD7OxsyxR/GRkZCA4OrnU/kZGRWLduHcaOHQuNRoO8vDycO3cOarUao0ePRlpamsNC9NE5dQf36ckQJSJ6gmbTIyXplNy9j50H0qB/WM7eKBFRPRik9FjlFZVQu7rIXQYRUaPGICUiIhKBY6REREQiMEiJiIhEYJASERGJwCAlIiISgUFKREQkAoOUiIhIBAYpERGRCAxSIiIiERikREREIjBIiYiIRGCQEhERicAgJSIiEoFBSkREJAKDlIiISAQGKRERkQgMUiIiIhEYpERERCIwSImIiERgkBIREYnAICUiIhKBQUpERCQCg5SIiEgEBikREZEIDFIiIiIRGKREREQiMEiJiIhEYJASERGJwCAlIiISgUFKREQkAoOUiIhIBAYpERGRCAxSIiIiERikREREIjBIiYiIRGCQEhERicAgJSIiEoFBSkREJAKDlIiISAQGKRERkQj/D4RNtP89TTexAAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 580.387x200.667 with 1 Axes>"
       ]
@@ -264,16 +264,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "State sampler gradient computed with parameter shift [[{0: 0.35355339059327373, 1: -0.3535533905932736}, {0: 0.0, 1: 0.0}]]\n"
+      "State sampler gradient computed with parameter shift [[{0: 0.354085, 1: -0.354085}, {1: 0.0, 0: 0.0}]]\n"
      ]
     }
    ],
    "source": [
-    "from qiskit.primitives import Sampler\n",
+    "from qiskit.primitives import StatevectorSampler\n",
     "from qiskit_algorithms.gradients import ParamShiftSamplerGradient\n",
     "\n",
     "param_vals = [[np.pi / 4, np.pi / 2]]\n",
-    "sampler = Sampler()\n",
+    "sampler = StatevectorSampler(default_shots=100_000, seed=42)\n",
     "gradient = ParamShiftSamplerGradient(sampler)\n",
     "pss_grad_result = gradient.run(qc_sample, param_vals).result().gradients\n",
     "print(\"State sampler gradient computed with parameter shift\", pss_grad_result)"
@@ -534,7 +534,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 538.128x200.667 with 1 Axes>"
       ]
@@ -557,7 +557,7 @@
     "\n",
     "# Define the Ansatz\n",
     "wavefunction = QuantumCircuit(2)\n",
-    "params = ParameterVector(\"theta\", length=8)\n",
+    "params = ParameterVector(r\"$\\theta$\", length=8)\n",
     "it = iter(params)\n",
     "wavefunction.ry(next(it), 0)\n",
     "wavefunction.ry(next(it), 1)\n",
@@ -603,7 +603,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Result of Estimator VQE: -1.8199999999619816 \n",
+      "Result of Estimator VQE: -1.8199999999894436 \n",
       "Reference: -1.85727503\n"
      ]
     }
@@ -616,7 +616,7 @@
     "optimizer = CG(maxiter=50)\n",
     "\n",
     "# Gradient callable\n",
-    "estimator = Estimator()\n",
+    "estimator = StatevectorEstimator()\n",
     "grad = LinCombEstimatorGradient(estimator)  # optional estimator gradient\n",
     "vqe = VQE(estimator=estimator, ansatz=wavefunction, optimizer=optimizer, gradient=grad)\n",
     "\n",
@@ -647,7 +647,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Result of classical optimizer: -1.8199999993744493 \n",
+      "Result of classical optimizer: -1.8199999996701823 \n",
       "Reference: -1.85727503\n"
      ]
     }
@@ -674,7 +674,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.0</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td colspan='2'>Mon Feb 19 11:24:10 2024 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>2.0.2</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.4.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.13.3</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td colspan='2'>Tue Jun 17 10:50:13 2025 CEST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -686,7 +686,7 @@
     {
      "data": {
       "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -721,7 +721,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/13_trotterQRTE.ipynb b/docs/tutorials/13_trotterQRTE.ipynb
index 7729fb86..97f7bcc7 100644
--- a/docs/tutorials/13_trotterQRTE.ipynb
+++ b/docs/tutorials/13_trotterQRTE.ipynb
@@ -179,7 +179,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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9YwOFZSJ9PQBo8v/UUF2H+spqtfavjLbW25b3XpP1qnLvDHF1QYnaN27omRhC38RIYZnY0AAQi5rUK61rUC8oymTy/l+aLkHJzWy1agMgP4NrbNNDrbbuhmGOiDqFlbsD9E2MMC/xXyrXMbK2QGVOAYDGmw6iXtqCWWc/R89B/RG1bAvKs+4q3a74ZtMv3aq8YtQUl8PCqY98mezeHakPXHoFGr/EjazMFZbVVVR3yBfr/WPLlB77YSM+CkPv4QNx9cuDyDga2+J+nZ8IhtdfQiD5NQlx675pcf2HjfwoDG5Pj1Pa9vD/1a39Z/DLK39V+xgP0uZ61X3vW6Or31+7Ud4YtOhxFCSkwcbHGf5rF+JCxI5Wb++zbJbKaWQerlfdqWTcnh4Hx0nD5LV5L5+JhC+/b/X21HoMc0TUaQqTMhD33i6V7TUPjTlzmDgU4j/PYth4OyPt+1/adfx7+zeyMpeHRgDoPXwgJh9UnBT4wXnFOsKDx1bFY/5EDJw/CdlnLyN+w74W99nT0wlBm5eiIqcAZ5d8Cll9g9p1JXz1I1IOKJ4d81/7HAA0CVuVd4rU3v+DtLledd/71urK91ffzBjBny9D1d0SnHhqHUZ88AIGLngM6YcvNBlnqsqt7842mSLGe9lMWHs6IXrFVoXlD06v0xLTvjYIWLcIRckZOBbyFiZ9+w78XpuL2yfi1DoDatSz8fenuqBErbbuhmGOiDpFaZoExjaWyP3ljwdOkalm4+uCYf8XiuyoK6gpKIXX0hDkRF9VOkWElbtDk2Umva1gZGWOnIz74+OKrt0GAFg62ytMTVKYlI4TTymGubKM5sfVyVrxGh5UkVOA2tIKWDrbK23vNdQdIz4IQ1nGncY58FrYv6GVOcZ/HQGxnh7OLtmM6vy2fYGV3Mhq8mV670u6tQGgNbS5XnXfe3V01fsLAAHrFsHcsTdOPfcxaovLEbvma9gH+2DUpy/hh3Gvob6i5TPN5bfzUH47T2GZ6+wxaHDt2656R322DPqmRohZtQ0NNXU49+pXmHFqM4K3LMex6W+3+k5mywGNvz9F1zLVautueDdrN+X21Fgsyo2EXWDLM6F3po58bBJpl5TvomDapye8/hKitN3Y9v44F31TYzz6t1dRW1KOmBVbcWH1DpTdzsPorSvkY88e1MOtH/pP9ldY5rNiFgDg9vGL8mWSX5MAAL2GeSisW1tSgdyYBIWfh7/QHlZfUQ3DZs6yPUwmleJO7DX0GurWpM2klxXG7nwDMqkUZ8I2tTgfmEgsxqN/fxUWTn0Q+87XuBt/s9V1aII216vue6+t+o3zg0foRNz6LgqZJy8BAGqLy3Fh9Q6YO/aG/9qFGqtt4MLH0O/RIbj65fcoTGi82aUsXYL4DXvR6xF3eC+f2ep99Rrmjqq8IpSmNL3Bobm27oZn5kjrtPWxST4vPwEbHxfY+LrAwqkPyjPzVE56GbxlucpxLWcWb0bG0V8VlhlZW8B72Uz0f9wfZv1sUVdWieIbWUjaeQyZJ+KU7qe7S9p5FH0f9YX/2oWwD/ZG7i9/oK68Emb9bGEf7IOGmjqcmPMeACBw4xJYDOiDn579UH4GJ/qlLZjy43oEb30ZP4d+qLDvwqQMjN62Ejf2nEJpai7sR3ljQEggJOcTkfbjefl6NQWlyD33BxwmPIJL7+9u1+u5G38T7vPG45GIZxrH7EllyDx5CfVVNSq3ST98Ho6ThsHWzw35l+9PUTJ25+sws7dBxrFYWA1yhNUgR6XbV90tQW70VfiFP4V+jw5B8c0s1FdWw2X2aJXHbO3dtp1Jm+tV973XRoaWpgja/BIqcgtwcY3iUx4yT8Qh5UC02pdbO4q5Y28Mf2cBCv9IazJfX/LOY3CaNrLVl1v1TY3Re8Rg3Np3Wq227ohhrptKiYxG2o/n0NBBM923VUc9NgkAhr0ViurCMhQmpMLQ0rRV20SvaPrcwge/dIHGu72mHvoQZn1tcGPPKRQlZ8DIyhxuT4/FhF2rcWH1DlzffVKtWrsDWX0Dfp7/EQYtmgzXOWPgF944yLpKUoS7l28h5c//X9e5j8J1zqNI2PaDwiXV/Mu3EP/xPvi/uxBefwlB4vbD8rbChFTEvbcLQ998FgMXTEJdeRWS/3kMv23Y2+SS2fVvTmDsjtdh4+uCgqupbX498Rv2wtDKHIMWPQ7DHmYQicWI9H9J5U0aAJB+6Dz831sE1zljFPpVn4DBAACnqSPgNHWEyu0l5xORG31Vvr6VuwPGbFvVbJ3aEOa0uV5133ttFPBBGMz62uCn0A9RW9r07tK2XG7tKMFblkNsqI+YVduUjpFU53Kr07QRMDA1xvV//6RWW3ckkqk7EIQ6VF1lNfa4ztd0GSqJxGKIjfTRUFXb8sodZM7Fr1CeeVftR9+Y9+8tv1Q288xnMDAzbvHM3C77OS3u13nWKDz6t1cR+87XSN55/zmDhpammBu/HWVpEhyaFK5029CU/8DgoacSaJK297fWWJQbqdZdgCKxGDNObUZhYjpiHhrQ3RV8VsyCz8tPIHLEcsFe0iPShJCTn6A88y7OhG1Sq601tO2zub04Zk4HiQ304b1sJmb8tAnzU/fg2evfYPrxjRj0/GT5OsrGzN1bZj/aB76vzsGTF7ZhQfpeOIcEyddxD52IaUc3IPTWvxF669+YefpT+IU/LW/3e/0plY85UjY+rqMemwSgxTFPqhiYmzQ7fYSBReNZvkqJ4p1ntaWVqK+sQV2l6stspHkyqRSX3t8N51mj0MO9X5cfP+kfR1FbUgHvl2Z0+bGJhKr/ZH9YDXTEpQ/+rVZbd8XLrDpGbKCPSfvWwH6UN7LPXkbKgRg01NSi5+D+cJo6Atf+dbzFffi/uxAiA33c3PMzasuqUPLn4NLR21bCdfYY3P3tBq5+cRC1JRXo4d4PA6aPxOVN+zuk/ugVXyBg3SJUF5bh6hcH5Mube2xSezx7YzcMLUzRUFOHO78mIX7jt8j/XXGwdu4vCZDW1WPYW8+ivrIaRckZMOxhDq8Xp8PQ0kyhTtJO2WcuY7fD0y2v2Akaaura/MByou7q9vE4/Ntpntpt3RXDnI7xXDIN9qO8cXXrQcRv2KvY2MLkpffoGRvi0GPhCpdWB4QEwnX2GKRERiFm5TbFcUmt3G9rtOWxSW1RlVeMxO2HUXA1FfWV1ejpOQCeS6Zhyg/v4+f5HykMGi5Lk+Ds0s8x4v3nMWnP2w/sowgn5r6HvDj1ZlwnIiLqSAxzOsblydGoKSrDlc++a9rYyuGR13efbDJGzuXJMQCAuHW7m+5HgMMuf/toj8K/bx+PQ+r3MZjx82YEfvwiDo56WaG9tqQChckZuLH3FAoT02FqZw2vpSEYv2s1TsxdpzCHGXWu1oxzJCLqTjhmTsdYutij5FYOGmrq2ryPEiVz9li62KFSUtjmiT87goGFKUx6WSn8iMQd14XL0iRIP3Qeli72sHS5P9Fr37FD8Nj+d3Dt6//hymffIfNEHK5/cwL/m7EGYj09jNywuMNqICIiUhfPzFET7blztbmbo0V6em3eLwCMWP98k7nhWpoaQl3lmY37MrK2BFJzAQA+y2ehvrIG2WcuK6xbdbcYd2KT0W/8IxAb6ENap9lpXoiIqHtimNMxpSk56OHWF2JDfUg7cA650tRc9J8cAGPbHs2enav5c+oFw57mwAMhS8/IACZ9rFCWLmnxWKoCobJnHlbdLW5F9a1374xc9QP7NbWzhkisfFygSF8PYn09le1ERESdjZdZdUzKwRgY9bTAkFc6dlxRyp83Iwx/Z0GzNzyUpjZeou072ldhueeL0yFu5Zk5VY9NKrmR1eQRTG25nKxvYgQ9I4Mmy629nTFgeiCKb2QqPKez+GYWDMxMMCAkUGF9c8fe6DNyMAqTMtp1WZuIiKg9eGZOxyTvPAbHx4ZjyKtzYOvniuyoK2ioroPVQEf0cO2Lk0+/36b9Zhy5gLQfzsHtqbGwdLZH5sk41JRUwNLFHv3G+uHHca8BAHKjE1ByKxuPhD8No54WKM/MQ++AQeg11B3VBa0bb9eWxyYBgMucMfL56IxtLCE20IfvK7MBAOVZd5Ea2XhWz9LFHhP3vI3bxy+iNC0X9ZU1sPZ0gvsz4yGTSnE+fLvCfq9+cRD9xvph9LaVsAvyarwBwt4Gg557DHpGBk3vGiYiIupCDHM6RlpXj5+eWQ+vpSFwfmI0hr35LBpq6lCaloub355p176jlm3BndhkuD87HkNemwtZgxRlt/OQfviCfB2ZVIpTz32MER+8gMFhUyCtrUd21BUcf3Itph76oFXHactjkwDAY94E2AV5KSwburpxLiLJ+UR5mKvKK0ZuzFXYj/KGy5OjoW9siMq8IqQdOo+ELw+i5JbiDSAFV1JwbMYa+K56Ek7TRsJjfuPjo/J/v4mEL3+A5EJiq14XERFRZ+DjvDRMFx6vRKpp2yNj2N+IiLTvs7m9eGaOiIi6TMD6F9D/8eEwd+yNQxPfQGFiutL1rAb1x8gPw2DcqwcAIP7jfbh9LLYLKyUSDoY5IiLqMhlHL+CPr37A1B9VD7vQMzHEhF2rEbPyS+RdvAaRWNx4hzwRKcUwR0REXebOr8ktruPyxGjc/e0G8i5eA9A4Fremk57PTKQLGOaIiEirWHk4oKG2DhN2/x/M7K1RmHwbceu+YaAjUoHzzBERkVYR6emh72hfXIjYjkOTwlEpKUDgx0s0XRaR1mKYIyIirVKRnY/c84molBQCAFIjo9FrqIeGqyLSXgxzRESkVdIPn4etnysMzE0AAP0mDEVhUrpmiyLSYhwzR0REXSbwkxfhMGEYTHpbYdK+Nagrr8LBoJcRtHkpMk9eQubJS6jIzsfVrQcx9fCHkEllqJQU4nz43zVdOpHWYpgjIqIucyFih9Ll599QDGupkdHyp7YQUfN4mZWIiIhIwBjmiIiIiASMYY6IiIhIwBjmiIiIiASMYY6IiIhIwBjmiIiIiASMYY6IiIhIwBjmiIiIiASsW4S5/Px8REREwM3NDcbGxnB0dMSqVatQUVGBsLAwiEQibNu2TdNlEhEREalN558AcfnyZUyZMgUSiQRmZmbw9PRETk4Otm7dipSUFBQWNj7I2c/PT7OFquDz8hOw8XGBja8LLJz6oDwzD5EByzRdFuko9jciIuHR6TCXn5+PkJAQSCQSvP7661i7di0sLCwAAJ988glWr14NfX19iEQi+Pr6arha5Ya9FYrqwjIUJqTC0NJU0+WQjmN/IyISHp0OcytXrkRWVhZWrFiBzZs3K7RFRERg7969uHLlCpydnWFpaamhKpsXOWIZym/nAQBmnvkMBmbGGq6IdBn7GxGR8OjsmLnk5GTs378ftra22LBhg9J1hg0bBgAYMmSIfNm98BcQEAAjIyOIRKIuqVeVe1+sRF2B/Y2ISHh0Nszt27cPUqkUoaGhMDc3V7qOiYkJAMUwd+vWLRw4cAB2dnbw9/fvklqJiIiI2kpnw9zp06cBAOPGjVO5TlZWFgDFMDdmzBjk5ubi0KFDmDhxYucWSURERNROOjtmLiMjAwDg5OSktL2+vh7nzp0DoBjmxOKOz7fDhw+HRCJR2mYgE2MtAjr8mKQdPNw9UCeSaroMOfY3IiLt+2wGADs7O1y6dKlN2+psmKuoqAAAVFVVKW3fv38/8vPzYWFhAWdn506tRSKRIDs7W2mboUgP6NOphycNysnNQa2sQdNlyLG/ERFp32dze+lsmLOzs0NRURHi4+MRGBio0Jabm4vw8HAAgK+vb6ff5GBnZ6eyzUAmBrTrjwPqQH3t+2rVX3/sb0RE2vfZDDSfFVqis2Fu4sSJSE5OxsaNGzFp0iR4eHgAAOLi4rBgwQLk5+cD6JrJgps7bVpXWY09rvM7vQbSjBs3b8DAVHum92B/IyLSvs/m9tLZGyAiIiJgY2ODzMxMeHl5wcfHB+7u7ggICICLiwvGjx8PQHG8HBEREZHQ6OyZOQcHB8TExCA8PBxRUVFIT0+Hp6cntm/fjiVLlsDV1RWA9oc5lzljYO7QCwBgbGMJsYE+fF+ZDQAoz7qL1MhoTZZHOob9jYhIeHQ2zAHA4MGDceTIkSbLy8vLkZ6eDrFYDG9vbw1U1noe8ybALshLYdnQ1fMAAJLzifxypQ7F/kZEJDw6HeZUSUxMhEwmg4eHB0xNmz5/MjIyEgCQlJSk8O8BAwZg+PDhXVcogOOz13bp8ah7Y38jIhKebhnmEhISAKi+xDp37lyl/37uueewa9euTq2NiIiISB0Mc0rIZLKuLIeIiIiozXT2btbmtBTmiIiIiISiW56Zu/fcViIiIiKh65Zn5oiIiIh0BcMcERERkYAxzBEREREJGMMcERERkYAxzBEREREJGMMcERERkYAxzBEREREJGMMcERERkYAxzBEREREJGMMcERERkYAxzBEREREJmEgmk8k0XUR3JpPJUF9Vo+kyqJPomxhBJBJpugw59jciIu37bG4vhjkiIiIiAeNlViIiIiIBY5gjIiIiEjCGOSIiIiIBY5gjIiIiEjCGOSIiIiIBY5gjIiIiEjCGOSIiIiIBY5gjIiIiEjCGOSIiIiIBY5gjIiIiEjCGOSIiIiIBY5gjIiIiEjCGOSIiIiIBY5gjIiIiEjCGOSIiIiIBY5gjIiIiEjCGOSIiIiIBY5gjIiIiEjCGOSIiIiIBY5gjIiIiEjCGOSIiIiIBY5gjIiIiEjCGOSIiIiIBY5gjIiIiEjCGOSIiIiIB+3/qe15id2PylgAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 788.961x200.667 with 1 Axes>"
       ]
@@ -209,9 +209,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 538.128x200.667 with 1 Axes>"
+       "<Figure size 872.572x200.667 with 1 Axes>"
       ]
      },
      "execution_count": 6,
@@ -220,7 +220,7 @@
     }
    ],
    "source": [
-    "result.evolved_state.decompose(reps=2).decompose(\"disentangler_dg\").decompose(\n",
+    "result.evolved_state.decompose(reps=5).decompose(\"disentangler_dg\").decompose(\n",
     "    \"multiplex1_reverse_dg\"\n",
     ").draw(\"mpl\")"
    ]
@@ -270,17 +270,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7ff5e869b9d0>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
+      "image/png": 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xaNmyJSwtLZGXl6e2TFPZo+r7e6YqsXn0uyAtLU1jubaa5BweALC1tcWSJUuQkpKCwYMHa6wjl8vx2muv4ccff1TeYvmwqp1aVffRzHfhwoUq/4pQKBRqpw5btmwJd3d3lJeXK8ueeOIJlJSUKP/FCgCXLl3Czz//rNW26RJ3XdVHX1W/+6HrB3Hx4sUq7xcuXAgAWn1RyeVy9O/fH//6179ULh8VFRUhIyMDvXr1UrvcVlt/2owJQ25DTbTdp9oeT23HcnV9aLNfdB0H+fn5aNWqVa31GnoOz+3bt3H8+HGV3/iKjIwEAHz99dcqdb/66iuYmpqq3FJeF/qMa12EhobC0dERa9euxdq1axEYGKh2SUqfz0FtdNkubb9P6zKmtY1Z3+9KuVyOsLAw/PLLL8jPz1eWHzt2DJs3b9a6f0MdB7lcjtDQUPzyyy8qc2vy8vLw22+/abV+fX7P2NvbA4BK2xkZGfj9998BQO3nSrTVZM/wAKj2VNrDZs2ahe3btyMoKAhjxoxBx44dce3aNeTm5mLr1q3KU+MvvvgiVq9eDXt7e3Ts2BHZ2dnYunUrWrRooWzr5s2baNWqFV5//XX4+fnB1tYWW7duxf79+1Wy7iFDhmDKlCl45ZVXMH78eNy+fRtLlixBu3bttJ7UqW3chmDovgICAgAAH330EYYMGQIzMzMMHjy41h9AO3PmDF566SUMGDAA2dnZ+Mc//oGhQ4dq/Uu4n3zyCTIzM9GrVy+MGzcOpqamWLZsGcrLy5Genq5zf9qMCUNvQ3V02afaHE9tx7Im2u4XXceBj48Ptm3bhvT0dLi7u8PX11fZxsMMNYdn0aJFuHHjhvIL/9dff1X+SnJCQoLyS3ffvn3o27cvkpOTkZKSAgDw9/fHqFGjsHLlSty/fx8hISHYsWMH1q1bh6lTp6pMBAUenO2qqqMrXce1LszMzPDqq69izZo1KCsrw9y5c9Xq6PM50Ia226Xt92ldxrS26vJdOX36dGzatAm9e/fGuHHjcP/+fSxcuBCdOnVSSeaqY+jjkJKSgi1btuCZZ57BO++8A4VCgUWLFqFz585aPcOtPr9nfH194ePjgwULFsDa2homJiaYNWsWIiMj8cMPPyAlJQWJiYno0qWLbhut831dRvLwbek1efS2dCGEKCoqEnFxccLT01OYmZkJV1dX0a9fP7F8+XJlnevXr4vY2Fjh5OQkbG1tRVhYmDh+/Ljw8vJS3sJdXl4uJk+eLPz8/ISdnZ2wsbERfn5+4ssvv1SLY8uWLaJz587C3NxctG/fXvzjH/+o9rb0y5cva9wWbeKuqZ2YmBhhY2Oj1m5ISIjo1KmTzn1V14+mWxZnzpwpPDw8hImJSa23B1e1e/ToUfH6668LOzs74eDgIOLj48WdO3e02tYqubm5IiwsTNja2gpra2vRt29fsXv3br3602ZM6Nqmvrel17RPNdWv7XjqMpYfpct+0WUcXLhwQXnsAIgFCxbUGktdeHl5aXU7e9Uty8nJySrrV1RUiJSUFOHl5SXMzMxE27ZtxRdffKHWz82bN7W6Xb2629KF0G1cV/fZqE5mZqYAIGQymSgoKFBbru3xrqn/6sa0NtslhHbfp3UZ09reli6Edt+V1a27c+dOERAQIMzNzUWbNm3E0qVLNd5er0ldj4OmmLKysoS/v78wNzcXTzzxhPjqq6/Ee++9JywtLWtdV5t9UZdj8scff4innnpKWFhYCAcHB/HRRx+JyspKMWrUKGFqaiq++eabWtt4lEwIPiGNjCslJQXTp0/H5cuX9b42S9RYbdy4ES+++CL+/PNP3f9FStTAwsPDceTIEYP9tEJj0mTn8BARNQXbt2/HkCFDmOxQo3Pnzh2V96dOncLGjRsNNgetsWnSc3iIiBq7OXPmGDsEIo3atGmDkSNHok2bNjh37hyWLFkCc3NzvP/++8YOrV4w4SEiInoMDRgwAN9//z0KCwthYWGB4OBgpKamGuRHMxsjzuEhIiIiyeMcHiIiIpI8JjxEREQkeUx4iIiISPKY8BAREZHkMeEhIiIiyWPCQ0RERJLHhIf09s0330Amk6k87dgQdQ25LtW/pnx8Gjr2prCv9u/fj6effho2NjaQyWRaPUiyOk1he+nxwYTnMVf1hXTgwAGDtLd7926kpKTgxo0bBmnPkAwRW0Nv361bt5CcnIwBAwbA0dERMpkM33zzjca65eXlmDJlCtzd3WFlZYWgoCBkZmbWqX9Djw8pqWksNObPQU3u3buHiIgIXLt2DV988QVWr14NLy8vY4elkw0bNkAmk2HNmjUN1uf+/fsRHx+PTp06wcbGBq1bt0ZkZCROnjzZYDGQFnR+3ChJirZPodfk/v374s6dO6KyslJZNmfOHI1P1dVUV9cYa3rStjaqi62h29DFmTNnBADRunVr0adPHwFArFq1SmPdIUOGCFNTUzFp0iSxbNkyERwcLExNTcV///tfvfvXZnzU5dgaW13GVk1joT4+Bw3h2LFjAoBYsWKFQdozxvZ++umnAoA4cuRIg/X52muvCVdXV5GQkCBWrFghZs6cKVxcXISNjY04fPhwg8VBNeOjJUhvcrkccrnc4HXp/7m5ueHSpUtwdXXFgQMH0LNnT4319u3bhzVr1mDOnDmYNGkSAGDEiBHo3Lkz3n//fezevbveYmzsx7asrAw2NjbGDgNA499XxcXFAIDmzZsbpD1jbO+hQ4dgYWGB9u3bN1ifiYmJyMjIgLm5ubIsKioKXbp0waxZs/CPf/yjwWKh6vGSFqlISUmBTCZDXl4eRo4ciebNm8Pe3h6xsbG4ffu2St1Hr8+npKRg8uTJAAAfHx/IZDLlck3X8s+dO4dx48ahffv2sLKyQosWLRAREaH39f6bN29i4sSJ8Pb2hoWFBVq2bInnn38eubm5NcambSy1tQEAFy5cwKhRo+Di4gILCwt06tQJK1euVIv1+PHjyM/Pr3WbLCws4OrqWmu99evXQy6X46233lKWWVpaYvTo0cjOzkZBQUGtbehL0zjQdgwB2u0zbcdKVd9Hjx7F0KFD4eDggF69eum0PXUdC7p+Dqr2wejRo+Hu7g4LCwv4+PjgnXfeQUVFhU77qSZ//PEHBg4ciGbNmsHW1hb9+vXDnj17lMtHjhyJkJAQAEBERARkMlmNT82u6fNW5eHtPXv2rHJfaHo9uj/03dZDhw6hY8eODZpoPf300yrJDgA8+eST6NSpE44dO9ZgcVDNeIaHNIqMjISPjw/S0tKQm5uLr776Ci1btsTs2bOrXefVV1/FyZMn8f333+OLL76Ak5MTAMDZ2Vlj/f3792P37t0YMmQIWrVqhbNnz2LJkiXo06cPjh49Cmtra51iHjt2LNavX4/4+Hh07NgRV69exa5du3Ds2LFaY9MmltraKCoqwlNPPQWZTIb4+Hg4Ozvjt99+w+jRo1FaWoqJEycqY/X19UVISAh27Nih0zZW548//kC7du3QrFkzlfLAwEAAwMGDB+Hp6WmQvrSlzRjSdp/pOlYiIiLw5JNPIjU1FULHxwXWdSzo+jm4ePEiAgMDcePGDbz11lvo0KEDLly4gPXr1+P27dswNzfXaWxpcuTIEfTu3RvNmjXD+++/DzMzMyxbtgx9+vTBzp07ERQUhLfffhseHh5ITU3F+PHj0bNnT7i4uFTbZk2ft+7du6vVd3Z2xurVq1XK7t27h3fffVclWajLtpaXl+PkyZN44403atwfj8ZQUlKiVV1HR0eYmGh3nkAIgaKiInTq1EnrWKieGfuaGhnXo3M0kpOTBQAxatQolXqvvPKKaNGihcZ1H56nUN3cBU11b9++rRZPdna2ACC+++67GtfVxN7eXsTFxVW7vKY5F9rGUlMbo0ePFm5ubuLKlSsq5UOGDBH29vYqfQAQISEhNW7Po/bv31/tHJ5OnTqJ5557Tq38yJEjAoBYunSpTn1V0WYOz6PHR5cxpO0+0/b4VPUdHR2t0/bpMy71mcOjqb8RI0YIExMTjfu4au6LLmNLk/DwcGFubi5Onz6tLLt48aKws7MTzz77rLJs+/btAoBYt25dje0JUfvnTYjaP7vjxo0TcrlcbNu2TVlWl23NyckRAMRnn31Wa/xVqrZZm5cuc71Wr14tAIivv/5a63WofvGSFmk0duxYlfe9e/fG1atXUVpaarA+rKyslP9/7949XL16FW3btkXz5s1VTotrq3nz5ti7dy8uXrzY4LEIIfDjjz9i8ODBEELgypUryldYWBhKSkpU2hFCGOzsDgDcuXMHFhYWauWWlpbK5Q2ttjGkyz7T9fg82rcuDD0ua1JZWYlffvkFgwcPRo8ePdSWy2QyncfWoxQKBbZs2YLw8HC0adNGWe7m5oahQ4di165den2u6/J5A4DvvvsOX375JdLT09G3b18Aun+OHnXo0CEAQNeuXbWOw8/PD5mZmVq9tLm8DDy4ZB0XF4fg4GDExMRoHQvVL17SIo1at26t8t7BwQEAcP36dbXLJvq6c+cO0tLSsGrVKly4cEHl0oO2p5gflp6ejpiYGHh6eiIgIAAvvPACRowYofIlX1+xXL58GTdu3MDy5cuxfPlyjXWqJoTWBysrK5SXl6uV3717V7m8odU2hnTZZ7oeHx8fH73jNvS4rMnly5dRWlqKzp0711inLmPr8uXLuH37tsZJvL6+vqisrERBQYHOl17q8nk7ePAgxo4di+joaCQmJqrEWpdt/fPPPwE8SGK05eDggNDQUK3r16awsBCDBg2Cvb29cm4dNQ5MeEij6j6kQsf5EDVJSEjAqlWrMHHiRAQHB8Pe3h4ymQxDhgxBZWWlzu1FRkaid+/e+Pnnn7FlyxbMmTMHs2fPxk8//YSBAwfWayxVdd54441q/0Wny786deXm5oYLFy6olV+6dAkA4O7uXm99V6e2MaTLPtP1+NQlwTP0uKwrY4+t6uj7ebt+/Tpee+01tGvXDl999ZXKsrpu66FDh+Dq6lrtfClNKioqcO3aNa3qOjs715jAlJSUYODAgbhx4wb++9//GuVzR9VjwkMG9ejdFjVZv349YmJi8NlnnynL7t69W6cfa3Nzc8O4ceMwbtw4FBcXo3v37vj0008xcODAGmPTNpbq2nB2doadnR0UCoVB/7WorW7dumH79u0oLS1VOQO3d+9e5fLGRpd9Vh9jpa591TSetP0cODs7o1mzZvjrr79qrFOXseXs7Axra2ucOHFCbdnx48dhYmKi94T2mj5vmlRWVmLYsGG4ceMGtm7dqjbZvK7bevjwYfj7++u0zu7du5WX1Gpz5swZeHt7a1x29+5dDB48GCdPnsTWrVvRsWNHneKg+sc5PGRQVb93os0fIrlcrnbGaOHChVAoFDr3q1Ao1C43tGzZEu7u7spLPTXFpm0s1bUhl8vx2muv4ccff9T4x+vy5csq77W9LV1br7/+OhQKhcplgPLycqxatQpBQUENfoeWNnTZZ4YcK9rEVZexUNuyh5mYmCA8PBy//vqrxl+zFkLoPLYeJZfL0b9/f/zrX/9SuR2+qKgIGRkZ6NWrl86XqbX5vGkyffp0bN68Gd9//73Gy4512dZLly7h8uXLOp/tMsQcHoVCgaioKGRnZ2PdunUIDg7WKQZqGDzDQwYVEBAAAPjoo48wZMgQmJmZYfDgwRrrvvjii1i9ejXs7e3RsWNHZGdnY+vWrWjRooXO/d68eROtWrXC66+/Dj8/P9ja2mLr1q3Yv3+/8l/q1cVmY2OjdSw1tTFr1ixs374dQUFBGDNmDDp27Ihr164hNzcXW7duVTltrstt6YsWLcKNGzeUk0N//fVXnD9/HsCDyy/29vYICgpCREQEpk6diuLiYrRt2xbffvstzp49i6+//lqtTZlMptNt8StXrsSmTZvUyidMmKDV+tXRdp8ZcqzUxhBjQZfPQWpqKrZs2YKQkBC89dZb8PX1xaVLl7Bu3Trs2rULzZs312lsafLJJ58gMzMTvXr1wrhx42Bqaoply5ahvLwc6enpOu8jbT5vjzp8+DBmzpyJZ599FsXFxWo/xld1K7m+21o1YTkvLw+zZs1SWz5ixAiNl5gMMYfnvffew7///W8MHjwY165dq3bbyMga9qYwamyquy398uXLGus9fFtmdbeczpw5U3h4eAgTExPlck11r1+/LmJjY4WTk5OwtbUVYWFh4vjx48LLy0vExMTU2s/DysvLxeTJk4Wfn5+ws7MTNjY2ws/PT3z55Ze1xqZLLDW1IYQQRUVFIi4uTnh6egozMzPh6uoq+vXrJ5YvX67SBnS4Ld3Ly0ur22Tv3LkjJk2aJFxdXYWFhYXo2bOn2LRpk1p7N2/eFADEkCFDau27at9X9yooKKj2tnRtxpC2+0zb41Nd37Vtnz7jUoiax4K2nwMhhDh37pwYMWKEcHZ2FhYWFqJNmzYiLi5OlJeX67SfapKbmyvCwsKEra2tsLa2Fn379hW7d+9WqaPtbenaft4e3t7abv9+mD7bmp6eXmP7hYWFWu0nfYSEhGi9bWQ8MiEMOAuViBq9jRs34sUXX8Sff/6JLl26GDscIqIGwTk8RI+Z7du3Y8iQIUx2iOixwjM8REREJHk8w0NERESSx4SHiIiIJO+xuy29srISFy9ehJ2dnU4/kkdERETGI4TAzZs34e7urvVT6x/22CU8Fy9ebJQ/wkZERES1KygoQKtWrXRe77FLeOzs7AA82GGGeggmERER1a/S0lJ4enoq/47r6rFLeKouYzVr1owJDxERUROj73QUTlomIiIiyWPCQ0RERJLHhIeIiIgk77Gbw0NERNKmUChw7949Y4dBejA3N9frlnNtMOEhIiJJEEKgsLAQN27cMHYopCcTExP4+PjA3Nzc4G0z4SEiIkmoSnZatmwJa2tr/rhsE1P1w8CXLl1C69atDX78mPAQEVGTp1AolMlOixYtjB0O6cnZ2RkXL17E/fv3YWZmZtC2OWmZiIiavKo5O9bW1kaOhOqi6lKWQqEweNtMeIiISDJ4Gatpq8/jx4SHiIiIJI8JDxEREdW78vJyxMfHIyEhAeXl5Q3ev1EnLf/++++YM2cOcnJycOnSJfz8888IDw+vcZ0dO3YgMTERR44cgaenJz7++GOMHDmyQeIlIqKmZ/Q3+xu0v69H9mzQ/urLzZs3kZSUhJ9//hnFxcXw9/fH/Pnz0bOn6vYtXrwYc+bMQWFhIfz8/LBw4UIEBgaqtZeRkYHQ0FCYmJhg9erVePPNNxtqUwAY+QxPWVkZ/Pz8sHjxYq3qnzlzBoMGDULfvn1x8OBBTJw4EW+++SY2b95cz5ESERE9Xt58801kZmZi9erVOHz4MPr374/Q0FBcuHBBWWft2rVITExEcnIycnNz4efnh7CwMBQXF6u1V1lZicrKSgAPfjOpocmEMXrVQCaT1XqGZ8qUKdiwYQP++usvZdmQIUNw48YNbNq0Sat+SktLYW9vj5KSEj4tnYhIIu7evYszZ87Ax8cHlpaWKsuayhmeffv24f3338fevXvh5eWFf/zjH8jNzcV//vMf/Pvf/zZwlDW7c+cO7Ozs8K9//QuDBg1SlgcEBGDgwIH45JNPAABBQUHo2bMnFi1aBOBBUuPp6YmEhAR88MEHKm3evXsXkyZNgkwmw5w5c9SOU1Wd6o5jXf9+N6k5PNnZ2QgNDVUpCwsLQ3Z2drXrlJeXo7S0VOVFRETUmOzZswchISEYNGgQDh06BF9fX8yYMQOzZ8/G9OnTVeqmpqbC1ta2xld+fn6d4rl//z4UCoVa0mFlZYVdu3YBACoqKpCTk6Pyd9nExAShoaEa/y5bWlpi0aJFWLhwocZkp741qR8eLCwshIuLi0qZi4sLSktLcefOHVhZWamtk5aWpjZYiJq6+Kx4nddZ1G9RPURCj9LnbEJjnvOxYfGfOq8zKM6vHiKRtsTERERERGDy5MkAgOjoaERHR+Pll1+Gv7+/St2xY8ciMjKyxvbc3d3rFI+dnR2Cg4Mxc+ZM+Pr6wsXFBd9//z2ys7PRtm1bAMCVK1egUCg0/l0+fvx4nfqvD03qDI8+pk6dipKSEuWroKDA2CEREREpnT9/HtnZ2Rg7dqyyzNTUFEIIjf9gd3R0RNu2bWt8mZpqPp/xwQcfQCaT1fiqSlZWr14NIQQ8PDxgYWGBBQsWIDo6ut4e7lnfmtQZHldXVxQVFamUFRUVoVmzZhrP7gCAhYUFLCwsGiI8IiIinR07dgwA0L17d2XZiRMnEBgYiC5duqjVT01NRWpqao1tHj16FK1bt1Yrf++992q9s7lNmzYAgCeeeAI7d+5EWVkZSktL4ebmhqioKOVyJycnyOVyjX+XXV1da+zDGJpUwhMcHIyNGzeqlGVmZiI4ONhIEREREdVNSUkJ5HK58leGr127hrlz58LPT/Olwbpc0nJ2doazs7NO8dnY2MDGxgbXr1/H5s2bkZ6eDuDBYyACAgKQlZWlvOGosrISWVlZiI/X/bJ7fTNqwnPr1i3k5eUp3585cwYHDx6Eo6MjWrdujalTp+LChQv47rvvADw4yIsWLcL777+PUaNGYdu2bfjhhx+wYcMGY20CERFRnXTr1g0KhQLp6emIiIjAhAkT4O3tjaNHj+LcuXPw8vJSqe/o6AhHR8d6j2vz5s0QQqB9+/bIy8vD5MmT0aFDB8TGxirrJCYmIiYmBj169EBgYCDmzZuHsrIylTqNhVEvxB04cAD+/v7KCVmJiYnw9/fHtGnTAACXLl1SmWnu4+ODDRs2IDMzE35+fvjss8/w1VdfISwszCjxExER1VXbtm0xY8YMzJ8/H/7+/nB3d8eWLVvg4eGBAQMGGC2ukpISxMXFoUOHDhgxYgR69eqFzZs3qzzFPCoqCnPnzsW0adPQrVs3HDx4EJs2bVKbyNwYNJrf4Wko/B0ekgLepdV48S4t49ylVdPvt1DTwd/hISIiIqoDJjxEREQkeUx4iIiISPKY8BAREZHkMeEhIiIiyWPCQ0RERJLHhIeIiIgkjwkPERERSR4THiIiIpI8JjxERERU78rLyxEfH4+EhASUl5c3eP9MeIiIiKhGs2bNgkwmw8SJE1XKU1JSIJPJVF4dOnTQ2EZGRgZCQ0Px/PPPY/Xq1Q0QtSqjPi2diIio3mVENWx/Q9c2bH/1bP/+/Vi2bBm6du2qcXmnTp2wdetW5XtTU82pRWVlJSorK2FiYgJjPMaTZ3iIiIgagX379qFPnz6wsrJChw4dcODAASxfvhwvvfSS0WK6desWhg0bhhUrVsDBwUFjHVNTU7i6uipfTk5OGusNGzYM27ZtQ2ZmJoYPH16fYWvEhIeIiMjI9uzZg5CQEAwaNAiHDh2Cr68vZsyYgdmzZ2P69OkqdVNTU2Fra1vjKz8/3yBxxcXFYdCgQQgNDa22zqlTp+Du7o42bdpg2LBh1fZtaWmJRYsWYeHChUZ5oj0vaRERERlZYmIiIiIiMHnyZABAdHQ0oqOj8fLLL8Pf31+l7tixYxEZGVlje+7u7nWOac2aNcjNzcX+/furrRMUFIRvvvkG7du3x6VLlzB9+nT07t0bf/31F+zs7OocgyEx4SEiIjKi8+fPIzs7G3PnzlWWmZqaQgihdnYHABwdHeHo6KhXXx988AFmz55dY51jx47BxsYGEyZMQGZmZo1nYwYOHKj8/65duyIoKAheXl744YcfMHr0aL1irC9MeIiIiIzo2LFjAIDu3bsry06cOIHAwEB06dJFrX5qaipSU1NrbPPo0aNo3bq1Wvl7772HkSNH1rhumzZtsHHjRhQXF6vEpFAo8Pvvv2PRokUoLy+HXC5XW7d58+Zo164d8vLyauzDGJjwEBERGVFJSQnkcjlkMhkA4Nq1a5g7dy78/Pw01q/LJS1nZ2c4OzvXGlO/fv1w+PBhlbLY2Fh06NABU6ZM0ZjsAA8mOZ8+fdook5Jrw4SHiIjIiLp16waFQoH09HRERERgwoQJ8Pb2xtGjR3Hu3Dl4eXmp1K/LJS1t2dnZoXPnziplNjY2aNGihUr5pEmTMHjwYHh5eeHixYtITk6GXC5HdHR0vcanD96lRUREZERt27bFjBkzMH/+fPj7+8Pd3R1btmyBh4cHBgwYYOzwanT+/HlER0ejffv2iIyMRIsWLbBnzx6tziI1NJ7hISIiMrKkpCQkJSWplOXk5BgpGs127NihVrZmzZqGD0RPTHiIiEjaJPbLx6QfXtIiIiIiyWPCQ0RERJLHhIeIiIgkjwkPERERSR4THiIiIpI8JjxEREQkeUx4iIiISPKY8BAREZHkMeEhIiIiyWPCQ0RERGrKy8sRHx+PhIQElJeXGzucOuOjJYiISNLis+IbtL9F/RY1aH+6Wrx4MebMmYPCwkL4+flh4cKFCAwMVKuXkZGB0NBQmJiYYPXq1XjzzTeNEK3h8AwPERHRY2Lt2rVITExEcnIycnNz4efnh7CwMBQXF6vVraysRGVlJQBACNHQoRocEx4iIqJGYN++fejTpw+srKzQoUMHHDhwAMuXL8dLL71ksD4+//xzjBkzBrGxsejYsSOWLl0Ka2trrFy5Uq3usGHDsG3bNmRmZmL48OEGi8FYmPAQEREZ2Z49exASEoJBgwbh0KFD8PX1xYwZMzB79mxMnz5dpW5qaipsbW1rfOXn56v1UVFRgZycHISGhirLTExMEBoaiuzsbLX6lpaWWLRoERYuXAhLS0vDb3QD4xweIiIiI0tMTERERAQmT54MAIiOjkZ0dDRefvll+Pv7q9QdO3YsIiMja2zP3d1drezKlStQKBRwcXFRKXdxccHx48fruAWNHxMeIiIiIzp//jyys7Mxd+5cZZmpqSmEEGpndwDA0dERjo6ODRmiJPCSFhERkREdO3YMANC9e3dl2YkTJxAYGIguXbqo1df3kpaTkxPkcjmKiopUyouKiuDq6mrgrWp8eIaHiIjIiEpKSiCXyyGTyQAA165dw9y5c+Hn56exvr6XtMzNzREQEICsrCyEh4cDeHAnVlZWFuLjG/bWfWNgwkNERGRE3bp1g0KhQHp6OiIiIjBhwgR4e3vj6NGjOHfuHLy8vFTq1+WSVmJiImJiYtCjRw8EBgZi3rx5KCsrQ2xsrCE2pVHjJS0iIiIjatu2LWbMmIH58+fD398f7u7u2LJlCzw8PDBgwACD9hUVFYW5c+di2rRp6NatGw4ePIhNmzapTWSWIp7hISIiSWvsv3wMAElJSUhKSlIpy8nJqZe+4uPjH4tLWI/iGR4iIiKSPCY8REREJHlMeIiIiEjymPAQERGR5DHhISIiIsljwkNERESSx4SHiIiIJI8JDxEREUkeEx4iIiKSPCY8REREpLPy8nLEx8cjISEB5eXlxg6nVkx4iIiIHkOzZs2CTCbDxIkTVcpTUlIgk8lUXh06dFBbPyMjA6GhoXj++eexevXqBopaf3yWFhERSVrB2HcatD/PpUsatD997N+/H8uWLUPXrl01Lu/UqRO2bt2qfG9qqp4uVFZWorKyEiYmJhBC1FushsIzPERERI3Avn370KdPH1hZWaFDhw44cOAAli9fjpdeesmg/dy6dQvDhg3DihUr4ODgoLGOqakpXF1dlS8nJye1OsOGDcO2bduQmZmJ4cOHGzTG+sCEh4iIyMj27NmDkJAQDBo0CIcOHYKvry9mzJiB2bNnY/r06Sp1U1NTYWtrW+MrPz+/2r7i4uIwaNAghIaGVlvn1KlTcHd3R5s2bTBs2DCN7VlaWmLRokVYuHAhLC0t9d/4BsJLWkREREaWmJiIiIgITJ48GQAQHR2N6OhovPzyy/D391epO3bsWERGRtbYnru7u8byNWvWIDc3F/v376923aCgIHzzzTdo3749Ll26hOnTp6N3797466+/YGdnp+OWNR5MeIiIiIzo/PnzyM7Oxty5c5VlpqamEEKond0BAEdHRzg6OurcT0FBASZMmIDMzMwaz8gMHDhQ+f9du3ZFUFAQvLy88MMPP2D06NE699tYGP2S1uLFi+Ht7Q1LS0sEBQVh3759NdafN28e2rdvDysrK3h6euLdd9/F3bt3GyhaIiIiwzp27BgAoHv37sqyEydOIDAwEF26dFGrr+8lrZycHBQXF6N79+4wNTWFqakpdu7ciQULFsDU1BQKhUJjfM2bN0e7du2Ql5dnoC02DqOe4Vm7di0SExOxdOlSBAUFYd68eQgLC8OJEyfQsmVLtfoZGRn44IMPsHLlSjz99NM4efIkRo4cCZlMhs8//9wIW0BERFQ3JSUlkMvlkMlkAIBr165h7ty58PPz01hf30ta/fr1w+HDh1XKYmNj0aFDB0yZMgVyuVxjW7du3cLp06ebxMTkmhg14fn8888xZswYxMbGAgCWLl2KDRs2YOXKlfjggw/U6u/evRvPPPMMhg4dCgDw9vZGdHQ09u7d26BxExERGUq3bt2gUCiQnp6OiIgITJgwAd7e3jh69CjOnTsHLy8vlfr6XtKys7ND586dVcpsbGzQokULlfJJkyZh8ODB8PLywsWLF5GcnAy5XI7o6Gj9NrCRMNolrYqKCuTk5KjMEjcxMUFoaCiys7M1rvP0008jJydHednr77//xsaNG/HCCy9U2095eTlKS0tVXkRERI1F27ZtMWPGDMyfPx/+/v5wd3fHli1b4OHhgQEDBjR4POfPn0d0dDTat2+PyMhItGjRAnv27IGzs3ODx2JIRjvDc+XKFSgUCri4uKiUu7i44Pjx4xrXGTp0KK5cuYJevXpBCIH79+9j7Nix+PDDD6vtJy0tTeOkLyIiejw0hR8CTEpKQlJSkkpZTk5Ovfe7Y8cOtbI1a9bUe7/GYPRJy7rYsWMHUlNT8eWXXyI3Nxc//fQTNmzYgJkzZ1a7ztSpU1FSUqJ8FRQUNGDERERE1BgY7QyPk5MT5HI5ioqKVMqLiorg6uqqcZ2kpCQMHz4cb775JgCgS5cuKCsrw1tvvYWPPvoIJibq+ZuFhQUsLCwMvwFERETUZBjtDI+5uTkCAgKQlZWlLKusrERWVhaCg4M1rnP79m21pKZqVnlTeI4HERERGYdR79JKTExETEwMevTogcDAQMybNw9lZWXKu7ZGjBgBDw8PpKWlAQAGDx6Mzz//HP7+/ggKCkJeXh6SkpIwePDgam+nIyIiIjJqwhMVFYXLly9j2rRpKCwsRLdu3bBp0yblROb8/HyVMzoff/wxZDIZPv74Y1y4cAHOzs4YPHgwPv30U2NtAhERETUBRn+0RHx8POLj4zUue3T2uKmpKZKTk5GcnNwAkREREZFUNKm7tIiIiIj0wYSHiIiIJI8JDxEREUkeEx4iIiKSPCY8REREpLPy8nLEx8cjISEB5eXlxg6nVka/S4uIiKg+bVj8Z4P2NyjOr0H708XNmzeRlJSEn3/+GcXFxfD398f8+fPRs2dPlXqLFy/GnDlzUFhYCD8/PyxcuBCBgYEqdTIyMhAaGgoTExOsXr1a+RSExopneIiIiB4Tb775JjIzM7F69WocPnwY/fv3R2hoKC5cuKCss3btWiQmJiI5ORm5ubnw8/NDWFgYiouLVdqqrKxEZWUlgKbxtAMmPERERI3Avn370KdPH1hZWaFDhw44cOAAli9fjpdeeskg7d+5cwc//vgj0tPT8eyzz6Jt27ZISUlB27ZtsWTJ/z9R/vPPP8eYMWMQGxuLjh07YunSpbC2tsbKlStV2hs2bBi2bduGzMxMDB8+3CAx1icmPEREREa2Z88ehISEYNCgQTh06BB8fX0xY8YMzJ49G9OnT1epm5qaCltb2xpf+fn5an3cv38fCoUClpaWKuVWVlbYtWsXAKCiogI5OTkIDQ1VLjcxMUFoaCiys7NV1rO0tMSiRYuwcOFCtTYbI87hISIiMrLExERERERg8uTJAIDo6GhER0fj5Zdfhr+/v0rdsWPHIjIyssb23N3d1crs7OwQHByMmTNnwtfXFy4uLvj++++RnZ2Ntm3bAgCuXLkChUKhfMRTFRcXFxw/frwum2h0THiIiIiM6Pz588jOzsbcuXOVZaamphBCqJ3dAQBHR0c4Ojrq1dfq1asxatQoeHh4QC6Xo3v37oiOjkZOTo7e8TcVvKRFRERkRMeOHQMAdO/eXVl24sQJBAYGokuXLmr19b2kBQBPPPEEdu7ciVu3bqGgoAD79u3DvXv30KZNGwCAk5MT5HI5ioqKVNYrKiqCq6uroTbZKHiGh4iIyIhKSkogl8shk8kAANeuXcPcuXPh56f59nZ9L2k9zMbGBjY2Nrh+/To2b96M9PR0AIC5uTkCAgKQlZWF8PBwAA/uxsrKyqr2Qd9NBRMeIiIiI+rWrRsUCgXS09MRERGBCRMmwNvbG0ePHsW5c+fg5eWlUr8ul7Q2b94MIQTat2+PvLw8TJ48GR06dEBsbKyyTmJiImJiYtCjRw8EBgZi3rx5KCsrU6nTFPGSFhERkRG1bdsWM2bMwPz58+Hv7w93d3ds2bIFHh4eGDBggEH7KikpQVxcHDp06IARI0agV69e2Lx5M8zMzJR1oqKiMHfuXEybNg3dunXDwYMHsWnTJrWJzE0Nz/AQEZGkNeZfPq6SlJSEpKQklbL6mEgcGRlZ6+UwAIiPj2/yl7AexTM8REREJHlMeIiIiEjymPAQERGR5DHhISIiIsljwkNERESSx4SHiIgko7Ky0tghUB0IIeqtbd6WTkRETZ65uTlMTExw8eJFODs7w9zcXPnLxdQ0CCFw+fJlyGQyld8FMhQmPERE1OSZmJjAx8cHly5dwsWLF40dDulJJpOhVatWkMvlBm+bCQ8REUmCubk5Wrdujfv370OhUBg7HNKDmZlZvSQ7ABMeIiKSkKrLIfVxSYSaNk5aJiIiIsljwkNERESSx4SHiIiIJI8JDxEREUkeEx4iIiKSPCY8REREJHlMeIiIiEjymPAQERGR5DHhISIiIsljwkNERESSx4SHiIiIJI8JDxEREUkeEx4iIiKSPD4tnagxyIjSrb6Ls85dFIx9R+d1PJcu0XkdIqLGiGd4iIiISPKY8BAREZHkMeEhIiIiyWPCQ0RERJLHhIeIiIgkjwkPERERSR4THiIiIpI8JjxEREQkeUx4iIiISPKY8BAREZHkMeEhIiIiyWPCQ0RERJLHhIeIiIgkjwkPERERSR4THiIiIpI8JjxEREQkeUx4iIiISPL0Sni2b99u6DiIiIiI6o1eCc+AAQPwxBNP4JNPPkFBQYGhYyIiIiIyKL0SngsXLiA+Ph7r169HmzZtEBYWhh9++AEVFRWGjo+IiIiozvRKeJycnPDuu+/i4MGD2Lt3L9q1a4dx48bB3d0d48ePx59//ql1W4sXL4a3tzcsLS0RFBSEffv21Vj/xo0biIuLg5ubGywsLNCuXTts3LhRn80gIiKix0SdJy13794dU6dORXx8PG7duoWVK1ciICAAvXv3xpEjR2pcd+3atUhMTERycjJyc3Ph5+eHsLAwFBcXa6xfUVGB559/HmfPnsX69etx4sQJrFixAh4eHnXdDCIiIpIwvROee/fuYf369XjhhRfg5eWFzZs3Y9GiRSgqKkJeXh68vLwQERFRYxuff/45xowZg9jYWHTs2BFLly6FtbU1Vq5cqbH+ypUrce3aNfzyyy945pln4O3tjZCQEPj5+em7GURERPQY0CvhSUhIgJubG95++220a9cOf/zxB7Kzs/Hmm2/CxsYG3t7emDt3Lo4fP15tGxUVFcjJyUFoaOj/B2NigtDQUGRnZ2tc59///jeCg4MRFxcHFxcXdO7cGampqVAoFNX2U15ejtLSUpUXERERPV5M9Vnp6NGjWLhwIV599VVYWFhorOPk5FTj7etXrlyBQqGAi4uLSrmLi0u1idLff/+Nbdu2YdiwYdi4cSPy8vIwbtw43Lt3D8nJyRrXSUtLw/Tp07XcMiIiIpIivc7wJCcnIyIiQi3ZuX//Pn7//XcAgKmpKUJCQuoe4UMqKyvRsmVLLF++HAEBAYiKisJHH32EpUuXVrvO1KlTUVJSonzxNnoiIqLHj15nePr27YtLly6hZcuWKuUlJSXo27dvjZeYqjg5OUEul6OoqEilvKioCK6urhrXcXNzg5mZGeRyubLM19cXhYWFqKiogLm5udo6FhYW1Z6FIiIioseDXmd4hBCQyWRq5VevXoWNjY1WbZibmyMgIABZWVnKssrKSmRlZSE4OFjjOs888wzy8vJQWVmpLDt58iTc3Nw0JjtEREREgI5neF599VUAgEwmw8iRI1XOnCgUChw6dAhPP/201u0lJiYiJiYGPXr0QGBgIObNm4eysjLExsYCAEaMGAEPDw+kpaUBAN555x0sWrQIEyZMQEJCAk6dOoXU1FSMHz9el80gIiKix4xOCY+9vT2AB2d47OzsYGVlpVxmbm6Op556CmPGjNG6vaioKFy+fBnTpk1DYWEhunXrhk2bNiknMufn58PE5P9PQnl6emLz5s1499130bVrV3h4eGDChAmYMmWKLptBREREjxmdEp5Vq1YBALy9vTFp0iStL1/VJD4+HvHx8RqX7dixQ60sODgYe/bsqXO/RERE9PjQa9JydbeAExERETVGWic83bt3R1ZWFhwcHODv769x0nKV3NxcgwRHREREZAhaJzwvv/yycpJyeHh4fcVDREREZHBaJzwPX8biJS0iIiJqSur8tHQiIiKixk7rMzwODg41ztt52LVr1/QOiIiIiMjQtE545s2bV49hEBEREdUfrROemJiY+oyDiIiIqN5onfCUlpaiWbNmyv+vSVU9IiIiosZApzk8VU9Ib968ucb5PFUPFdXmaelEREREDUXrhGfbtm1wdHQEAGzfvr3eAiIiIiIyNK0TnpCQEI3/T0RERNTY6fUsLQC4fv06vv76axw7dgwA0LFjR8TGxirPAhERERE1Fnr98ODvv/8Ob29vLFiwANevX8f169exYMEC+Pj44Pfffzd0jERERER1otcZnri4OERFRWHJkiWQy+UAAIVCgXHjxiEuLg6HDx82aJBEREREdaHXGZ68vDy89957ymQHAORyORITE5GXl2ew4IiIiIgMQa+Ep3v37sq5Ow87duwY/Pz86hwUERERkSFpfUnr0KFDyv8fP348JkyYgLy8PDz11FMAgD179mDx4sWYNWuW4aMkIiIiqgOtE55u3bpBJpNBCKEse//999XqDR06FFFRUYaJjoiIiMgAtE54zpw5U59xEBEREdUbrRMeLy+v+oyDiIiIqN7o/cODAHD06FHk5+ejoqJCpfyll16qU1BEREREhqRXwvP333/jlVdeweHDh1Xm9VQ9UJQPDyUiIqLGRK/b0idMmAAfHx8UFxfD2toaR44cwe+//44ePXpgx44dBg6RiIiIqG70OsOTnZ2Nbdu2wcnJCSYmJjAxMUGvXr2QlpaG8ePH448//jB0nERERER60+sMj0KhgJ2dHQDAyckJFy9eBPBgYvOJEycMFx0RERGRAeh1hqdz5874888/4ePjg6CgIKSnp8Pc3BzLly9HmzZtDB0jERERUZ3olfB8/PHHKCsrAwDMmDEDL774Inr37o0WLVpg7dq1Bg2QiIiIqK70SnjCwsKU/9+2bVscP34c165dg4ODg/JOLSIiIqLGok6/wwMABQUFAABPT886B0NERERUH/SatHz//n0kJSXB3t4e3t7e8Pb2hr29PT7++GPcu3fP0DESERER1YleZ3gSEhLw008/IT09HcHBwQAe3KqekpKCq1evYsmSJQYNkoiIiKgu9Ep4MjIysGbNGgwcOFBZ1rVrV3h6eiI6OpoJDxERETUqel3SsrCwgLe3t1q5j48PzM3N6xoTERERkUHplfDEx8dj5syZKC8vV5aVl5fj008/RXx8vMGCIyIiIjIErS9pvfrqqyrvt27dilatWsHPzw8A8Oeff6KiogL9+vUzbIREREREdaR1wmNvb6/y/rXXXlN5z9vSiYiIqLHSOuFZtWpVfcZBREREVG/q9MODly9fVj4stH379nB2djZIUERERESGpNek5bKyMowaNQpubm549tln8eyzz8Ld3R2jR4/G7du3DR0jERERUZ3olfAkJiZi586d+PXXX3Hjxg3cuHED//rXv7Bz50689957ho6RiIiIqE70uqT1448/Yv369ejTp4+y7IUXXoCVlRUiIyP5w4NERETUqOh1huf27dtwcXFRK2/ZsiUvaREREVGjo1fCExwcjOTkZNy9e1dZdufOHUyfPl35bC0iIiKixkKvS1rz5s3DgAED1H540NLSEps3bzZogERERER1pVfC06VLF5w6dQr//Oc/cfz4cQBAdHQ0hg0bBisrK4MGSERERFRXOic89+7dQ4cOHfCf//wHY8aMqY+YiIiIiAxK5zk8ZmZmKnN3iIiIiBo7vSYtx8XFYfbs2bh//76h4yEiIiIyOL3m8Ozfvx9ZWVnYsmULunTpAhsbG5XlP/30k0GCIyIiIjIEvRKe5s2bqz0tnYiIiKix0inhqaysxJw5c3Dy5ElUVFTgueeeQ0pKCu/MIiIiokZNpzk8n376KT788EPY2trCw8MDCxYsQFxcXH3FRkRERGQQOiU83333Hb788kts3rwZv/zyC3799Vf885//RGVlZX3FR0RERFRnOiU8+fn5eOGFF5TvQ0NDIZPJcPHiRYMHRkRERGQoOiU89+/fh6WlpUqZmZkZ7t27Z9CgiIiIiAxJp0nLQgiMHDkSFhYWyrK7d+9i7NixKrem87Z0IiIiakx0SnhiYmLUyt544w2DBUNERERUH3RKeFatWlVfcRARERHVG70eLWFoixcvhre3NywtLREUFIR9+/Zptd6aNWsgk8kQHh5evwESERFRk2b0hGft2rVITExEcnIycnNz4efnh7CwMBQXF9e43tmzZzFp0iT07t27gSIlIiKipsroCc/nn3+OMWPGIDY2Fh07dsTSpUthbW2NlStXVruOQqHAsGHDMH36dLRp06YBoyUiIqKmyKgJT0VFBXJychAaGqosMzExQWhoKLKzs6tdb8aMGWjZsiVGjx5dax/l5eUoLS1VeREREdHjxagJz5UrV6BQKODi4qJS7uLigsLCQo3r7Nq1C19//TVWrFihVR9paWmwt7dXvjw9PescNxERETUtRr+kpYubN29i+PDhWLFiBZycnLRaZ+rUqSgpKVG+CgoK6jlKIiIiamx0ui3d0JycnCCXy1FUVKRSXlRUBFdXV7X6p0+fxtmzZzF48GBlWdVzvExNTXHixAk88cQTKutYWFio/FAiERERPX6MeobH3NwcAQEByMrKUpZVVlYiKysLwcHBavU7dOiAw4cP4+DBg8rXSy+9hL59++LgwYO8XEVEREQaGfUMDwAkJiYiJiYGPXr0QGBgIObNm4eysjLExsYCAEaMGAEPDw+kpaXB0tISnTt3Vlm/efPmAKBWTkRERFTF6AlPVFQULl++jGnTpqGwsBDdunXDpk2blBOZ8/PzYWLSpKYaERERUSNj9IQHAOLj4xEfH69x2Y4dO2pc95tvvjF8QERERCQpPHVCREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikrxGkfAsXrwY3t7esLS0RFBQEPbt21dt3RUrVqB3795wcHCAg4MDQkNDa6xPREREZPSEZ+3atUhMTERycjJyc3Ph5+eHsLAwFBcXa6y/Y8cOREdHY/v27cjOzoanpyf69++PCxcuNHDkRERE1FQYPeH5/PPPMWbMGMTGxqJjx45YunQprK2tsXLlSo31//nPf2LcuHHo1q0bOnTogK+++gqVlZXIysrSWL+8vBylpaUqLyIiInq8GDXhqaioQE5ODkJDQ5VlJiYmCA0NRXZ2tlZt3L59G/fu3YOjo6PG5WlpabC3t1e+PD09DRI7ERERNR1GTXiuXLkChUIBFxcXlXIXFxcUFhZq1caUKVPg7u6ukjQ9bOrUqSgpKVG+CgoK6hw3ERERNS2mxg6gLmbNmoU1a9Zgx44dsLS01FjHwsICFhYWDRwZERERNSZGTXicnJwgl8tRVFSkUl5UVARXV9ca1507dy5mzZqFrVu3omvXrvUZJhERETVxRr2kZW5ujoCAAJUJx1UTkIODg6tdLz09HTNnzsSmTZvQo0ePhgiViIiImjCjX9JKTExETEwMevTogcDAQMybNw9lZWWIjY0FAIwYMQIeHh5IS0sDAMyePRvTpk1DRkYGvL29lXN9bG1tYWtra7TtICIiosbL6AlPVFQULl++jGnTpqGwsBDdunXDpk2blBOZ8/PzYWLy/yeilixZgoqKCrz++usq7SQnJyMlJaUhQyciIqImwugJDwDEx8cjPj5e47IdO3aovD979mz9B0RERESSYvQfHiQiIiKqb0x4iIiISPKY8BAREZHkMeEhIiIiyWPCQ0RERJLHhIeIiIgkjwkPERERSR4THiIiIpI8JjxEREQkeUx4iIiISPKY8BAREZHkNYpnaT32MqJ0X2foWp2qF4x9R+cuPJcu0XkdIiLSQwP8HQAe778FPMNDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeSZGjsAIqLHXkaU7usMXavzKgVj39G9ny5jdV+HqBHiGR4iIiKSPCY8REREJHlMeIiIiEjymPAQERGR5DHhISIiIsljwkNERESSx4SHiIiIJK9RJDyLFy+Gt7c3LC0tERQUhH379tVYf926dejQoQMsLS3RpUsXbNy4sYEiJSIioqbI6AnP2rVrkZiYiOTkZOTm5sLPzw9hYWEoLi7WWH/37t2Ijo7G6NGj8ccffyA8PBzh4eH466+/GjhyIiIiaiqMnvB8/vnnGDNmDGJjY9GxY0csXboU1tbWWLlypcb68+fPx4ABAzB58mT4+vpi5syZ6N69OxYtWtTAkRMREVFTYdRHS1RUVCAnJwdTp05VlpmYmCA0NBTZ2dka18nOzkZiYqJKWVhYGH755ReN9cvLy1FeXq58X1JSAgAoLS2tY/QGdPuezqtM+vdbOtWfWKHQuY9GtY+kTscxUFFWoXMXNzkGGkTFnVs6r1N6X/fvAOhxbG5W6D5ubuuzPRw3umuAvwNA0/5bUBWHEEK/BoQRXbhwQQAQu3fvVimfPHmyCAwM1LiOmZmZyMjIUClbvHixaNmypcb6ycnJAgBffPHFF1988SWBV0FBgV45h+QfHjp16lSVM0KVlZW4du0aWrRoAZlMZsTIDK+0tBSenp4oKChAs2bNjB0OGQHHAHEMkFTHgBACN2/ehLu7u17rGzXhcXJyglwuR1FRkUp5UVERXF1dNa7j6uqqU30LCwtYWFiolDVv3lz/oJuAZs2aSWqQk+44BohjgKQ4Buzt7fVe16iTls3NzREQEICsrCxlWWVlJbKyshAcHKxxneDgYJX6AJCZmVltfSIiIiKjX9JKTExETEwMevTogcDAQMybNw9lZWWIjY0FAIwYMQIeHh5IS0sDAEyYMAEhISH47LPPMGjQIKxZswYHDhzA8uXLjbkZRERE1IgZPeGJiorC5cuXMW3aNBQWFqJbt27YtGkTXFxcAAD5+fkwMfn/E1FPP/00MjIy8PHHH+PDDz/Ek08+iV9++QWdO3c21iY0GhYWFkhOTla7hEePD44B4hggjgHNZELoe38XERERUdNg9B8eJCIiIqpvTHiIiIhI8pjwEBERkeQx4SEiIiLJY8LTRC1evBje3t6wtLREUFAQ9u3bp1x29+5dxMXFoUWLFrC1tcVrr72m9mON1PTVNAaWL1+OPn36oFmzZpDJZLhx44bxAiWD+/333zF48GC4u7tDJpOpPUtQCIFp06bBzc0NVlZWCA0NxalTp4wTLNWL2sbATz/9hP79+yufKnDw4EGjxNmYMOFpgtauXYvExEQkJycjNzcXfn5+CAsLQ3FxMQDg3Xffxa+//op169Zh586duHjxIl599VUjR02GVNsYuH37NgYMGIAPP/zQyJFSfSgrK4Ofnx8WL16scXl6ejoWLFiApUuXYu/evbCxsUFYWBju3r3bwJFSfaltDJSVlaFXr16YPXt2A0fWiOn1BC4yqsDAQBEXF6d8r1AohLu7u0hLSxM3btwQZmZmYt26dcrlx44dEwBEdna2McKlelDTGHjY9u3bBQBx/fr1Bo6QGgoA8fPPPyvfV1ZWCldXVzFnzhxl2Y0bN4SFhYX4/vvvjRAh1bdHx8DDzpw5IwCIP/74o0Fjaox4hqeJqaioQE5ODkJDQ5VlJiYmCA0NRXZ2NnJycnDv3j2V5R06dEDr1q2RnZ1tjJDJwGobA/R4O3PmDAoLC1XGh729PYKCgjg+6LHGhKeJuXLlChQKhfKXqKu4uLigsLAQhYWFMDc3V3tAatVyavpqGwP0eKsaAxwfRKqY8BAREZHkMeFpYpycnCCXy9XuuioqKoKrqytcXV1RUVGhdldO1XJq+mobA/R4qxoDHB9EqpjwNDHm5uYICAhAVlaWsqyyshJZWVkIDg5GQEAAzMzMVJafOHEC+fn5CA4ONkbIZGC1jQF6vPn4+MDV1VVlfJSWlmLv3r0cH/RYM/rT0kl3iYmJiImJQY8ePRAYGIh58+ahrKwMsbGxsLe3x+jRo5GYmAhHR0c0a9YMCQkJCA4OxlNPPWXs0MlAahoDAJTzufLy8gAAhw8fhp2dHVq3bg1HR0djhk4GcOvWLeWxBR5MVD548CAcHR3RunVrTJw4EZ988gmefPJJ+Pj4ICkpCe7u7ggPDzde0GRQtY2Ba9euIT8/HxcvXgTw4B++AJRXAh5Lxr5NjPSzcOFC0bp1a2Fubi4CAwPFnj17lMvu3Lkjxo0bJxwcHIS1tbV45ZVXxKVLl4wYLdWHmsZAcnKyAKD2WrVqlfECJoOp+rmBR18xMTFCiAe3piclJQkXFxdhYWEh+vXrJ06cOGHcoMmgahsDq1at0rg8OTnZqHEbk0wIIRo2xSIiIiJqWJzDQ0RERJLHhIeIiIgkjwkPERERSR4THiIiIpI8JjxEREQkeUx4iIiISPKY8BAREZHkMeEhIiIiyWPCQ0RERJLHhIeINBo5ciRkMhnGjh2rtiwuLg4ymQwjR45s+MAkRCaT4ZdffjF2GESPBSY8RFQtT09PrFmzBnfu3FGW3b17FxkZGWjdurURI6tdRUWFsUMgokaECQ8RVat79+7w9PTETz/9pCz76aef0Lp1a/j7+yvLKisrkZaWBh8fH1hZWcHPzw/r169XLlcoFBg9erRyefv27TF//nyVvnbs2IHAwEDY2NigefPmeOaZZ3Du3DkAD842Pfqk74kTJ6JPnz7K93369EF8fDwmTpwIJycnhIWFAQD++usvDBw4ELa2tnBxccHw4cNx5coVlfUSEhIwceJEODg4wMXFBStWrFA+fd7Ozg5t27bFb7/9ptK/Nu2OHz8e77//PhwdHeHq6oqUlBTlcm9vbwDAK6+8AplMpnxPRPWDCQ8R1WjUqFFYtWqV8v3KlSsRGxurUictLQ3fffcdli5diiNHjuDdd9/FG2+8gZ07dwJ4kBC1atUK69atw9GjRzFt2jR8+OGH+OGHHwAA9+/fR3h4OEJCQnDo0CFkZ2fjrbfegkwm0ynWb7/9Fubm5vjf//6HpUuX4saNG3juuefg7++PAwcOYNOmTSgqKkJkZKTaek5OTti3bx8SEhLwzjvvICIiAk8//TRyc3PRv39/DB8+HLdv3wYAndq1sbHB3r17kZ6ejhkzZiAzMxMAsH//fgDAqlWrcOnSJeV7Iqonxn5cOxE1TjExMeLll18WxcXFwsLCQpw9e1acPXtWWFpaisuXL4uXX35ZxMTEiLt37wpra2uxe/dulfVHjx4toqOjq20/Li5OvPbaa0IIIa5evSoAiB07dtQYy8MmTJggQkJClO9DQkKEv7+/Sp2ZM2eK/v37q5QVFBQIAOLEiRPK9Xr16qVcfv/+fWFjYyOGDx+uLLt06ZIAILKzs/VuVwghevbsKaZMmaJ8D0D8/PPPGreZiAzL1KjZFhE1es7Ozhg0aBC++eYbCCEwaNAgODk5KZfn5eXh9u3beP7551XWq6ioULnstXjxYqxcuRL5+fm4c+cOKioq0K1bNwCAo6MjRo4cibCwMDz//PMIDQ1FZGQk3NzcdIo1ICBA5f2ff/6J7du3w9bWVq3u6dOn0a5dOwBA165dleVyuRwtWrRAly5dlGUuLi4AgOLiYr3bBQA3NzdlG0TUsJjwEFGtRo0ahfj4eAAPEpeH3bp1CwCwYcMGeHh4qCyzsLAAAKxZswaTJk3CZ599huDgYNjZ2WHOnDnYu3evsu6qVaswfvx4bNq0CWvXrsXHH3+MzMxMPPXUUzAxMYEQQqXte/fuqcVpY2OjFtvgwYMxe/ZstboPJ1NmZmYqy2QymUpZ1aW1ysrKOrdb1QYRNSwmPERUqwEDBqCiogIymUw5GbhKx44dYWFhgfz8fISEhGhc/3//+x+efvppjBs3Tll2+vRptXr+/v7w9/fH1KlTERwcjIyMDDz11FNwdnbGX3/9pVL34MGDagnFo7p3744ff/wR3t7eMDU13Nedodo1MzODQqEwWFxEVD1OWiaiWsnlchw7dgxHjx6FXC5XWWZnZ4dJkybh3XffxbfffovTp08jNzcXCxcuxLfffgsAePLJJ3HgwAFs3rwZJ0+eRFJSksok3TNnzmDq1KnIzs7GuXPnsGXLFpw6dQq+vr4AgOeeew4HDhzAd999h1OnTiE5OVktAdIkLi4O165dQ3R0NPbv34/Tp09j8+bNiI2NrVOiYah2vb29kZWVhcLCQly/fl3veIiodkx4iEgrzZo1Q7NmzTQumzlzJpKSkpCWlgZfX18MGDAAGzZsgI+PDwDg7bffxquvvoqoqCgEBQXh6tWrKmd7rK2tcfz4cbz22mto164d3nrrLcTFxeHtt98GAISFhSEpKQnvv/8+evbsiZs3b2LEiBG1xuzu7o7//e9/UCgU6N+/P7p06YKJEyeiefPmMDHR/+vPUO1+9tlnyMzMhKenp8p8JyIyPJl49MI4ERERkcTwDA8RERFJHhMeIiIikjwmPERERCR5THiIiIhI8pjwEBERkeQx4SEiIiLJY8JDREREkseEh4iIiCSPCQ8RERFJHhMeIiIikjwmPERERCR5/wes2iZleIL2pQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -325,7 +315,8 @@
     "    f\"Measurement probabilities at $t={final_time}$, for various field angles $\\\\alpha$\\n\"\n",
     "    f\"Initial state: 10, Linear lattice of size $L=2$\"\n",
     ")\n",
-    "plt.legend()"
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -381,10 +372,10 @@
    "outputs": [],
    "source": [
     "from qiskit_algorithms import TrotterQRTE\n",
-    "from qiskit.primitives import Estimator\n",
+    "from qiskit.primitives import StatevectorEstimator\n",
     "\n",
     "num_timesteps = 60\n",
-    "trotter = TrotterQRTE(num_timesteps=num_timesteps, estimator=Estimator())"
+    "trotter = TrotterQRTE(num_timesteps=num_timesteps, estimator=StatevectorEstimator())"
    ]
   },
   {
@@ -506,7 +497,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -606,7 +597,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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lJTUav0xOTg5bvHgx4/P58vdi+PDhbPv27fX2FQqFzNjYmAFgu3fvbvB49+/fZ5MnT2ZWVlbMyMiI9evXjx0+fFhhn4amHWWMsd27dzMvLy9mYGDAevXqxU6cOFHvfDHGWGRkJOvduzczMDBQmIK0ofNTU1PDVq9ezTw9PZm+vj7j8/lsxYoVrLKyUmE/d3d3Nnbs2Hqvp7HpUAkhhDxdOIxRRRghhBBCCCHtFdUQEEIIIYQQ0o5RQkAIIYQQQkg7RgkBIYQQQggh7RglBIQQQgghhLRjlBAQQgghhBDSjlFCQAghhBBCSDtGCQEhhBBCCCHtGCUEhBBCCCGEtGOUEBBCCCGEENKOUUJACCGEEEJIO0YJASGEEEIIIe0YJQSEEEIIIYS0Y5QQEEIIIYQQ0o5RQkAIIYQQQkg7RgkBIYQQQggh7RglBIQQQgghhLRjlBAQQgghhBDSjlFCQAghhBBCSDtGCQEhhBBCCCHtGCUEhBBCCCGEtGOUEBBCCCGEENKOUUJACCGEEEJIO6an7QB0nUQiQVZWFszNzcHhcLQdDiGEEELaGcYYSkpK4OLiAi6XruUS1aOEoBlZWVng8/naDoMQQggh7Vx6ejpcXV21HQZ5BlFC0Axzc3MA0j9CCwsLjT1v1PoocLlc9PuoX737Yj6PgUQiwYAVAzQWDyGEEEK0QygUgs/ny9skhKgaJQTNkA0TsrCw0GhCYG5ijojQCJgamSIwJFC+XRAuQPyn8QheE6zReAghhBCiXTR0magLJQQ6SpYERIRGyG8LwgWICI1A8JpghSSBEEIIIYSQ1qKEQIfVTgoE4QJIaiSUDBBCCCGEEJWiUnUdFxgSCA6PA0mNBDwDHiUDhBBCCCFEpZ6ahKCgoAAzZsyAhYUFrKysMG/ePJSWljb5mCFDhoDD4Sj8e/PNNzUUsWoIwgVgYgYAEFeLIQgXaDkiQgghhBDyLHlqhgzNmDEDjx49wqlTp1BTU4O5c+di4cKF2LNnT5OPW7BgAdasWSO/bWJiou5QVUZWM2DWwQySGgksPCwUagoIIYQQQghpq6eih+D27ds4fvw4fvzxR/Tv3x8DBw7E119/jT/++ANZWVlNPtbExAROTk7yf0/LzDy1C4iZhKHvB31RdLcIgaGB8poCQgghhBBC2uqpSAgEAgGsrKzQp08f+bYRI0aAy+UiOjq6ycf+9ttvsLOzQ7du3bBixQqUl5erO1yVkIilBcT9PuqHsuwydJnaBcZ2xrDrZofgNcGQiCXaDpEQQgghhDwDnoohQ9nZ2XBwcFDYpqenBxsbG2RnZzf6uOnTp8Pd3R0uLi5ISEjARx99hKSkJOzfv7/Rx1RVVaGqqkp+WygUtv0FtEJwWDAAoCilCBwuB2YuZvCb44fEnYl4+cjLWomJEEIIIYQ8e7TaQ/C///2vXtFv3X937txp9fEXLlyIUaNGoXv37pgxYwZ++eUXHDhwAPfv32/0MevXr4elpaX8H5/Pb/Xzq0JJegnMOpiBy+PCd5YvHp56iNKspoupdUlEWOPDmwThAkSERWg4IkIIIYQQUptWE4Jly5bh9u3bTf7z8vKCk5MTcnNzFR4rEolQUFAAJycnpZ+vf//+AIB79+41us+KFStQXFws/5eent66F6ciJWklsHCT1j1Y8C3AH8LHrd23tBpTS3B53AZrHmQ1ElzeUzFqjRBCCCHkmaXVIUP29vawt7dvdr/AwEAUFRUhNjYWvXv3BgCcPXsWEolE3shXRnx8PADA2dm50X0MDQ1haGio9DHVrSS9BOZ8c/ntbnO7QbBGgL4f9H0qljCnFZcJIYQQQnTbU3F51sfHBy+88AIWLFiAmJgYREREYMmSJZg2bRpcXFwAAJmZmejatStiYmIAAPfv30d4eDhiY2ORmpqKf//9F6+99hoGDRqEHj16aPPltIgwTaiQEHSc2BFlj8qQHdN47YSuCQwJRPCaYESERmCz4WZKBgghhBBCdMhTkRAA0tmCunbtiuHDh2PMmDEYOHAgtm/fLr+/pqYGSUlJ8lmEDAwMcPr0aYwcORJdu3bFsmXL8PLLL+PQoUPaegmtUpL+ZMgQAOgb66PrtK5I/DlRi1G1XMC7AQCki6vRisuEEEIIIbrjqZhlCABsbGyaXITMw8MDjDH5bT6fjwsXLmgiNLWqO2QIAPzm+GHfC/swZPMQ6Bvraymyljk48aD0P9wnKy5TUkAIIYQQon1PTQ9BeyVMEyr0EACAc39nmDqb4t7BxoujdcnFFReRfi4dnqM9YWhuiKCwIFpcjRBCCCFER1BCoMOqS6pRVVRVr4eAw+HAb44fbu68qaXIlCcIFyDmsxhYdbLCpEOTAA7gNdZLXlNASQEhhBBCiHY9NUOG2iNhuhB6xnowsjGqd5/fLD9EhERAmC6EBd+igUfrhsrCSnD1uBj35zhweVzwB/ORdjZNPlyIVlwmhBBCCNEu6iHQYbKC4oamFzVzMYP7CHfc+lW31yQQlYvQ6aVOcPR3BADwh/KRdi4NwH+zD/23IjMhhBBCCNEOSgh0WEMFxbV1m9sNiT8nKhRT65LCe4VI3JmI4DVPGv1uQ92QeSkT4hqxFiNTRKspE0IIIaQ9o4RAhwnThDB3azwh8B7njcrHlciMyNRgVMqLDIuEzwwf2HSxkW+z62YHPSM9ZF/RnXUUaDVlQgghhLRnVEOgw0rSS2Dh3nB9QESYtKHa9dWuuLnzJlwHusrvE4QLIBFLtDocJ+9GHu7uu4u5d+YqbOdwOeAP5SP9XDo6BHXQUnSKaDVlQgghhLRndOlTh5WkldSbclRGdlVbIpbgzp93UF1WDUB3rmpf/uQyeizsAUt3y3r31a4j0BW1V1PeZLCJkgFCCCGEtBuUEOiwpmoIZA3YhO8ToGesh7v77+rMVe2sqCyknUlD/5X9G7zfbagbsiKyIKoSaTiypgWGBILD40BSIwFXn0vJACGEEELaBUoIdBRjrNmiYllSUJFXgWOzj+lEMgAAlz++jN7v9Yapo2mD99t0tYGhlSEeRT3ScGRNi1wTCSZmAAeQ1EhojQRCCCGEtAuUEOioivwKiCpFTSYEgDQp4BpwAQZwDTR/VbvuDD0PzzxEzrUc9Fnep9EZejgcjs4NGxKECxC5KhI8Qx7eSHsDPCMeLZzWCjRjEyGEEPL0oYRAR5Wkl8DIxggGpgZN7icIF0BSLV3cS1Kt+avatWfoYYzh0spL6PdRP8R9HddkLYPbUDekn0vXaKyNkQ21curnBL/X/GDuao5Bnw2CsZ2xTiUFT0Njm2ZsIoQQQp4+NMuQjhKmCRstKJapXTOQfzMfFXkVCjPlaELtGXoe334M4UMhaspqELU2qsnhS27D3HB68WnUlNdA30RfI7E2RiKWIHBVIOK/icfgjYMBAL0W9ULC9gQ49XXSmdWUZY1tQPH9rf050DaasYkQQgh5+lBCoKOaqx+o28i6tuUaHhx7IJ8pB9BsUsAYQ+SqSHD0OM0mAwBg6WUJUydTZEVmwX2Eu0bibExwWDDu/XsP+mb68ulbefo8DP1qKA69cgijd43WanwyT0tju3ackWGRYBKmU/ERQgghRBH13+uo5hICiVii0MhyCXbBI8EjDPh4AILXBGv8qna3ud0AAEzEwDPgNdv407U6glu/3oLvTF9wuBz5No/nPcAfwsflTy5rMTJFCtOjGuru9KiBIYEAF2AS5T4PhBBCCNEeSgh0VHOrFAeHKTYCHXo6QCKSIP9mvrTRqOFFySJXRwIAeAY8iKvFSo27dxvmhrSz2k8IKosqcf/QffjM9Kl335Avh+DWr7eQE5ejhcga1nFCRwDSmhFdbWxHhEUA/+Wkyn4eCCGEEKIdlBDoqJL0Eljwm64hqI2rx4Vzf2dkRmSqMaqGCcIFSNyRCAt3C7xf9b78CnZzjUC3oW7IvpKN6pJqDUXasOS/kmHX3Q62XW3r3WflbYWAdwNw9p2zYIxpIbr6Dk09JP+/Lja2BeECCFYLoG+mD/8l/nDq46RTxdmEEEIIUdSqhKCsrEzVcZA6StJKmuwhaIhLsAuyIrLUFFHDZGPY+UP5cB0sHX9fe1hLU41ACzcLWLhbIONyhqbCbdCt3bfgN8uv0fsHrByAovtFSPozSYNRNezkWydRcKcAfT/oiw4DO8B9hLtONbZln4cOAzug88ud4T3OG6WPShG0Okin4iRPj6dhdi1CCHnatSohcHR0xOuvv47Ll3VnbPWzRCKSoDSrtNk1COrqENxB4z0EsloGC3cLWHpayrfLkoLmahm0PWyoOLUYWYIsdJnWpdF9DMwN4NzfGSffOIma8pp692uqUSIIFyBhWwJcglwweMNg9F/ZH7nXczEgZIDONLZln4eashp4jPKA62BXVBVXwXuct1ZqWxpDjcynB01lSwgh6teqb9Ldu3ejoKAAw4YNQ+fOnfHZZ58hK0uzV6afZaWPSgEAZi5mLXqcywAXFKcWyx+vCbJaBmGqEJYelgr3KVPLoO31CG7/dhseIz1g6tDwqsoyDv4OqBZWY//Y/QrbNdkoKU4tBlefi4kHJwIAPF/whHkHc5g4mOhMYzs4LBg9FvZA3vU8uD/vDj1DPXiM8sD9Q/e1UtvSGGpkSj0NiVFDPY66OLsWIYQ8zVr1qzdx4kQcPHgQmZmZePPNN7Fnzx64u7vjxRdfxP79+yESiVQdZ7tSklYCU2dT8PR5LXqcoaUh7Lvba6WOoDilWKGHQFn8oXzkxuWisqhSDVE1jTEmn12oOUGhQeixsAfSz6fj3LJzADTfKClJK0Hf5X1hYm8CQDpTU78V/XBl4xX0+18/nWlsPzz1EA4BDjCxk8bpPc4b9w/d13JUiqiRKfW0JEa1368vuF+0u/eJEELUrU3f9vb29li6dCkSEhKwadMmnD59GpMnT4aLiwtCQ0NRXl6uqjjbleamHG2KNuoIxDViaRG0h/JF0DJmzmaw7myNjIuaryPIvpqN0qxSeE/wVmr/kd+PhL2/PWI3xWKz4WaNNkrSL6bjUcwj9FneR2F755c7Q89QD7f33FZ7DMpKPZEKj5Ee8tteY7yQG5eL0izN9Vwpo3YjU9Pvp654mhKjwJBAcPQ4AAO4+lydio0QQp52bUoIcnJysGHDBvj6+uJ///sfJk+ejDNnzuDLL7/E/v37MXHiRBWF2b4os0pxY7RRR1CSUQIOlwPzDq1LYvhD+VqpI7i9+zY6T+4MfWPlV0qefHQyAOnsPpqa8pMxhoiQCPR+vzeMbYwV7uPyuOj3UT/EfBYDJtH+LEhMwpB6MhUeozzk20zsTeDc3xkPjjzQXmCN6Pa6dP0MTb6fuuZpSYwE4QIwkfQzLqmR6ETNDCGEPCtalRDs378f48aNA5/Px549e7Bo0SJkZmZi9+7dGDp0KGbNmoV//vkH58+fV3G47UObegiCXJAbl9tg8au6CFOEMOebg6vXuvxSG3UE4hoxbv9+G76zmh8uVFvCDwkApNO8amrKz7Szaci/kY8+7/dp8H7fWb6oKa3B3YN31R5Lc3Kv50JUIYJLoIvCdu8XvXH/sG4NGwKAc+9Jh3+Bo5tTuGpKYEig/BzoYmIk67Uw62AGlyAXWHpZ6kwhPSGEPAta1YKbO3cuXFxcEBERgfj4eCxZsgRWVlYK+7i4uODjjz9WRYztTkl6Sat7CCw9LGFsb4zsK9kqjqpxxamtqx+Q4Q/hIz8xH+X5mhtilnoyFXqGeuAP5iv9GFmjxL6XvXTMvpLrLbSFrHegz/I+MLQ0bHAfngEPfZb3QfSn0VpfKyH1RCrchrnVq3/xGueFh6ceoqZCc4lqcwThAiT/nQynfk4wsTeB7yzfdtvIvBxyGWDQycRI9ncXuCoQFfkVGPDJAJSkl2DAJ7ozuxYhhDztWpUQPHr0CN9//z369u3b6D7GxsZYtWpVqwNrz4Rpwlb3EHA4HI0PGypOKW5V/YCMib0JbP1skX5ec70Et369BZ8ZPuBwOUrtX3tcdfe53ZFzLUfp9RbaIuV4CgrvFiLgnYAm9+s+vzuEqUI8PP1QLXEoK/WE4nAhGTs/O5g4mujEytTAk/fT2NYYAz4egF6Le6Esu0wjSZ6uEYQLELU2CnrGeuDqcXVqGlvgyVS2nSZ2As+AB89RnrD0sIRTHyedmV2LEEKednqteZBIJIJQKKy3ncPhwNDQEAYGBm0OrD1ry5AhQFpHkHoqVXUBNUOYKoR1F+s2HUM2bKjL5MbXA1CVquIq3P/nPmZenan0Y2SNksCQQGRczkD0Z9EAIB9aoY5GCWMMEaER6PdRPxiYNf03ZWBqgID3AhD9aTQ8nvdQeSzKqC6tRmZEJkbtGFXvPg6HA+9x3nhw6AG8xypXxK1OErEEfT/oiytfXIHrc65wCXRBzGcxGPLlEPn97YEsMXLs44gOwR1wd/9d8AfzwdPnISJUOuWotocPyWbPurHjBhwCHMDhcuA52hMpx1Lw/LbntRobIYQ8K1rVQ2BlZQVra+t6/6ysrGBsbAx3d3esWrUKEkn7+FFVpZryGlTkV7R4leLaXIJdkBWZpbEi0+KU4nprEChLNg+62zA3pJ1TvHqsqnnQ6861nrwvGTY+NrDzs1P6OWTrLQCAQ08HlGWXydd7UMX8+g3NB3//3/soSS9BlbBKqRj9F/sjJzYHWQLtrAmSfi4dFu4WsPKyavB+73HSOgJtD2sCpO+nYx9HOPR0gJG1kXzIUOzmWJ1ZL0ETawRIxBIErQ5CeU45vMZ4gT+Ej/Tz6UovLKhJ2bHZcOztCADwHO2JB8ceqOyz9DSsx0AIIerUqoRg586dcHFxwcqVK3Hw4EEcPHgQK1euRIcOHfDdd99h4cKF2LJlCz777DNVx/vMK8koAc+QJ59rvjUcejlAXC3G49uPVRhZ49pSQyCbB/1R9CMUJhXKG9mqnAe97lzrt3bfgu8s31Y/h4G5Aaw7WSM3LrfNsTUWI5NIewccAxwRFR6lVIyx/xcL+572iF4fXe8+TTRqUk6kNDhcSIY/mI9qYTVy41V33toi/Xw6XAe7ym/3fq83bu+5jbLsMi1G9YQm1ggIDgtGp5c6oeJxBVwHuUoTgv8K/HUlMZLJvZYLp95OAADXwa6oyK1Q2Xfc07IeAyGEqEurhgzt2rULX375JaZMmSLfNm7cOHTv3h3ff/89zpw5Azc3N6xbtw4rV65UWbDtgWy4EIej3Nj2hvD0eXDu54zMiEzY+dmpMLr6RFUilGaVtrqGQHbVPSI0AqbOpkg/n46ie0Uqnfqw9nNUFVch81ImnPo44crGK61+DscAR+Rcy4HXGK82x1c3RgCw6WqD4pRi5CXkKR0jl8dF5uVMcPW4yLuRB/vu9gAU6x/U6eHJhxj8xeBG7+cZ8OSrFjv6O6o1FmVkXMjAwLUD5bdtfWzhNtwNcVvjMDB8YBOP1Iy6n4nAkEC1rBGQcjQF7sPdoWekB/4QPk69eQrVZdUwMNWdoZ8SkQR51/PkPQT6xvrgD+Mj5VgK7Hzb/h2nqXNNCCG6qlUJQWRkJLZt21Zvu7+/PwQC6RWWgQMHIi1NNwoInyZtKSiuTVZY3HNhTxVE1biStBLw9HkwczZr9TFq/xgfnXkUTMJU/iOs8IPPQZuSAQBwCHDAo6hHKosPqB8jGFoUY+3HH5lxBHMS5misUVOUUoTi1GK4DXVrcj+vF70Q900cgkKD1BaLMsrzyvH49mN0eK6Dwva+y/ri0JRD6L+iP/RNlF+fQl1qv6eCcAEkNRKVv5cPjj5A16ldAQCWnpYwdTLFI8EjuI9wV8nxI8KkV9gbilkQLpDW5zTTE/H41mNw9Diw7vSkVslztCfuHbiHvssan9yiJWqf68jVkWBi1X8PEUKIrmpVPyifz8eOHTvqbd+xYwf4fOk0jo8fP4a1ddsKTdujthYUy2hqxeLilGJYuFsoPVtPYwJDAsHV44JJmNrmQZfNtQ6GNj+HrIdA1QJDAsHV57Y6xsCQQPR+vzfyb+Rjk8EmjV3hTD2RCpcgFxiYN31VWVdWLc64mCGd+chOcWgefygfZq5muPXrLS1FVl9gSCDAlS7GpeoVequKq5AVkQXP0Z4ApMXfsjoCVVHFcJzs2Gw4+jsqfM94jfZCxqUMVJdUqyxW2XcEEzNwDWg1ZHWgeg1CdFOrEoIvvvgCmzdvRs+ePTF//nzMnz8fvXr1wldffYUvv/wSAHDlyhVMnTpVpcG2ByVprV+DoDaXQBcUPShCWY56x0O3dQ0CGUG4ABKRtIBRXfOgR66JBBjA1W/7omIO/g4QpgpRUVChwgghvwrclvngh24aCg6XA0mNRGOLTKWeSIXnKM9m99OVVYvTLyjWD8hwOBz0WdoHsZtj21SUr8pGz6WPLwESyN/T88vPtzquuh6efgjrztYKkwKoOiFoaHrelvZc5cTmyIcLyVh5W8HSw1KlU9meWHhCvh6DpLplqyHrQkNXF2JozrNSr/E0nGtCWqJVf3njx49HUlISxowZg4KCAhQUFGD06NG4c+cOXnzxRQDAW2+9hU2bNqk02PZAVT0ERlZGsPW1Vft6BMUpbU8I5AsPhQaCw5M2yFQ9D7ogXIDIVZEAgCUFS9o837yxjTEs3C1UWlgsOw/OA5wR8HZAq2MUhAukjVkNLTIlrhEj7UxakwXFtXmP88b9Q42vWqyJH9qMCxngD2l4Ubqu07qiSliFB0dbn7SoqtEjCBcg+tNomDqZYqloKRx6O+Dql1dx+ZPLrY6ttgdHH8h7B2T4Q/h4FPMI1WXNX3lX9r2qnRRsNtzc4p6rhhICAPLpR1VBEC7AjR9uoMPADhi6eSgsPC1a9PenCw1dTcTQ1r/Pugkik7B6CeLT0NjWhfebEFVqcQ1BTU0NXnjhBWzbtg3r169XR0ztmjBdqJIeAuBJHUHnlzqr5HgNEaYKYd/TvtWPr/tDkLQ3CW4j3GBoZaiyedBlz+Ezwwc5V3NgYGbQYBFhSzkGOCI3Lhfuw9s+1rr2eUg7mwb7HvboPq97i2OsfRyf6T7Y2X2n2ueTfxT1CDwjHhx6OSi1v/c4bwjWCFBTUQN94/rj9GU/tIBizKoqjq54XIG8G3lwHVS/hwCQDtXyX+KPq5uuwvvF1q2ZoIoiVdn+1l2s4feaHzgcDmYIZuBnn58RtS4KXH0ugla1vhaDMYaUYykYu3uswvaW1BE09171+18/3D1wF2nn0uS9DuJqcYt6ruoWFNfmOcYTJxeeBGOsTRMxyBtxelyM3zceTMxwYfkF9FneR+m/H10oTNZEDKr4+6wdp+xYLoEusPWzRXleudq/A1Sh7rnus6wPrn55lQrRyVOrxQmBvr4+EhIS1BFLu8cYQ0maanoIAGlCEP9tvEqO1ZjilGJ0nNix1Y+vveAXANj62SI/MV+lC37JnkM2Nlimrc/hEOCgsjoCWYwDPhmAa/93DXbd7VocY0M//C8ffxl/jfhLrUlB6olUeIz0ULqOxNbXFqZOpkg7m9bgImXqbtRkXMqATVcbmDqYNrpPzzd7ImpdFHLiclo9I5JCQfAa6ZC4lsQvEUvQf2V/XP3iKrq8Il2wj6fPw6zYWdjReQfuH7ov7VVrZUM473oeqkuq0WGgYmF17TqC5hKCuu9Vr8W9cGbJGdz5/Q5MHE0Q83kMbH1twR/Ch62vLfJv5IPD5ch7rpQ5F4/vPAaHy4F15/o1afzBfFTkVeDxrcdtmlFNIpbAxtcGHs97yD8X/CF8mDmbtWg9hqaKwFVRXN3SGCLXRIKJVFscraq/z8CQQESGRYJJGLh6XNj3skfkqkjk38yHTRcbOPZxlM4MV1SFIV8OadFzaPJc15TXSM/1f6+FkgHytGrVLEMzZ87Ejh07aJ0BFasqqkJNWY1KE4IT8080eiVWFdqyKBmAel/Kdn52eHxTOre4qr5UZc/xz8v/wLm/s8J9bS0svr37dptik5HFWPqoFBUFFbD1s5Xf15IGZN0fI/4gPkZuH4lTb51SWz1JyokUBLwToPT+HA4HXi96NblqcWBIIIpTi9Uyu07GhQzwBzc8XEjG2MYY3eZ0Q+zmWIz5ZUyrn8tvjh8iVkVAImp5QXBwWDBu7b4FGx8bhdl1DC0NMfPKTPzW/zdc/fIq+i5v3Sw7D44+gMfzHuAZ8Ordxx/KR+KORKWO09DVXqd+Tuj7QV+4DnKFqYMpBOECxG+NR483eiDpjyQEvBegdJKaE5sDh14ODQ7B0DPSA3/of9OPtiEh6PJKF1z5/IrCuew6vSvit8Zj1tVZLTpW7/d6IyI0QloLBOkw0PuH70vXFlmtmavehtaGAAAmYuDocVTeQK39nketjYK4Wtziv8+LKy5KkwF9LiQ1Epg5m2HOjTmoeFyBzIhMZFzMQNmjMlzddBWxW2JblNhoqofh3r/3kPhzIsCFWifEIEQTWpUQiEQi/PTTTzh9+jR69+4NU1PFK21UO9A6wjQhDC0NYWhhqJLjWXpZwsjGCDlXc+D6XMPDI9qiprwG5bnlsPBUzRAnQNpD0Jax203JjctFzzdVNw2rg78DCpILUF1aDQMz1czZnpeQBytvq1bNAd/YFa9uc7qh4E4Bkvclo+JxBYxtjdsaplx5fjlyr+XC43kPpfaXXbnzHueN43OPYwQbIb/CLbty1/mlzri44iIyL2dKiztVXBydfiEd/T7s13ycelwk7U3Cc+ufg3mHJ0m6slcYMy5lYN/ofQB7UhCs7FVxmaS/ktB5cv0hfxZuFvAa54VLH1+ChYcFukzuonC/MjGmHE2B32y/Bu/jD+Hj1BvKr0cQGBIon6qTZ8DDzOiZCrHIGmEDPh6A+//cB38Iv9FGW12N1Q/IyKcfbWViBADR66PhN9sP5q5P3ufOL3XG6bdOoyCpADZdbJQ+1qGphwBIh56Jq8XIT8zHw9MPUZZdBpuuNogIjUB1aTUGfz5YLUOKkvcn4/zS8wAgb2wfmnYI4/4Yp5LjA9KLD4ZW0t+plg4BA6SfiZjPYmDhYYGFKQvl5wGQfhY6ju+IjuM7YsgXQ7DJYFOLZ9hSRS9GU70Mlz6+hAdHHkD4UAi34W64u++u/P1u6d84IbqiVVUviYmJCAgIgLm5OZKTkxEXFyf/Fx8fr+IQ2w9VFRTLcDgceR2BOhSnFkPPRK9NqyrXZednh8e3HrdphpeGVBZWojilGA7+yo1zV4aZsxlMHU2Rdz1PZcfMv5EvX1BMlZ779DlweBz82udXiKvF9e5vbaHew1MPYdfdDqZOjQ+/qU3WCMyMyJSuWvxfUbbsx/reP/fwW//fYNPFBr0W9ZLO+ALVFUdXFlUi73pegzMM1Y3z2pZrsPCwQNw3cfLtyhQMMsYQ/1089g7fi5qyGgSvCcbshNng6jVchNiYKmEVUk+kyocL1WXBt4CkWoIj048gK+rJFMPKxFhRUIGsqKx6BcUylh6WMHU2RVakclMXR6yKkE7V2cAMXrV7rjhcDrzGeuHB4Qfy4tLmhuM0lxC0dfrRwnuFSP47GX0/UkwoDC0N4TXWC7d/V74XMHJ1JFKOpaDzlM54v+p9BK8JRpYgC35z/DDzykz4veYHc745rmy4gi95X6o8Gci4nIHD0w7Le9SWVi+FzwwfJP2ZhKOvHVXJc+Rcy8FvA36TT9IAtOzvU/b5NLQyxOCN0oUMG5qJSravpEYCjl7LZ9iqfcwv9Vt+rhsrGj46+yiiP41GTVkNus/rjrv77iJ4TbD8/Vb1hBiEaEqregjOnTun6jgI/ksI3FSXEADSYUNp59SzQJwwVQhLD8s2FfPVZd3JGpIaCYofFsPK00plx82Nz4W5q3m9eefbSlZH0CG4Q/M7KyEvIQ92PVS/ujSHy0GXV7ogam0Ufgv8DbOuzlK4Mt/abvTUk6lKzy4EKF65s/W1xf3D95H0VxJiPouRjiPubo+JBybi1q+35DEVpxQj93quSuogMi9lwqqjVbML6dWOM/arWAz4eABiN8fKY5KIG77aL6oS4ffg35GXkFdvmJNLsAt4hjylX8eDww9g3dm60avTtWPcO3wv5tyYg9u/3Vaq4fPw1EPY+dkpXBGvrXYdQXO9P4JwAQRrBDC2M8ai3EWIWhul8Brr9lJ4veiFS/+7hCFfDGn2HEjEEuTG5zaZEFh5W8HSUzr9aMcJLa9nivksBl2ndW3w+8Znug8urbiEoFVBzX7PCcIFiAyLhKGVIV78TTrbXu33iMORDt3pv6I/NhlugqRaAg5PdcN58m/l4+/n/673uRu7eyzE1WLc+vUWuPpcvLDjhSaP09iV8SphFf4e9Teyr2ajQ3AHVBZWImh1EFKOpsDA0kDpz7VELIHPDB+kn0tXeL/q1ksp9Cx9MgC7++7G1S+vQs9EDwPXKLeSuPd4b0SERrRq6FTdXobe7/fG3mF7kX0lG97jveHY2xGRqyIVzrUqJqsgRFvaNC/WvXv3cOLECVRUSOdiZ0y1V3XbG2GaEBZ81Q2/AaQJQVZklsqvuAP/LUrmodp4eQY8WHe2ltcRqEpuXC4cAlTXOyCj6gXK1NVDAAADwweiz7I+yL2WKx3KgpbPB197OkDGGB6efChff0DZXgbZlbvHtx4jclUkYj6LgVVHK8y8OhNjfhmjkAwEhgSi7wd98fjmY/Rd3rfNV9/SL6Q3Wz9QO86g1UEQV4qxxWILIkIj4DfXD30/6Nvg1cPSR6XY0WmH9Ip2H8d657TXol4ouluEoNVBShWpJv2V1GjvQO0Yg9cEQ1Quwo8df1T6vXxw9AE8xzS9boQy6xHIPj9mHcwQuCpQ3uht6kqp+wh3FKcUo/BuYZPHBoCCOwUAAJuuTQ/Zae30o8I0IW7tvoX+K/o3fNwxnijLKUNObPN/4xKRBKbOpgheEwyu3pOf1ro9IYJwASTV0iEwTMzw7yv/tjjuukoyS7DvhX1w7Ff/cwcA4/eOR8cJHXFr9y1kX81u8lh1P9uMMST9nYTvXb/Ho6hHcOrthIwLGQheE4yg0CAMCBmAvPg8DAgZoNTfZ3BYMCoeV6DHGz3A01esX5ElkHW/lzgcDqZHToc53xxR4VHSNWWakRmRid8G/CZ9TfpcMBHDkVlHmn1cvXj++yxvMd+C7CvZ6P1+b0z6Z1KjBcTK9nwRomtalRA8fvwYw4cPR+fOnTFmzBg8evQIADBv3jwsW7ZMpQG2J+roIXDwd4CoXISCpAKVHhdQ3aJkddn52SE/MV+lx8yNy1XpcCEZB38H5F5TzVoEEpFEOltKd9X3EMgM+WIIerzRA6knUrFJv+UrGdduLOQn5qOyqBIuwS4tnns7MCQQXAPpvlx9LubfnQ+HntL3p97MUz628HzBE+CizT+0jS1I1pig0CBpnP/VAaQeT8U31t8g42IG3Ee4S2cXWR2JR9GPsKPzDpSkl2BAyADMiJxR75x2mtQJogoRnPo6NVt/UF1SLR168krzUwYHhgRKC4P/i7G595JJpNONNjZcSIY/hI/smOwm1yOQiCXo8UYPVBVVwe+1J/UITTWKDMwMwB/KV2pxuqYKimvzHO2JB0cftPiiVMyGGHSc2LHRXhh9Y310eqkTbu9pftgQfygf4koxus3tVu++hhq6S6uXwmemD5L/TpYuiKaEhubnryquwv4x+2FsZwy3IW6Nvv8TD04Efwgffwz6A3mJ9Yc5yhL62o3gc0vPYf+L+3Fs9jFUl1QjaHUQPF7wUPj79BrjBXNXcxjbGCv191l4txDpZ9PRY0GPRvdpaIIEngEPM2NmwsDcQFpf1ISU4yn4c9ifEFeK5ee68+TOuL37Nk4vOd3kY+uqnYzyDHgYumkoAGli09i5bqhnjBBd16qE4P3334e+vj7S0tJgYvJkCMbUqVNx/PhxlQXX3qhyylFA+uMR83kMnPo51asjUMXiLm2dYagxtn62Ku8haMv0kU1xDHDE41uPIaoUtflYBckF4PA4sPK2antgTRi5baR0TK6o5cW6tRsLF5ZfgNtQN1zZcKXFiYXsKinPgCcvtpVp6Ie274d9cX3bdQS8E9DqH9oqYRVyr+Uq3UNQN04mYej5Zk/MipuFjhM7wsDcADwjHiLDIvHbgN9QU1qDoNVBjQ5n4Onz0GNhD8RvjW/2ee8fvg/rTtaw7Wrb7L6CcAHE1WJwDbhgEoZ/pzZ9xTk7NhuSaglcAl2a3M/SwxKmLk3XEQSHBUNSLYHvLN96kyE01SjyetEL9w83vjidTM61pusHZPiD+ajIl04/qqyy7DIk/pSIASsHNLmfz3Qf3PnjTrMN3Ssbr6DnWz0bnWCgod64sb+OhedoT9z44QbOLzvfbMx1r96LqkQ4OOkgasprkBuXq9Az0RDXga4QVYiwJ3APiu4X1YuNiRhSjqdAVCGCOd8csZtjkXo8FaJykbxHoO7fJ4fDwYCQAYjZEIO+H/Rt9u8z/rt4dJzUsclhe401tk2dTDHl7BRkRWYh5UTDPUJ39t7BgfEHIKlWTCrG/zUe/KF8xG+Nx4UPLzQZo0zct3E4MlPaq1C7aJiQZ1GrEoKTJ0/i888/h6ur4pW2Tp064eHDhyoJrK5169YhKCgIJiYmsLKyUuoxjDGEhobC2dkZxsbGGDFiBO7evauW+FRBmC5UaUIg+/FgjCkkBKpaSVGYKlRfD8FN1fUQ1FTUoOBOgVp6CCzcLWBgbqCSHo38G/mw9bNV+wqXgnABmEh6JbU1P3CypCD1ZCpSjqe0KhmQPUbZQrwOQR1g38Me8dviWxRrbZkRmbDwsGh03LwycUauikTyX8nwX+yPCfsn4N3Sd8HVl75fPAMegkKbXiisx8IeeHjqIYpSiprcL/mvZKV6BxSuOFctRZdpXZC8NxnnljZe55VyLAXuI93rDdeoq3YdQWMqCipw5/c76LW4V7Ox1uY11gsZFzNQJaxqcr+c2Bw4BjSfEOgZ6cFtmFuLhg1d+fIKPEZ6wL5H00P03Ia6gUkYMi5kNLpP/s18pJ1JQ8DbjU+/29BVbwB46chLcOrrhFu7bzVbGF07IY9cE4njc4+jIKkARfeKlPoblA2Dqymtwa99fkX+zXwcmXFEPuwr+rNonF50GqWPShG0OkieZDZ34aDj+I4wsTfBjR03mnz+mvIaJP6cCP/F/k3u1xSnPk5wf94dByceROE9xWFnCT8k4OjMo7DuYt3g+ZhyZgrse9jj1m+3UFNR0+hzMMYQsSoCF5ZdkCcWVDRMnnWtanmUlZUp9AzIFBQUwNBQNVNm1lVdXY1XXnkFb731ltKP2bBhA7Zs2YJt27YhOjoapqamGDVqFCorK9USY1tIxBKUZpaqbJVi4MmPR+alTNz/V3o1TpXT3KmjhgAA7LrZoeB2gcrGYObfyIehpaFKky0ZDoejsgXK8m/kN9s4aava73+H4A7oOLFjq37g+n4onZFFNs1ka5KB2oV4yvzQ9vuwH659dQ2iqtb1xmRcyAB/iHK9A8rGGf1ptHxaVGWSK/MO5vAa54Xr2643uk916X/DhRqYbrS5GMf9Pg5OfZ0QuzkWkasbHmedcrT54UIyzSUEiT8nwqmfE+y7texza+VpBevO1kg9mdroPhKxBLlxTRcU19ZUHUHdoTbl+eW4/t119P+4f7O9pVw9LrpO7drksKGrm67Cd6Zvk7NtNXbVm8Ph4NWIV2HX3Q6Hpx9u9nsvMCQQ/Vf2R+SqSNz5/Q7Ksspa9H0eFBqEoNVBqCqqws5uO3F7z2049nbEoM8HYUHKAix4sACjfx6N0oxSee9Yc59tDpeDAZ8MQMxnMU3+fd7ecxvmfPN6i+G1lGOAI8SVYuwJ3CNPKmM2xODM22cgqZGg65SujZ7rGTEzYOVphWOzjzVYWycRS3D6rdOI/SoWokpRq76rCHkatSoheO655/DLL7/Ib3M4HEgkEmzYsAFDhw5VWXC1rV69Gu+//z66d++u1P6MMXz11Vf45JNPMGHCBPTo0QO//PILsrKycPDgQbXE2BblOeXSxVk6ND37SUsFhgSi34p+qCyoxCbDlo8Zb0yVsAqVBZVq6SGw8rYCkzAUpxSr5Hiy+gFVzoZUm6rqCPIS8tRWUAzUb0Dyh/BhYGHQqh+404uk43Bb2o3e2FVSZQrxvMZ6wcjGCLd+vaV0nLWln1e+oFiZOFvT0wEA/ov8kbgjsdFhZg8OP4CllyXsfJuuJWksxqnnp8LY3hgPz9bvrS3PK0f2lWxpTYYS3Ia6NVpHwCQM17+7Lp0ethW8X/TGg8ON1xEUJheCSRhsfZofNgVIE4LGph+tO9Tm2v9dQ4eBHZB6PFWp3lKf6dKx/g01dksfleL2b7fRZ1kfpeJsCE+fB8fejsgSZOHiRxfr3S9LWnLjc3Fy4UnEfhUL/Pd11po1OoJCg+QL0vEMeJh1dRZ8Z/jKJ7VozWe788udYWBhgJu7bjZ4P2MM8Vvj4b/Ev83fxYEhgQgKC0JFfgV299uNiysuInJ1JMRVzS+QpmeohwkHJuDhqYf4c+ifCveJKkU4NOUQkvclw8bHhoqGSbvSqoRgw4YN2L59O0aPHo3q6mp8+OGH6NatGy5evIjPP/9c1TG2SkpKCrKzszFixAj5NktLS/Tv3x8CQeNfalVVVRAKhQr/NKEkvQSmTqbQM2zVTLBNGvTpIOkCT9WqW+BJmCqEgbkBjKyNVBChIq4eFzZdbVRWWJxzLUctw4VkHAMckRPX9h6CvBt5ai0ortuAlF39HfDJgBb9wAnCBUj8KRF23e1a3I3elkI8DpeDvh/2xZWNV1r8Y1xdWo3sq9lKFxQ3F6esgdmaq4f8oXwY2xsj6a+kBu9P+jtJqeFCjcWob6KPKWemIOdqDtIvKF7dTz2ZCodeDs1Ouypj6WEJsw5mDdYRpJ5MRU1ZDTpN6qTUseryetELD44+aPS9zInNgX1P+2bHxQPSHoDbv92GpaclHp5RTITki7T9995c+vgS4r6Og5mrmdIXSJz6OcHI1qjBHoi4r+Pg/ry70olLYwzMDFD5uBLx38Yj4ceEJ69tlXQF6Bs7bmBP8B4wxqQF3Kz149pldScNPb61vXgcLgcDPh6A6PXRENfUX+skS5CF4pRi+MzwaVGsjQlaFYR+K/qhMKlQ2jNRLlL6YpeJvQl8Z/ki42IGDr50EIC0OHvf6H14FPMIFfkV8B7rTUXDpF1pVULQrVs3JCcnY+DAgZgwYQLKysrw0ksvIS4uDt7e3qqOsVWys6VTqzk6KnY3Ozo6yu9ryPr162FpaSn/x+crX4DYFsI01dYP1CYIF0hnIOFxVFYUJZthSF1X3VVZWJwbl6uWgmIZxwBH5F3Pa/BHUFlVwioIU4VqHTJUtwHpHOiMskdlKE4tVvoHTtZYMHczR+93ewPQbDe6z6s+EJWL5EPglJUVmQVzV3NYuqumR6stPR0cDge9FvVqsLi4uqwaKUdTmp1utDn23e0xeMNgHJ15FBWPK+TbU46mNDvdaF2NDRuK/zYe3Rd0l19pbimXAS5gIobsKw1/Hze3IFltsgTNyEax0S77vHK4HPjM9EHnKZ0R/Wk0qoRVSNyRqHQDksPhwGe6T71hQ9Ul1Yj/Lr5NqyTLyKeQrRDh9KLTuPXbLfw+8Hf5Gg/9PuiHNzPfhIWbBa5vu97qce3NXf1vy2e7y9Qu4BnwcGt3/V68+K3x8Jvj16pV2Bsz6NNB8tnKWnqxa/iW4fCb7Yd7B+7h36n/4s8hf6IkswSlGaUqXSyOkKdFqy9HW1pa4uOPP27Tk//vf/9rtkfh9u3b6Nq1a5uepyVWrFiBpUuXym8LhUKNJAWqXqVYRvbl7/68OwzMDeDQy0Eli6YUp6hnylEZVRUWS0QS5N/IV8saBDLWnazB1eei4E5Bq4f85Cfmw8TRRKWrPjfHwNQATn2dkH4+XelF4CRiiXysMH/Yk7+LuosKqQvPgIfe7/dGzOfS6SKVTUhbOt1oc5pKnpT5u/J7zQ+XVlxCdmw2nHo7ybc/OPIAFh4WsPNre09Rr0W9cG3LNfw+8HfMvTVXOt3o8RRMOjQJQK0r580kgvwhfCT8kKCwrTi1GKknUjHi2xGNPKp5XD2udLrQww/gMqD+jEc5sTnwm+vXwCPrq70g1ONbjxEUFoTzy8/jzp47sOpohbgtcYgKj4JddztwuBylCmXr8pnug18DfkWVsEo+o9KNn27AupM1XAep5rNV+3UcnSldWdhvrh9e2PECOBxOo1fvZY+pfbshyjy+LZ9tLo+L/iv7I2ptFPxm+cl7d8pyypC8LxmzE2Y3fQJaqPYsYLKLXS15T0fvHI3Kwkok702Wfy4oGSDtVaunMykqKsLJkyexe/du/PLLLwr/lLVs2TLcvn27yX9eXl6tis/JSfojm5OjOJQjJydHfl9DDA0NYWFhofBPE0rSS1RaUAwofvn3+7Afcq7lqOxqrjBVqJaCYhm7bnYq6SF4fOcxwJU22tWFw+XAoWfbCos1UVDcEGUWn6otOCwYHQZ2gJmrWb0kQlPd6D0W9EBBUgEyLjU+60tdGRcyWjTdqLoZWhrCd6Yvrn+nWFyc/Fdym3sHZDgcDjq91AkFdwpwYMIBZMdIr8Q793du0Uxj8vUISp+Mzb/+/XV4veil9IxNjWls+lEmYdKpgpWYYUgmMCQQgaGBqBZWY5vLNtzZcwf2vewR8G4AXjryEt4peQedJnWSJwMt7S219bGFTVcb3Dt4D4D0YkPs5lj0Wd5HpT2l8nUlIE2AR/80Wn78tly9V8XjleEz3QdMwnDnjzvybTd+vAHXQa6w6dz0AnMt0doanrom/TNJunBZK5JEQp4lreohOHToEGbMmIHS0lJYWFgofBlyOBy89tprSh3H3t4e9vbqaQR5enrCyckJZ86cQa9evQBIr/ZHR0e3aKYiTRGmCdEhuG0zL9RV+8u/4nEFhKlCVBZWquRqbnFKMfhD1dfAsvWzRcGdAkhEEqXGEDcmNy4XDj2bX9iorRwCHJAblwu08gJYXoJ66wcawx/Cx8kFJ1v0mLSzaXAb5qamiJpnYG6AXot6IebzGPAHNf8ZrCmvwaOYRxj9y2gNRKe8nm/1xJ7APRi8cTCMrI1QXVaNB0ceIHCV6hokg9YPQllOGW7+fBPF94vhMcoD0Z9Gt2hygdp1BB4jPSCqFOHGjzcw7s9xbY7PY5QHjs46CmG64irthXcLIamRwNa3ZePyg1cHI/qzaPlV49lxT/4g614dl90GlOvViQiLgLGDMW7vuQ2/1/yQ/HcywAE6v9RZ6d4WZTQ0vl8WX1t7ptr6eGVErZP2xESti0LXV7sCDLi+7TqGfzNcZeeprT0ldY9Vd6YwSgpIe9SqVtKyZcvw+uuvo7S0FEVFRSgsLJT/KyhQ/Yq4AJCWlob4+HikpaVBLBYjPj4e8fHxKC0tle/TtWtXHDhwAIA0MXnvvfewdu1a/Pvvv7hx4wZee+01uLi4YOLEiWqJsS3UsUpx7THjxrbGMHczlzZa0faruepalEzG0tMSHC6n3jzTLaWuFYrrcgxwbNNMQ/k38tU6w1BjXIJcUJpZiuJU5Wd0Sjuj3YQgIiwCEpEE6WfTkXdDccXVhqaQzBJkwdTRVK1D3FrDoacDHAMckbgzEYB0fL+Fm2qGC9U2+qfRcB3kise3HiPpr6RWzTRWuycp+e9kGNsZq+SCgLGNMToEd6i3arGsoLi5tRLqamgIiWx7a4vAZbg8Lh6efIiHpx6iLKcMVzZeQZ+lfRC9Plol67rUjfNpnfeey+Pi/j/3UfqoFMl/J+P+ofsAF8iNz1XZeVJVT8ezcL4JUZVW9RBkZmbinXfeaXAtAnUJDQ3Frl275Lf9/aULm5w7dw5DhgwBACQlJaG4+EnD5sMPP0RZWRkWLlyIoqIiDBw4EMePH4eRkepnxmkrYZriFTJ1cPSXzobT1sYcY0xeVKwuXB4XNj42eHzzsVKrtTYmNy5XZbNaNMUxwBFn3j4DJmHgcFs2fIAxhrwbeVoZMmRgagCnftI6Ass5zb+flYWVyI3LhdtQ7SUEXB4XVzZcgVMfJ1zZeAVjfhkD4MmPO38IX+Eqn6x+QDYGW1VXctsqIiwCps6miP82Hr3f7Y2kv6SzC6kjzldOv4KvjL8CE7VsWEREmLQBxx/CR8J2aR1B3NY49FrUC1Fro1QSo9eLXnhw+AF6vdlLvi07NlvpgmKZpnoAmmpAyu5vTu0r0H8O+xPlj8pRnluOqLVRKhl3rsqr3tpUO+az75yFXTc72HS2QWRYpMrG56uip+NZOd+EqEqrUvVRo0bh6tWrqo6lSTt37gRjrN4/WTIASBtWc+bMkd/mcDhYs2YNsrOzUVlZidOnT6Nz5+an89M0UZUI5TnlKu8hqMshQDXz5VcWVqJaWK3WGgKg7YXFjDHkxmumh8DGxwaSGkmrejRKMkpQLayGjY/qxte2REvqCNIvpMO6izXMXFS7XkZLyK4CZl/Nxu09tyFMEyr8uLsNc1O4yierH1DVCt2qwuVxkfx3MsoeleHev/fw4MgDdHmli1rijPksRr6IXEvGzstm78lLyEP2lWxkXMpA/o18lGWXqSxG7xe9kXYmDTXlT1aOzYnNUSi2bk5zPQBcHlclU0gGhgTCa5wXCm4VoEpYpbJkANDM+H5NkdVzlOeWI+1sGh6efqhzxbrP0vkmRBVa1UMwduxYfPDBB7h16xa6d+8OfX19hfvHjx+vkuDai9LMUnD1uTB1bHyVS1VwDHBUKPRqLWGqEEY2RvKZNtTFtpttmxKY4tRi1JTWwK6b+sfm8/R5sOtuh9y43BYXzuXfyId1Z2voG+s3v7MatKSOQNv1AzK1r+Rtd98OALDvYQ9xtRiWHpbwm+2HiNAIiCpFeBT9CA69HHBtyzWdapTUfg1HZhyBuas57h68i8hVqruSCrRt7HztGA0tDXFi/gnY+tgi+tNolcVo09UGpi6mSDuXBu+x3mAShtxruRj21TClj6GKHgBljf55NLbab23xKt3N0cT4fk0KXh0sXclbpLr1b1TpWTvfhLRVqxKCBQsWAADWrFlT7z4OhwOxuPXzsbdHwjQhzDqYtXioSUs5+Dug4E4Bqsuq2zQXtLrrB2Ts/OxavSotAORey4Wtr61aFntriGOAI3Ku5aDr1JZNk6utgmKZ2nUEzb2vaWfTELxG+8NtAOmPdtTaKIirxeDocdB5cmcIHwqRJciCMFUIDpeD6E+jAUDnkgGZwJBAVBVV4eqmqyi6X6T2ZED2nIDywyJq719VXAUAKo2Rw+HIVy32HuuNwnuFEFeJYeun/FBBTTbu4r+Nr7comK59rnSBIFwgTwboPBGi+1rV3yuRSBr9R8mAciLCngxpqDvlaEOFkapg5mIGE3sT5Ce0bX7/4tRiWHiqfzpWWz9bFCYVQlzdus+UpgqKZVpbWKytgmIZ+XoEF5oeNlSWXYaC2wU6M31n7dlYmIgBAEb9OApTTk/B/HvzsbRmaasXLdKkIV8OAUePo/IrzoDqhkUEhgTKZ/tSx7mU1REwJu0dsOth1+KCYk2gIlTl0Hki5OnTooRgzJgxCkW7n332GYqKiuS3Hz9+DF9fX5UF9yyTjc0VhAtQkvZkUTJ1jnPmcDhw8G/bfPmA5noILN0twTXgovBu62Yaauk85m3lECA9t4yxFj1OWwXFtSlTR5B2Lg32Pe1hbGusoagap0yDI2pdVIMzzugaQbhAXuyr6jjrrk5dW0vGzjd0tVeVXAe5orKoUlqrUGexNl2hipmK2gM6T4Q8nVo0luLEiROoqqqS3/70008xZcoUWFlZAQBEIhGSkpJUGuCzqnY3vFMfJzgMdsDZkLOIXRuLgWsGIjAkENXV1aipqYGenh4MDZ+M1y8rKwMAGBsbg8uVJg41NTWorq4Gj8dTmEWp7r6OAY7Iis1C57LO9fYtLy8HYwxGRkbg8aRX50QiEaqqqsDlcmFsLG0IClOFcBruhLKysmb3BYCKigpIJBIYGhpCT0/6kROLxaisrGx2X1tfW+TcyIGxhzE4HI7CzFaVlZUQi8UwMDCQ17HIjsvhcJAbl4v+/+vf6L4SiQQVFRUAAFPTJ/UbVVVVEIlE0NfXh4GBgdL72ne3R1VxFYRpQujZSV+niYmJfJ0O2ftZ+7iiKhEe3XoEY29jMMbq7avMe6+Kz4ndADvEvx0PsVgsfz/r7iurH1D2c1L7/Wzr56T2ey8IF+B86Hn0/6Q/+q7oC0D69ySWiHEu9BwqqythZGAkb5T4f+CPyHWRuBh6Ub5vY+9nWz8njDGUl5crve+50HOIWhuFIauHICg0CIJwAS6EXkBFdQWCPwlu8P1s6DOlyu+IuvvGbYyTn8uey3oian0ULoVekp/Lxt5PZT8nEWERqJHUwGmYE5L/SUZObA58XvWBWCzGhbALgAQYtu5JPYGyn5PG3s+W7Fv7va/d21L7/ZR9l4tEIvm5bMnnpLnviOb21dR3REPvZ0P7SsQS9P6kN3os7aHwfdJ3RV9UVFegorICtanjO6Kl+7bk776xfWXnj5CnFmsBDofDcnJy5LfNzMzY/fv35bezs7MZl8ttySF1XnFxMQPAiouL1XL8yDWRbCM2MgAMADvy0RH5fWvXrmUA2Pz58xUeY2JiwgCwlJQU+bbNmzczAGz69OkK+9rZ2TEALDExkTHG2J29d9hc/lwGgE2YMEFhX3d3dwaAxcTEyLft3r2bAWAjRoyQb/vJ9yfW0a0jA8DOnTsn337gwAEGgAUFBSkct0+fPgwAO3z4sHzbyZMnGQDWs2dPhX0HDx7MALC9e/cyxhg7Ouco+3bOtwwA69ixo8K+Y8aMYQDYzz//LN8WFxfHADBnJ2e2ERtZZXElY4yxyZMnMwDsm2++ke+bnJzMADBLS0uF486ePZsBYBs2bJBvy8jIYACYnp6ewr6LFi1iANiqVasYY4z93P1ndnX3Vfn7WV1dLd93+fLlDABbvny5fFtmbKZ838LCQvn2VatWMQBs0aJFCs+np6fHALCMjAz5tg0bNjAAbPbs2Qr7WlpaMgAsOTlZvu2bb75hANjkyZMV9nVxdmEA2MUjF+Xbfv75ZwaAjRkzhjHG2Hav7ez+0fusY0fpe3/58mX5vnv37mUA2ODBgxWO27NnTwaAnTx5Ur7t8OHDDADr06ePwr5BQUEMADtw4IB827lz5xgA5uvrK992edVl1terLwPAdu/eLd8eExPDADA7Qzu2ERtZ5JpIxhhjEyZMYADYR+M/km9PTEyU7mtnpxDD9OnTGQC2efNm+baUlBQGgJmYmCjsO3/+fAaArV27Vr4tNzdX/n7W9u677zIAbOXKlfJtZz45I9+3tLRUvv21Qa8xAGzKgCkKx5Dtm5ubK9+mju8Ixhjbvn07A8Ce6/qcwrmUfUf8+MaP8u0NfUcwxpivr69S3xGRayKZK1wZALas8zK2xXILy47NZl+99hUDwDo5dVI4bt3vCMYYu3z5cou/I1xcXBT21dR3BGOMFRYWKv0dUV1drRvfES7S74i4uDj5trrfETLa/o5gjLERI0Y0+h3h7u6usK/sO2L79u3ybS39jlA3dbdFCKGUVssCQwIVion7Luur1udzDHBEaVZp8zs2gv23BoGmxvfa+dmhOEX5RbNkJDUSWHW0UvtMSHU5Bjgi97rydQT5iW2r51CZ/z6COVcbHk5WnFqMkrQSuA501WBQDQsOC4alV+ND1vSM9BocN+/9ordOTSfYWByyc8xaOPRMLVjDBcQ95vVQ2bkMDAmUT2NbmFwIUYUI9/69h5u/3AQAmDhqbr0bQghprzisBb86PB4P2dnZsLeXjnc2NzdHQkICPD09AQA5OTlwcXF5pgqLhUIhLC0tUVxcDAsL1RfSysZbivRFkNRI5EMHAPUMB2AShq+svsKE4xPgEuDS4iFDZbll+M7xO8zPnQ99E321Dxl6cOwBzr53Fq9ee7VFwwGufnEVxTeLMX7v+Eb3VeWQoSufXYGhgSEMLQ2RciIFL+x9AYC0i1+2gFPflX3rDQe48NEFFOcWY/g3w7U+HOBSyCWIHoswdufYevve/f0ubvx4A9Mjpmt9yFBL91XFcAB1DRlqzVAQTQ8Zas2wwpbuW1FRAcGnAlxZewU8Dg9gwICwAfBf7t+mz4kqhwypYlhhexgy1Jr3/ln4jlD3kCF1t0UIaVENAftv4S/ZF0plZSXefPNN+R9L7foC0rzG5gfncDgIDAmEgYGB/EehttpfTjL6+vr11oNoaF8OlwPnAGeU3SmDUZDiis0NrTytp6cn/zIFpPUDJg4msLK3anZfmdpf6DI8Hq/B11F3Xzs/OxTfL4ahnmG96UMbWnFadtzim8Vw9Hdscl8ul9tgDIaGhgo/msrsa2hgiIjQCHSb1w158XnyfWu/xw29n/mJ+fAc7Vnv2C1571XxOTExMUHnkZ1x+q3TDe6bdubJ+gPKfE5kGnrv1fE5aWrflrz3bf2ccDicNu+r6fe+pZ+Tuhp7P1v6ORkWPgxxn8XJC6wHrhpYbz/ZvnW15L1XxeekNd8RtbXkvdeVz0lD76cqPifPyncEIU+7Fn2KZ8+eDQcHB1haWsLS0hIzZ86Ei4uL/LaDgwNee+01dcX6TNHmTAyO/o6tnmmoOKUYlp7qn2FIxpxvDn0TfRQmtWymodxrmp1yVPbeJe5IRGlWKcqyyxp8j+vKT9DulKO1dQjuAGGaEMUPFYdoMcZ0ZkEy8uxS52xLhBBCmtaiHoKff/5ZXXG0O5pcWbMuhwAHXN92vVWPLU4thoWH5rorORwObP1skX8zX+mpOauKq1B0v0ijCQGgOHPU9/zvIRE1/B7LVBZWoiSjRKuLktVmYCZdjyDjQgYsX3uS9BUkFaCqsAougS5ajI48y9qymjIhhJC208wSrqQebS6b7uDvgNz4XEjEkhavdyBMEWq0hwCQDht6fPOx0vvnXs+FmYsZTB3rd/eqW2BIICJXR8rnbG/qvcxPzIdZBzMY22h/Xn8Z2XoEfq/5ybelnU2DS7AL9Izo64KonipWUyaEENI2NPCtHbLtagtI0KoFvzS1KFlttn62LZqNR9MrFNcmCBeAiaV1+s0Ne8hLyNOZ3gGZhhYoo+FCRJ1UtZoyIYSQ1qNLfu0QV48Lux52yL2WK00OWqA4VbM1BIC0hyD+23il99dWQlD7SqdLkAv2j9nf5BXO/BvKD4PSlA5B0joCYZoQFm4WYBKG9HPp6LtcvdPhkvZLm72lhBBCpKiHoJ1yDHBETlzLCouZhEGYKtRoDQEg7SEoul+EmooapfbXRkJQd9iD+3B3dJ3eFdadrRstEs+7kaczBcUyBuYGcOrjhPQL0l6C3Ou5kNRI4NTHScuREUIIIURdKCFopxz8HZB7TfkFtACgLLsM4moxLNw1mxCYuZjB0NIQBXcKmt1XVCXC41uPNZ4QNDTsYcgXQ1BVVIXOUzrXG/bAGNPJHgJAcdhQ2tk0uA5yBVePvioIIYSQZxX9yrdTjgGOyI3LbdFqqMUpxTBzMau3HoC6cTicJguLI8KeXIHPT8yHvpm+vM5BEC5ARFiE2mMMDqs/BtrY1hjD/m8YMs5nIOCdAIX7hA+FEFWIYNPVRu2xtVTdhMBtONUPEEIIIc8ySgjaKbtudqgSVkH4UKj0Y7RRPyDTVGExl8eVD8vJvZYLh14O4HA48mE8LZ1JSZW6TO0Cp75OuPDBBYXteQl5sO5iDZ4BT0uRNa5DcAcIHwpRlFKEjIsZVFBMCCGEPOOoqLid0jPSg52fHXLjcpWeNag4RbNrENRm52eH1FOpDd5Xe4pCp75O6DCwg1KLgmkCh8PBiG9H4Ge/n+E7w1feuNbV4UIRYdIEyqmPE65svAI9Qz15nYMgXCAdGtVEESghhBBCnj7UQ9COOfg7tGjFYmGq5tcgkLH1s21yLYLAkEAErQ5C9pVsXPv6mk4kAzIWbhZ4bt1zOLnwpLwwWhcLioEnvS1cfS5u/HgD/KF8cLi60dtCCCGEEPWgX/d2TFZHoCxtrEEgY9fNDsUpxaguq27w/tJHpciOyQYAMBFrdlEwTSvPL4eoSgTBammtQ15CHux6SNcg0FSdgzJkc79nXs6EpEYCt2FuOtPbQgghhBD1oISgHXMIaLqHoHaxLqBYQ6DpRqyJgwmMbY1RcLv+TEN3/ryDnd12QpgurYfgGfCaXRRM03j6PJRmlOLql1fxKPoRCpMLYd/dXievvAeGBGLAxwMAAGffPUvJACGEEPKM051WCNE4h54OKMsuQ1l2WYP31y7WlYglKEkrgYWHhUYbsbKkhMPh1CssvrjiIn7u9jPOLD4D9+fdkZ+Qj+A1wXi/6n0ErwludP5/bZCvuiqS4K/n/4KBmQESdybqbGN74NqB4BpwIamR6FxvCyGEEEJUi4qK2zEDcwNYd7JGTlwOvEZ71bu/drFuVVEVmITh5i83IVgt0FgjVpaUAP/NNHRTmhAcmXkEt3+7Desu1uj2ejdc2XhFIabasde+rU2BIYGQiCQQrBEAHCByVaROJgPAfwXE1RKF3hZdjJMQQgghbUcJQTvn4O+A3LjcBhMCoH7DWpPJQN3n9xrrheqSavzS+xfkXstFx0kdMWHfBESubrhhLbtdd1EwbQpeHYzo9dE6feW9bs2A7DagG4kVIYQQQlSLEoJ2zjHAEY9iHjW5T/+V/aX1AhJopRFbNykBgD5L+2DIl0MAoMlpMHWtASsIF8iTAV288t5QAbEu9rYQQgghRHWohqCdk/UQNOX3gb8DEoBrwNVasW5gSKB8ES+eAU+eDDxNaje2dbHOAZD2pjTW2xK8JlinelsIIYQQohrUQ9DOOfg7oPhBMSqLKmFkZVTv/n1j9+FR1CP0fq83hm4eqrXhI4JwAcTVYp29st6cp+XK+9PU20IIIYQQ1aCEoJ0zsTOBOd8cufG5cBvipnDfkRlHkHI0Bd0XdMfQzUMBaKcR+yyMaW/qyrvsfkIIIYQQbaCEgEgXKLummBBkx2Yj6a8kdJnaBaO2j1LYX5ON2Kflynpz6Mo7IYQQQnQVJQSkXh2BMF2IA+MOYODagej3Yb8GH6OpRixdWSeEEEIIUS9KCNqxiDDp4mKOAY5I2psEAKgSVmH/2P0wczFDTVmNliOkK+uEEEIIIepGswy1Y7JFv9IvpKPgTgGqhFU4NOUQROUi5MTmgKtHHw9CCCGEkGcd9RC0Y7XH4uuZ6OHAiwdQkFSA8txynV1BlxBCCCGEqBYlBO1c7aQg41IGAFAyQAghhBDSjtCYEILAkEBw9aUfBW2sREwIIYQQQrSHEgICQbgAkhqJwqJfhBBCCCGkfaCEoJ2rPc//+1XvI3hNMCJCIygpIIQQQghpJ6iGoB17Vhb9IoQQQgghrUcJQTMYYwAAoVCo5UhUr6S8BL1W9oLfu34Kr8/vXT+UVZahpLzkmXzdhBBCyNNE9lssa5MQomocRp+uJmVkZIDP52s7DEIIIYS0c+np6XB1ddV2GOQZRAlBMyQSCbKysmBubg4Oh6OW5xAKheDz+UhPT4eFhYVanqO9oHOpOnQuVYPOo+rQuVQdOpeqo4lzyRhDSUkJXFxcwOVS+SdRPRoy1Awul6uxbNzCwoK+mFWEzqXq0LlUDTqPqkPnUnXoXKqOus+lpaWl2o5NCKWZhBBCCCGEtGOUEBBCCCGEENKOUUKgAwwNDbFq1SoYGhpqO5SnHp1L1aFzqRp0HlWHzqXq0LlUHTqX5FlARcWEEEIIIYS0Y9RDQAghhBBCSDtGCQEhhBBCCCHtGCUEhBBCCCGEtGOUEBBCCCGEENKOUUJACCGEEEJIO0YJASGEEEIIIe0YJQSEEEIIIYS0Y5QQEEIIIYQQ0o5RQkAIIYQQQkg7RgkBIYQQQggh7RglBIQQQgghhLRjlBAQQgghhBDSjulpOwBdJ5FIkJWVBXNzc3A4HG2HQwghhJB2hjGGkpISuLi4gMula7lE9SghaEZWVhb4fL62wyCEEEJIO5eeng5XV1dth0GeQc90QrB+/Xrs378fd+7cgbGxMYKCgvD555+jS5cuSh/D3NwcgPSP0MLCQmWxRa2PApfLRb+P+tW7L+bzGEgkEgxYMUBlz0cIIYSQp5NQKASfz5e3SQhRtWc6Ibhw4QIWL16Mvn37QiQSYeXKlRg5ciRu3boFU1NTpY4hGyZkYWGh0oTA3MQcEaERMDUyRWBIoHy7IFyA+E/jEbwmWKXPRwghhJCnGw1dJuryTCcEx48fV7i9c+dOODg4IDY2FoMGDdJSVFKyJCAiNEJ+WxAuQERoBILXBCskCYQQQgghhKjLM50Q1FVcXAwAsLGxaXSfqqoqVFVVyW8LhUK1xVM7KRCsFUBSLaFkgBBCCCGEaFS7KVWXSCR47733EBwcjG7dujW63/r162FpaSn/p+6C4h5Le+AszuKb6m8AfVAyQAghhBBCNKrdJASLFy9GYmIi/vjjjyb3W7FiBYqLi+X/0tPT1RpX7IZYXMRFpCAFN2tuQhAuUOvzqZJEItF2CIQQQgghpI3axZChJUuW4PDhw7h48WKz03UZGhrC0NBQI3EJwgWIWRODOf3moPxhOWYumKlQU6BtEWER4PK49WJ5+PAhZoycgazCLNzPuU9FToQQQgghT7FnOiFgjOHtt9/GgQMHcP78eXh6emo7JLnaBcRL3l6CrbZb0W9RPxgbGOtMUsDlcRViEYlESEhIQNGBIkQnR0MEEe7cuQMfHx+txkkIIYQQQlrvmU4IFi9ejD179uCff/6Bubk5srOzAQCWlpYwNjbWamwSsWIBsX1Pe2RcyJDfloi1Pxyn7kxI0ebRWLp0KYawIfhk8id4KfQldO3aVZshEkIIIYSQNuIwxpi2g1CXxoay/Pzzz5gzZ45SxxAKhbC0tERxcbFa1wU49c4pXLxzEVn8LPz44486NQxH1pvxD+cfXGaX8eH4D/H5P59rOyxCCCGkXdBUW4S0XzqdEJw5cwZnzpxBbm5uvQLWn376SSMxaOqPMH5PPAbMGoAqSRUuXLig9XUS6vpS70swMUM6Lx2bqzeDy2039eiEEEKIVlFCQNRNZ1t1q1evxsiRI3HmzBnk5+ejsLBQ4d+zpvPIzgiWBGP5u8vh5eWl7XAUCMIFYGIGcAC+mI9z754DAMTFxeGjjz7CoUOHtBwhIYQQQghpLZ2tIdi2bRt27tyJWbNmaTsUjTCxM8GsbrMQ9FxQszMhadK5kHNYt3YdRjuMxrjvxiH2q1jEfRMHIxsjnGAnsGHDBty/fx/jxo3TdqgNSk5OhpubG4yMjLQdCiGEEEKITtLZHoLq6moEBQVpOwyNch3sivQL6l33oCUE4QL839r/wxmcwdd5X8PW1xZTTk+BqYspBGsE8Mr3wowZMzBz5kxthyqXmZmJqVOnYufOnSgrK8Po0aPh7++PpKQkbYdGCCGEEKKTdDYhmD9/Pvbs2aPtMDSKP5iPjAsZuH79OjZs2KDtcCARS9Dz5Z54bsBz8Of6w7qjNXgGPEw9OxVcAy7sS+yxe/duTJw4Uduhyp08eRJ79+7Fd999h7t376K8vBxlZWVwdnbWdmiEEEIIITpJZ4cMVVZWYvv27Th9+jR69OgBfX19hfs3bdqkpcjUx/U5VzxMeIgFfRegpqYGL7zwAnr06KG1eILDghGMYMzYPwOCNQJw9aT5o00XG4zYOgIRoREozy+HiZ2J1mKsa8CAAQgNDQWfz0evXr1w8+ZNPHz4kIqwCCGEEEIaobMJQUJCAnr16gUASExMVLhPl6bkVCVTJ1O4dnHFC44vwNDBEDweT9shAQAe33wMu252Ctu6z+uOlKMpODn/JIK2B+HMmTOYNm2aVt6b2isq+/j4YPXq1fL7krYmSdd08Nd4WIQQQgghTwWdTQjOnTun7RC0wnWwK94zfQ/DNg3TdigQiUTg8XjIT8yHQy8Hhfs4HA5G/jAS37p/i1ddXkWluBJ+fn4KPRqCcIF0AbawYLXGWXdF5drPHxEaAf4QPn57+zf8dv83iEQinDx5UuMxEkIIIYToKp2tIagtIyMDGRkZ2g5DI/iD+ci8mKntMAAABw8ehKWlJb44+QVs/Wzr3W9sawzfyb7wEnvB285bYTpYWWOcy1P/RywwJBDBa4KxO3Q3dryxA2KxWP78wWuC4TbMDbe+uYVjx47h3LlzKC8v13iMzbl9+zaWLVuGgwcPajsUQgghhLQz2m8JNUIikWDNmjWwtLSEu7s73N3dYWVlhfDw8HqLlD1LXAe7IjcuF1XFVSgvL0dlZaXWYklMTERJSQkqiyvrDRmSGb1zND4K/Ahv5r8JvXPSDqfajfHaV+zVKTAkEAIPAeZvn49pBtMQERqBoLAgBIYEIjAkEGNXj8XLeBnb52+HoaGhVmKsq7S0FEePHgUA/PXXX9i0aRO++eYbrcRCCCGEkPZLZ1cqXrFiBXbs2IHVq1cjOFg6nOPy5csICwvDggULsG7dOo3EoY3VAX/s+CN+t/0dZ2PO4uDBg5gwYYJGnreu6upqRB+NxrFXj2Fd2TpwuA3XB4iqRPjB4weUZZeBZ8CDuFqs8YY2YwzTp0/HwT8O4l28CzvYgcPlwNjeGCYOJjB1NEVZThnyb+SDq8+FpEai1WSgoqICo0aNwuXLl/Hrr79i4MCBeP/99/H666/jxRdf1EpMhBBCdBOtVEzUTWdrCHbt2oUff/wR48ePl2/r0aMHOnTogEWLFmksIdAG10Gu4EZLO2/u3buntTgMDAxgX20PHz+fRpMBANAz1MOUs1Oww3cHyqvLYW5grvGGNofDwRuub6AXesHAwADiajEC3gtA16ldUZ5bjvKccpTllOFy4mVIaiTgGfC0lgwAgKGhIbp164aEhAR06tQJ7u7u2L9/v9biIYQQQkj7pbNDhgoKCtC1a9d627t27YqCggItRKQ5roNdMc5wHPLy8rBs2TKtxtLQDEMN+X7191iLtTjCOQJxtXQMvyYJwgW4+sVVmNqY4v2q9xG8Jhixm2KReiIV3i96o/u87qiprkESS8IZnIGoWqTxGGvjcrnYunUrrl27hn79+iEiLKLReAThAkSERWg4QkIIIYS0FzrbQ9CzZ09888032LJli8L2b775Bj179tRSVJrBH8yHaL4IFsba6xa8d+8edu7cCXaGYcJLTQ9ZEoQLcPPPmyhBCWo61iB4VnCDs/6oiyBcgLOhZ9FlVBfom+orPK8sDgCIWB2BXbxdqBHXYM7bczQaY0M4HA68vLwAPJkpqaSiBCnuKejduzf69OmjUOtACCGEEKIOOpsQbNiwAWPHjsXp06cRGChtsAkEAqSnp8sLMZ9Vlh6WMHUxRVZkFjye99BKDJGRkVi3bh26GHfBvNB5je4na7DO+t8sBCYFwsvNq15jXN0N7szHmVjFXQW/6374ev7X8u2y5007m4b08+kYvGYwXr71Mu79dQ/uI93hZu+m8aTg8uXL+PHHHzF27Fi88sor9WJdGroUUYjC7Nmz8Yb3G1ovfK4rNzcXdnZ24HJ1tnOREEIIIS2kswnB4MGDkZycjK1bt+LOnTsAgJdeegmLFi2Ci4uLlqNTP/5gPr764iuU/1WO8PBwODo6avT5vb29MX/efOT+lAs7v8aHDEnET4pzr26+ioyL0ulhZQ1YiVj9M0JV9a2CWCJGZWklHHsonqfAkEBIxBK4DXOTzjiEQOx5uAcWRRbwDfHVWIwyp06dwq5du8AYU0gIZLHOTZuLlB9TULK7BBFi3UgGKisrYWRkhNLSUgwdOhReXl7YvXs3LC0ttRoXIYQQQlRDZ2cZ0hXaquxP+DEBo94ZheyKbJw+fRrDhw/X2HPL5F7PxR/P/YG3i99WagXiB0cf4Pzy83j91usaiE7Rvbv38F3377AybiVsfeqvmVDbqUWnoGesh6FfDtVQdE9WU+aO5OLw4cPo37+/fDYh2eJoAz4egNu/38ax2cfAAQc8Ax7er3pfYzE2JCsrCz4+PpgyZQqmTJmCcePGwdraGteuXYOzs7NWYyOEkPaCZhki6qZTPQQJCQno1q0buFwuEhISmty39oq4zyL+YD56V/dGj496wNXVVSsxPL75GLZ+tkolAwCQVJKEfUn74HXOC0OGDlFrbHXZcmzhylxh1dGq2X0d/R0R80sMgqqCYGhoqP7g8KRGIBjBCA8Pl2+XDbnyHO2JH71/RE1ZDTiQnm9ZcbY2ewj27dsHoVCIO3fu4Pnnn0dERASqq6spGSCEEEKeITqVEPTq1QvZ2dlwcHBAr169wOFw0FAHBofDgVgs1kKEmmPV0QpjHcZi7Atj4dbFTaPPXVVVheLiYuQn5jc5XKiuo5eO4qjkKLz3eGs8IchPzIdNVxvw9HnN7vv2jrdxMfoi3C+64/nnn9dAdPWLnANDAnHxfxcR83kM9Iz0UJ5XDpcgFyT9mYTgNcF4nPQYUTeicCT0iMLjNW3JkiXo1auXvGagd+/eCveLxWJwOByqKSCEEEKeYjqVEKSkpMDe3l7+//aMw+GAP5iPjAsZcBui2YTgypUreO655+Bp6Ym/wv5S+nFDhg7B1V+vorNdZ/UFV8d3332HxMREBIgD4NTdSanHuHi4ANFAXGScxhICADCfZA67R3a4GHoRkWGRYBIGq45WGLl9JDIuZSByVaS8ZmB88HgcSjiE1wa9ptXZkDgcDp577jn5bdnQp8CQQCxfvhy7du3C3r17MXToUPnQp+AwmhGJEEIIeZroVELg7u4u///Dhw8RFBQEPT3FEEUiESIjIxX2fVa5DnZF4h+JsJlq0+CaDOqSmpoKDocDsxoz2Po1PR6/tpdffhm83Ty4OmhuiNOBAwdw6tQpLOq9CK9Nfk2px3z2+WcIig/CaP/Rao5O0YYNG/Drr7/iec7zGCkZCa4+F/PvzgcApF9IVyggnjRtEk5FngI/mI/gEcEaLXyWkUgk9a78y4Y+AUB+fj7y8/Nx9OhRGF02oulRCSGEkKeUzvbzDx06tMEFyIqLizF0qOaKQbXJqrcVZl+YDR8fHwiFQo0978yZM1GQU4AJ5RNaNGQIAKw7W6MwqVBNkdX3xhtv4KOPPoJLsYtSC6gB0sTTq48XcuNy1RydIgMDA5gamsKNuYGjx4GkRiJfjCw4THE2oRlvzMAG1w1YMGwBAkMCNX7V/fr16+jYsSO++uorhe2BIYEIXiNdZ2KY6TCcPXsW403H69z0qIQQQghRns4mBIyxBotZHz9+DFNTUy1EpHnufdxhxjWDmYkZ0tPTNfrclWmVcLR2hKlzy861TRcbZN3MQmVlpZoiU/Tyyy9j3Zp1ME41VjohAAAHfwfkxms2IZjnPg+hVaHwMfbBq5delTesG1qh2MDAAB2Hd0TauTSNxijz448/IiUlBQJB/dgCQwIRtDoIud/mIm5UHKJXR+tcMiCRaL5HhRBCCHla6dSQIUC61gAgHbs8Z84chVlgxGIxEhISEBQUpK3wNIrD4WDr2K3w7u8NPz8/jT53fmJ+i2YYknnvx/dwVnAWlsctMXHiRPUEV0dBUgF4RjxYuCk/Fdv18uv48dSPEP0twuTJk9UYnZRsNqG+S/vi2v9dg31Pe7gMkK6n0ViNAH8IHwnbEyAWi8HjNV8srUqff/45/P39G5zNSyKSoOh+kfT/NRLwDHg6kwxUVVXho48+Qnl5ObZv367tcAghhJCngs4lBLLFjhhjMDc3h7Gxsfw+AwMDDBgwAAsWLNBWeBojK970fd4XDw4/AD5+cp86izezs7Px3nvvweKRBV7p9krzD6jDzkV6lf7u7bvARBUHV0dGRgbKy8tRdb0Kdn524HCVT16SC5MRXRaNQwcOaSQhEIvECF4TDFtfW6SdSYO+sT6AphdwK3EpwUeCj/C5/+e4nnBd7THWZmJigtdfr7+ehKhKhCPTjyDjknQBunKUI6Y6Bud6n8O/sf9qNMaGxMbGYsuWLWCM4a233oK/vz8iIyPRo0cPmJmZaTs8QgghRCfpXELw888/AwA8PDywfPnydjM8qC5Z8WavRb2QGZEJcY0YPH2e/Eqzuoo3ExIS8Oeff8LF1AVvvvJmix//5VdfIuB0AGaNnKWG6BR9++23WL9+PSYGTMSb/i2L9eWpL+PWjlsYP2C8mqJTFB4VjoqKCsx6MAv83nyF+xq7uu7dyxsZyEBmYiaKi4u1vjJwTXkN/nn5H+Ql5KEirwLBa4Jx/fB1HI05CnaN4eDyg5j4xUStxhgUFITPP/8cPj4+8Pf3x//93/9h6dKlmD17Nn766SetxkYIIYToKp1LCGRWrVql7RC0StZIPBJ6BBd4FxA5KRLv9X9P7cWbHTt2xIYNGxC7PrbFBcUA4OrqClcfVxQmFcKpt3LTgLZWZWUljI2NYVttC7vuLYu1f//+mDd4HvgifvM7t1F1dTUuXryIyspKvCx+GU4zlTsvDg4OWDV8Ffw6+2lsZcobN25g3bp1GDVqFObOnSvfXiWswoFxByB8KERZVpn8M2jqbIpRqaPg188PN768AUdLR40PHyoqKoKlpaV8eNsHH3wgv69Xr14ApD2OIpGo3qxlhBBCpCQSyTO/xlN7w+PxlF4nSKd/Hf/++2/s3bsXaWlpqK6uVrjv2rVrWopKcwJDApFTnIN1X64D9wgXAUcCMGjNILU2uLy8vPDum+9iy4dbWlSkW5tNFxsUJNefIUrVNm3ahC+++ALbOm5rVaz2vew1Ulisr6+PuLg4CAQClC4thWMfR6Uf+8rsVxC/Nb7FtRyttePDHfjz+J8oKSmRJwQVjyuwb/Q+VBZWwpxvju7zuss/g11e6YJRb4/CzM9m4m6/u1qZHnXKlCnIyMjADz/8gOBgxZ6zwYMH4/r16+jWrZvG4yKEkKcBYwzFxcUoLy/XdihEDUxMTBQumjVGZxOCLVu24OOPP8acOXPwzz//YO7cubh//z6uXLmCxYsXazs8jZmwcQKGbxoOW2YLjj5HI1dfH996DGN7Y5jYm7Tq8cfzjuP2pdvYs2QP7Oxal1QoS1QhQnlKeasSAgtfC5z+/TTcEt3U2mDkcDjo2rUrnAyd8FPpT7Dvbq/0Y/lD+Dg+9ziqS6phYG6gthhl+nj3wSiMwgDTAQCAsuwy/PX8X5CIJSi6VwS/1/wUPoOGlobo9FIn3Nx1E4M3DFZ7fHUVFhZKE63SUri4uDS4T933trEZzAghpD2SJQMWFhYwMDCg78dnBGMM1dXV8mnrraysmtxfZxOCb7/9Ftu3b8err76KnTt34sMPP4SXlxdCQ0MbXJ/gWRW1NgovsBcADoAaaUGxupICiUSCqKgocK5xWjVcSGb/1f3IyM3ArVu3MGjQIBVGWN/jW49hbGcME4eWJy97r+7FunvrcG/9Pez+bbcaolOUczUHdt3toGek/J+dvp0+btrexKLZi/DDvh/U/kU985uZ8Hb0RkRoBM4tO4f7/96Hvqk+ChILGh2q5jfbD/+89g+Kgorg6OSIAQMGqDXG2qytrZGRkYHz58/D09MTgOJqyrVVVFRgxuAZcDB3wLYz2zQWIyGE6CqJRCJPBmjihWePgYH0QqJQKISFhUWTw4d0dh2CtLQ0+fSixsbGKCkpAQDMmjULv//+uzZD0xhZAbH/En/oGeshaHVQo/PWq8KDBw8QHByMoe8PhbWvdauPM+fVORilPwrOzs4qjE7RqVOnMHr0aGzetBl23exa1VDuHdwbZhwz8KrUO6Xn6tWrsW/fPjyMeginPi2rq+BwOPg1/1fsOLADqamp6gmwjsCQQASuCkTsplgUPyhG3vW8JutW3Ia74Xj5cUycNBGbN2/WSIy1WVpaYsKECfLbsoL8un8nX875EgeuHMBfMX/VG4JICCHtkaxmQNZwJM8e2XvbXH2IziYETk5O8p4ANzc3REVFAQBSUlLAGNNmaBpRezah4M+C8aj8EcT9xE0uZtVWubm56NChA1yMXeDQ3aHVx1n12SqMEI2Ak4n6ioqvXLmC48ePIy4+rtW1DhMnTsQPg37A8tHLVRzdExkZGQgLC8PUqVORHZsNx97K1w8AgJGRESYNnoTRjqPVvhbBzZs3ERUVherqargNcwMAMAlrdp0BLo+LCZMmwM7EDt7e3mqNsbbGvtxqr6Ys+zsRhAugv1cfI7qPwI5fd2h8XYfGSCSSdvF9RgjRbTRM6Nml7Hurs0OGhg0bhn///Rf+/v6YO3cu3n//ffz999+4evWqfPGyZ5lELJFfld2/fz82YANOvHcC8Xfi5ferWlBQEDIyMvC1y9etbmQDgJ6RHiw9LFGYVAjzDuYqjPCJl156Cfb29kjbltbqWDkcDhx6qXfFYrFYjDfffBNFRUUoPl4Mxy9blhAAwE+7f8L3/O/haNnyx7bEli1bsH37dnzwwQcIiAoAAPAMeBBXi5sdqjb5f5NR/kc5Fn2wSK0x1jZp0iRwuVx8+umn8PX1VbhPFmtEaAQiwyLBJAyD1gzCRyEfaSy+phw7dgzTpk2DUCjEnTt30KVLFwBAfn4+jh49itdee03LERJCCGlPdLaHYPv27fj4Y+lqXIsXL8ZPP/0EHx8frFmzBt99952Wo1O/4LAnQzS6du0KA64B9CTS/C0wJFAti5IBQGVhJaoeVbWphgAATL1NcfX8VRVFVV/Xrl2xYMECuDxyafGUo7U5+Ks3IXB3d8d3332H79Z+h5qymlYlL2YuZrDqaIWMixlqiPAJExMT2NnZwTbDFhmXMtB5cme8X/W+Ur1Sdj52cOjlgKS9SWqNUSY7OxtHjhzBP//8o7CauYxELEF1iXRYEJMwcHiaKchX1vPPPy9fybuqqgoAUF5ejhEjRmD27Nn49ttvtRgdIYQ8vYYMGYL33ntP22G0SGpqKjgcDuLj47UWg872EHC5XIXih2nTpmHatGlajEh7fHx8cPitw9AzVP/blX8zH6bOpjCyNmr9MfLzMfP0TOA0UL6yHEZGrT9WUyoeV6DsUVmbkpczqWfwVeRXeLTpEZYuXarC6BRlX82GfQ/7Vr+HHQZ3wOX9l+H2ghv09fVVHJ3U5s2b8Yr1K7i86jLAAUZ8OwKA4tX22rfr6janG27uugnr0dbg8/lq7YJ2cnJCYmIizp49W2+YUkVBBQ5PO4zcOGmix9XnQlIjweHph/HinhdRVlaGP/74A+Xl5Xj77bfVFmNT9PT0sHXrVmzcuBE2NjYApLVSEyZMQHZ2NoYOHaqVuAhpjyQSCXJzc+HkpN61c4jqzJkzB7t27aq3/e7du9i/f3+bfyc5HA4OHDggv3DTHuhsD4GXlxfmzp0rv3omk5+fDy8vLy1FpR0cDge2XWxRdLdIrc8zZswYvLn8Teh1alviYWtrCzNjM5jomSAjQ/VXtYuKinD8+HHEn4mHOd8chpb1rxArq0K/AqmSVFw+e1mFEUqJxWJkZ2eDMYbsq9ktLiiu7a3Db2HOzjmIi4tTYYT1MQlDl5e6wKmPk8K0s7Jx+U0NVev0Sid8GPUh3N3dcevWLbXFGBEm7a3w8fGpNwXx6SWn8aP3jyhJL0FFvnQ15aXVS9FlShfc+f0Ozi09h4sXL2L+/PkIDQ1FRUWF2uKs69y5c1i/fr28ZsDMzAwODg7yxdI4HA5Wr16NGzduwMfHR2NxEaIOT0ttTFJSEszMzODr6/vUxKxLZN/HDRGECxARFqG2537hhRfw6NEjhX+enp6wsbGBuXnjw5W1OamEOp+7rcfW2YQgNTUVEREReO6555CdnS3fLhaL8fDhQy1Gph3Wna1RmFyotuMLhUIcO3YMR6KPwNmvbbMDcTgcXN5zGZv4m9CxY0cVRfjElStXMHr0aEx/Z3qbah0AYNLLk7DIYxHeGPmGiqJ7IikpCc7OzujYsSOyr7a8oLi2rn5doQc9JN1Qz5AciUTa0A8OC4aekR48XvCot09zQ9VM7Uzh4OAAHoeH69evqyVOoPFZhP6d+i/it8bD0NoQBXcUp0kd9+c4OPVzQuzmWJgKTDF8+HCsXLlSY6tyPnr0CBMmTMDKlSuxc+fOJve1t3+yTsXNmzdp+BB56sTGxsLQ0BCOjo64d++efLtIJNL6tOE//fQTTp06Jb/t5uaGyspKlJWVobDwyW/ssWPHkJ+fr40QnyqNfR/LJkbh8tTXzDQ0NISTk5PCPx6PV2/IkIeHB8LDw/Haa6/BwsICCxcuRHV1NZYsWQJnZ2cYGRnB3d0d69evl+8PSOvUOByO/HZDbty4gWHDhsHY2Bi2trZYuHAhSktL5ffPmTMHEydOxLp16+Di4iKvF4uJiYG/vz+MjIzQp0+fBi/2JSYmYvTo0TAzM4OjoyNmzZql8JkcMmQIlixZgvfeew92dnYYNWpUG86mDicEHA4Hx48fh6urK3r37o0rV65oOyStik6PxsbkjVgVukotx9fX18dff/2FWR1nwb23e5uP59HbAyUPSyCqEqkgOkU1NTXw8/ODu5l7mxOCLl26YNzQcTDJad0ibE25f/8+uFwuHB0dkXstt0UrFNe1c/dObO2yFYF26hkHP2LECPTv3x/RUdFIPZEKzxc8W3WcL9Z+gY3OGzFtqvqG921/sB1xwXE4GnoUgnABJGIJfn/udyTvTUbX6V3h95pfg9OkTrswDabOpkg7mYbTp0/jgw8+0Ni8287OzggPD8ewYcPw6quvKvWY3NxcDB06FIsXL8Yvv/yi5ggJaZvajaCAgAD06NEDubm5CotTfvvtt/By9cLqV1Y3eAx1X1H+5ZdfMG/ePEyfPh05OTkApEP17t+/j7KyMvnwvaysLLw04SV48j1x9+5djcf5NGlsVjfZLIm6Urv1xRdfoGfPnoiLi0NISAi2bNmCf//9F3v37kVSUhJ+++03ecNf1t78+eef8ejRo0bbn2VlZRg1ahSsra1x5coV/PXXXzh9+jSWLFmisN+ZM2eQlJSEU6dO4fDhwygtLcWLL74IX19fxMbGIiwsDMuXK852WFRUhGHDhsHf3x9Xr17F8ePHkZOTgylTpijst2vXLhgYGCAiIgLbtrVxfR2mozgcDsvJyWGMMfa///2PGRsbs19//ZVlZ2czLpersTiKi4sZAFZcXKyx52zITz/9xACwQYGD1Po8Wx22skxBZpuPIxFL2GaTzSwvMU8FUTXst+DfWOKuxDYfJ3ZLLPt7zN8qiKi+srIydv3cdbbJcBMTVYnadKyTb51kZ949o6LInqisrGSGhoYMALv892W2xWoLE9eIW3UscY2YbXXcylJPpao4Sqm0tDTG5XIZAPbHO3+wjdjINnI2so3YyE6/fbrZxxfeK2RbLLew5P3JaomvOSLRk8/A5VWXWeSayAb3i1wTyS6vusw++eQTFhgYyB49eqSpEJXy3XffsQ4dOrB33nmnzcdS5jy0Z8nJySwwMJD17NmTpaeny7cfO3aM/d///R97/PixFqNj7MGDB2zChAmsU6dOrKamRv5+5ubmsri4OCaRSBhjjEkkEtbDrQcDwF7CS/Xe88g1kWwjNjb6WVCFiooK1rt3b7Z27Vp2MfRio8/16+JfmYeZB3OHO4tYHaHxOBuirrZIdXU1y8zMZNXV1QrbJRIJqyyuVPrfxY8vso3YyL40+JJtxEZ28eOLLXp8ZXGl/LOijNmzZzMej8dMTU3l/yZPnswYY2zw4MHs3Xffle/r7u7OJk6cqPD4t99+mw0bNqzR5wTADhw40GQM27dvZ9bW1qy0tFS+7ciRI4zL5bLs7Gx5nI6Ojqyqqkq+z/fff89sbW1ZRUWFfNt3333HALC4uDjGGGPh4eFs5MiRCs+Xnp7OALCkpCT56/T3928yRsYaf4/r0tmi4toFievXr4efnx8WLFig9NW1Z83QoUMx22k2xk8er7bnKM8rR3luOWx9bdt8rAcpD/Cv8b+IXhqN30+ofiE5xhge33zcphmGZMrsynA08ig63+iM7t27qyC6J0xMTKD/SB/2PezBM2jb3PduQ90QtTZKRZE9YWBggKSkJAgEAnBucuA+wh1cvdZ1HnL1uPCd6Yubu27CfUTbe5rqcnBwwL59+xAfH4+pYVPxxTdfABKAa8DF8C3Dm328lbcVRv44EsdfPw67HnZIzErE3bt38frrr6s0TtlqyXov6MHf319eJ8Dj8eS9GrKudkCxULv21bU1q9YgJCSk1YsGNbZqs+x5JGJJszOWCQQChISE4Pfff5cPZxKJRMjMzERmZqZ8P7FYjMmTJ2PcuHGYMWNGg7M/NUSZ86AqJ0+exJIlS2Bubo7Y2Fj59k8//VQ6GcLMmQgICFDZ86mCu7s7MjMzkZaWhiubriDdOh2BIYFITEzEBx98gOPHj+Po0aNKv5+qxuPxEBkZifz8fERGRkKfp9/g+xm1NgrT06Zj/KTxGNVzlHwfxxmOSPkpBXHr4tRyRfn69evo2bMnAOm6LgKBAPr6+vLPV904BeECZG/Nxu6w3SgqL0LkqkhwONJZynTxyre6VJdU42vLr1v8OEm1dPhp9LpoRK+LbtFj3y5+G4YWytcEDh06VGHWSVNT00b37dOnj8LtOXPm4Pnnn0eXLl3wwgsv4MUXX8TIkSNbFO/t27fRs2dPhecNDg6GRCJBUlISHB2lowK6d++u8B1++/Zt9OjRQ2HClcBAxc/T9evXce7cuQZ7se/fv4/OnTsDAHr37t2imJuiswkBq1PcM3PmTHh7e2PSpElaiki7PDw8MLH/RPC5fLUcPzo6GnnxeTBwNWjRH2Rjqqurce7xOZicNwFjTOUzzpRmlaJaWA2brjZtPtZPx37CzqKdcNjpgPVfrldBdIqyr2a3abiQzLGUY/gu4TvY7bfD2JfGqiAyKQ6HA3d3d7i7u2NP8B50f71tSZHQT4iQzSGIdI3E2vVrVRSllKGhISZOnIiJEyfixMIT8mRAUi1pdq0EmS6Tu+DKhiv4dMCn+CL/C5iammLy5MmwsLAAoHxDuSlcHhcHQw/iqzVfoXvP7jhx4gRsbW0bbFDUbpQ0dH/tH5LIyEh069ZNHqsycbSlsc0Yw5IlS3Dt2jWsXr0a33zzDQBg8uTJCAwMhIuLi3zf48eP4+DBg7h48SKmT5+ucIym/v6VPQ+tkZWVBS6XK589xsnJCXfv3oW/v7/CfidOnMDFixfRt29feUKQmZmJgwcP4uWXX4aTk5NKkqvmiEQibN26FZcvX8bevXvB4XBgYGCAffv24fHjxzCKNJKfp4DgAPTq1QuzZ8+Wny/nQc44En8Eq/5cVS8hU1fC4ObmhsOHD8Pc3FxaDD9Iur32+xkRFgHBagH6vtsXvjN9UVVchcKphbgUegnfhH6DalTj63e/Vnkje9u2bVi0aBF27dqFWbNmAYB89pnmPncDPh6A8vxyGJYaIiI0Ap+t/gwV4gqsXrP6mU8GAMDA3ABvFys/C1vMhhhEr4uWfx/3/7g/+n3Yr8XP2RKmpqZK1ynWTRYCAgKQkpKCY8eO4fTp05gyZQpGjBiBv//+u0UxtOa5lVFaWopx48bh888/r3efs/OTOs/WHLsxOpsQyIocawsMDMT169dx584dLUSkfdadrVGQrJ6CrKVLlyIyMhJLe6lm6k1vb2/MDJ4JR64jxGKx/AppWxUVFaFv377wsPPAK96vQN+47VNwBg4MxKW9l2Bao7o/rKKiIixcuBA9e/aE5xVPdJ/T9p6H67evIxnJOPL7EZUmBDKVhZV4FP0I4/4c1+pjRIRF4MbtG0iQJKDslzKFhECVDZLINZG48cMNuA52xbTz0xq92tcYz9GeeLTmETxNPTH81eEoLy+HhYWFyq5KB4YEQpAsAG83D9xCLmxsbBSO3ffDvshLzIN1Z2vwh/ClC6itiQQTsUYbwb/99hvmzJmDkSNH4t9//1VqteXmGj0Scf1EqqamBnp6eohaGwWJWIItW7Zgx44dCAkJke8jK+CrrW/fvvjss8/A5XIVrnyNHj0ajo6OCAsLg6dnw7UpteMUrBFAIpK0ORnYuXMnFi1ahHfffVdeLNi9e3f8888/6Natm8K+b7/9Nrp164bg4Cfv+4ULF7BkyRJ8++23uHnzpkZ6MhITE7F8+XKIRCJcvHgRgwcPBlDr6uYowEDPQP58cXFxiFwTiYhV0tuHYg9h/T/rsd9zP25n3pYnYqqMsaqqCu+99x7mzZsnj6tfP8WGX2BIIESVIkSERsjPGQDEfxuPO7/fgaGlIQytDCGEEEUogggidJyo+gkoUlNTwRhDbGysPCGoG6e4SqywgKFZBzMkbE+Qfw4NrQyRhjT8K/4XAPDxoI9VHqcu4nA4Sl8cFIQLEL0uWv43K/u86Rnq6XTyZGFhgalTp2Lq1KmYPHkyXnjhBRQUFMDGxgb6+vrNTjrh4+ODnTt3oqysTN4wj4iIAJfLlRcPN/a4X3/9FZWVlfLvyqgoxd7/gIAA7Nu3Dx4eHiprPzVHZxOCxjg6Osq7YdqbKrsqnD91Hh53PdCpUyeVHtve3h7Wxtbo0r3xD3FLGBgYYMVbKxC/NV6lH+bbt2/j3r17EOYJsWT4kuYfoISFCxfC8aQjnDqobg7qhIQE/PXXX4iOjsbSgqVw/Lrtn9nXXnsN5inmCDBU3ZAGiUSCDz74AH369EE3dIOtjy3MXVu/ujSXx4V4rxhTOk1BN9MnjS5VNEh+/fVXVFRUwD3FHTc+uwE9Yz1M+kfaY6jsWgkywauDUS2sxptfvQmfEh84OTmpfDjA0l+XopNzJ0RvjMZmg82QiCSw8bHB7T23Ebk6EnpGerD1tYWtry04PA6YSNoramRjBFGlCNGfRStcke7SpQv09PRgYWGBS6svQZ+rr1RyFRgSiJKsEoVGT++lvTHgkwGIWhulcM4uXbqERYsWYWKnibA8YCk9F8GBCg3lxjg4OOCjjxRXgn748CFOnDgBLpercKXrs88+ww8//IBXX30Va9euhTBdiNzr0nUjJKL/LgZxgPL8csR9E9eqK/OmpqaoqKhAcnKyfBuHw8H48fWHXTonOmOm00y4ubnJt9nZ2aF///7wM/ZDRFiE/DnCQsMwN38upv3fNJV/Znr16oWtW7dCIpHUG0Igo5A8hQsgqZEgMDQQgSGBePjHQ9ifs0enR50QtTZK3ji7FHoJz615TiUxhoWFYdu2bTh27BiSkpIaHBqWejIVN3fdBDgAmLQX7+2it6FnpKeQpGRfycYy/WXIrcnF+RfOw3CHIXxn+NY7XmutX78e/fv3b3Qu+Xv/3pPHKVvAcOhXQ2HmbAZTZ1OYOpvi6hdXURVahdHc0RBLxEienwz/M/6wcFOul+5Z19DfQEu/j7Vh06ZNcHZ2hr+/P7hcLv766y84OTnBysoKgHRUxpkzZxAcHAxDQ0NYW1vXO8aMGTOwatUqzJ49G2FhYcjLy8Pbb7+NWbNmNdlOnT59Oj7++GMsWLAAK1asQGpqKr744guFfRYvXiz/jvzwww9hY2ODe/fu4Y8//sCPP/6o1AWhltKphCAgIABnzpyBtbU1/P39m+xmvnbtmgYj0w3bz27Hvvh9MPvLDCtXrlTpsQ8ePIg/Bv2BbsO7Nb+zkmy62KAgSbU9Gt26dcOZM2dwbu25Ns8wVJujvyPy4vNUdjx3d3ds2LABlY8rwf6PqaQuY+jQoehQ0AGRYZEqiFAqKSkJmzZtgrGxMfZO3dvgdKMtIfviNwk1AYfLQdGDItz+7XabG02MMaxduxbJyckInRQKJ2cn9FnWR2ENCtmxm1orobahm4ei9FEpkv5Mwt39dyGpaftV6br8hvoh6YskSEQScLgcdJvbDXZ+drD1tYWFmwU4XA4E4QIwMQPPgAdxtRiCNQJErYuCQy8HpBxLkb+2Pn364MqVKxDuFyJyVaRSyVV5Xjkuf3wZt3bfkiYdYgZwgGv/dw13fr8DlyAXeIzyQERoBCQiCQSmAiQmJiI3MRf7w/Y32IMgIwgX4OGZh3Af7t7o/SKRCFvnbkVydrJCj8K9e/fw4MEDPI56jL3P78WjqEcw8zLDPuxDN71u6CTqhMSfExH9aTTs/OyQfTVbfh5qH79ukllSUiKff/yVV17BsWPHMHLkyGaH+2RcyED6+XSF5xg5ciTMo81xKfQSuMOkNTXFfYtxEidxfst5PPjuAQxqDOSfmdYMKWKMYfv27Rg3bpx8+NXChQsbeivl8hLzkBMrnSFHUiP9rAvWCHBlwxUY2RhhbYe1qLGqkSaAqyORIk7Bv7b/wtnDGZIwSZuHPQ1hQ3CEfwRfbP+iXjJwOeQyUo6noDCpEPyhfJRmlso/11e/uCp/3rqNSNntkwtP4tyec9h7by+WjV+G8RvrJ29NxVlVVYWffvoJ3bK7QU9PenW67jBjQbgAlYWVED4UIv18OlwHuaIko0QeZ8HtAnSZ3KVenMtDluPiyouIWR+DHZ12wHWY6/+zd97hUVTfH35303uvJAQICQk9QIBQpRdpCoogRVGUIigoKnwhQECqoCCIggVQmiBVFJAmkARIQu8hhJKE9J6QuvP7Y3+7ZEkPaeh9n4eH7OzMnTN3Znfvufecz6GOTx06+HaocF/+G1DkF/29Wd7v4+rGxMSEZcuWERoaipaWFt7e3vz555/qgrgrVqxg+vTpbNiwgTp16nD//v1CbRgaGnL48GE+/PBDvL29MTQ0ZOjQoaxcubLEcxsbG3PgwAEmTJiAl5cXjRs3ZunSpQwdOlS9j6OjI/7+/nz22Wf07t2b7OxsXFxc6Nu3r0bR3sqkVjkEgwcPVn/BDB48uEornb6ItGrXCv/D/hjqV75EpiRJxF+Pf66qv89iXN+YB4kPOPfPOdp1bVcpbZqYmNC9e3ciP4usVIfApqUNN365gUKhqJQPm4uLCzNmzODGrze42PIiWjqV4807dXUi4UYCmXGZGoXDKoqenh4fffQRWVlZRByIoN+mfs/dZsHZoR/df0TKLz4Mpqzk5eUxbtw49uzZw6DBgwg6H0TLSS2LPXdZGbh9IHd23SEpN4lAeSBTPi1bzGxxgz9JkhjRcQQv1X0JmzQbHh5/CBLqwUZ+Vj4N+j8trFjcwMhjhAdJt5PQMVImaOZn59NpYSfS9qSpnYH8vHz6tuzL+DnjNX5IAALmBxB1NorHZx/j1MWJFu+3IOTrELUd7Wa2w7m7M0d3HiUmOgZDQ0NliAQKetKTie9MpINvh0IrCM/aXbd73VLDaLzqe5H5c6aGY7Fw4UIaRTUi7a804m3jaTS8Eft+3MdZznLD8AY7J+/kyuIrtJzUkpz0HGIuxuDv609yeDJ9f+yrtkvVb8nJyUyfPp2zZ88SHByMoaHys9G3b1+gbLkUz15LgF8AAXMDaPJmE+Tacv4Y+QdXgq7QWNYYa8ka3VxdZHIZxnWMSXmYUqGQorlz57JgwQJ++eUXvuj2Bbq6usUO1jPjM8lOyubOzjvYtFQmd6vuZ9vP2tJ0XFOyErOU/5Ky+GvsX0j5Ev748yjhEbvW7mJ6l+kELQ8q0cZnn+3Lly9z8OBBZs2aReCCQJLPJTP60WhMzplAgRzMQ+8e4tqP1zB1MaXZu80I+Sqk0HOtorgZ5dO+p1lxdAURORHkfpmLjalNucKzhg4dysGDBxnZcSRe/l6FrjNgfgAB8wLQ0tWi0euNaD6+OeeXni+znV0WdUHHQIfTvqdZfmQ59Q7V40ncE3p881TQoCqS4WszJTk9VbkyUFI9l5MnT2q8LmowP378eMaPH19sGwMHDmTgwNLDZ5s1a8bx48eLfb84O9u3b8+lS5c0tj2bO+vm5sbu3buLbfvZ63xeapVDMHfuU439efPm1ZwhtZSZ82ZitNKIkb1Glr5zGSj4xZ8RnUFWUhaWnsok3cqY4di5fydf8iVnPznL6aDTlWIzKGccEq4nVKpDsGLPCjbf3ozWRi3Gjhtbae3GhMQ8V0GygvjP8yczN5PIupHsX7+fN/73VOu/overQYMGfPXVV8Rdi2PL5i3U6VSnUmxt9792bJ+/nTv5d+iu0/25fxh0dHT47LPP+GT6J2xsspEO8zpUSv5I4IJA8vPzWcc6EhQJfNr3U1adWFXqccUN/ua+MpcdgTvYG7iXVa1WkZ+VX+Rgo7jE2YLOVIf5HejYpiNH3j/C2S/Ocm7xOWW4z0fKcJ+Fwxdy+PJhTrxxgvAH4eoZ5r/G/cX1n6+jb6XPgG0DiA6Kxt/XH595PrSd1ZbgJcH4+/qz//J+lh9YTteuXTmRfoKv9L5CO1ebPvI+PNz6kA3HNtCgfwM8R3mWaHdZrkt1TQDN3m3GoT6HkF2V0bxvcxzaORA4P5CuU7uiiy6Ojo70/qw3JgYm+Pv6YzbejPH3x/Pnm39y/efrXN94HSToML+Dun0DAwOOHDlCVFQUR44cKRQiUlIuRYf5HWj6dlMSbiRQv199Zdz7XH+QABnEXIhBkafAppkNr7/xOj4nfQj6Kgi5jhxFroI/Zv/ByndW0s+uH73b9tZw4EoLKRo9ejTffvstQ4cORSetaHWefz79h6DlQci15XiO8qTFpBaErCw82NYx0tGYgVetOg3LGUaLJi1oSUtCvgrBrIEZh3wPsWL/Ct774j1MzpkUez9d3nGhbdu25OTkYHzHmJxNOYWcJ+8Z3uzsuZNI/0jq9a2Ho4+j2mkt6rl2fsm5xBllu0g7vjv5HX3ClEpEkkKizcw2BC8JLtTuswwbNgx/f3/emvsWxmeNNfrz6OSjXPr2EvoW+gzYMYDHZx+X+Pkryc7Dlw8T9HsQF2UXcVvjRvz1eIYdGsb5pef/MypEgn8ftcohKEiDBg0ICgrCykoz1CI5OZlWrVpx7969GrKs5pDJZFi6W5J0JwmbZjalH1AKqkHNjsAdnHlwhiZWTdA10q20GQ5PT0/0tfSR51be8tamTZswk5mRk5eDhVvhmL6KIulIZJBB8Mng53YIFAoFgYGBNGvWjOjgaJq9UzlSpnItOV/O/5IjHOGljS+pHYLKuF/3D92nbre6aOtVzleCv58/G/I38IQnuOa6llkBqDSub1TG+zZ96/lD21T91tmvMwbmBqydvZa8k3kc//A43Vd1L/HYZweY7Wa1Y9/QfWjv06aFYQvatGhDWmBaiXG1ZVlqb9C/Ae8/fJ+v9L5ShofI4OLai9z49QZubdx4td6r5NzPIWxNGMYTjdncezNLby3FysqKoIgggpcrB/8nvU7yv0X/Y53zOsbNUUqsHvA9gJGeEY6OjgT4BaDIVahnnNt80gZHH0fC/wwnyj8KubZcHYIi5Uu4DnLF3NWc0L2hOLRzoNm7zTRi2pu+0xR7b3vu7L5DbkYuBjYG6tAk1fW3+aQNLy1/SRmf///98AZvaPRDeGw4o9aMYl3wOi5cuMBKvZUochSEE07I9yHYtrTFdaArwYuDmdp1Kh0ndSyU71DQWZYUksZ1GNcxJvjLYALnB2Luao6lp6U6tEquI2dq6lS09bU12gr5KkQdjx+4IJAZvjOIJ55Q21DGtBlD8t1kDQdO5bj4z/PnyqMr6Pjo8O677wLKGcB79+5xfdV1FAqFusATgNcHXuwdvJeI0xFYeloyaNcgQn8PLTVeW/V3wQG+nq8e7fza0fT3ptzecZudvjv5O/hvQvuEMprReLzhgUl3E1555RWSkpJY7LdY3d57773HjRM3iN8UTz+/fvjM8UGSJHLScjScJ68pXvRY3UPjfhb3XBf3XeAzxwcffBjLWOKuxPFbz98ImBfAjPkziJAi+PLDL9V9GZEUwb64fYwfP55u3boBSgfL/rY9hv6G+Mx72i+q++3U2Ymhh4eiY6BDpH9khe2cu3MuKdNT6Ny5M8ZHjLny/RW+NvgaSfH8q6ECQU1Rax2C+/fvF5nhnZ2dTURERA1YVDuwcLcg6U5S6TuWAdWX1hTfKYQQQiOPRpWaJNe2bVv2jtuLgaVBZZhLVlYW48aNQ6FQ8HXjryuslV8UH374IS4XXOjaqutztxUeHk6nTp3Q19fnC9kX9FzXsxIsVN6vweGDufDzBbQjlR/d57lfKSkpREVF4eHhQfih8EpT+QhcEMi5+efo1boXeUZ56AbqPldyWWhoKFFRUbRr3Y6AeQF0+7rbc9/7Z/utbX5bJk+ezI6uO7iw+gLIoPvXpTsFkiRpDore8GL2ltn4z/dHu19hhY2Cg42yLrWfXXhWY7DebmY7pUrSuce8bfE24SnhnFt8jnOLzxFLLBFEkJaXho6+jtrpCI0IJedijnrpXNV+n5w+yiqXRcwQd/TrSI9vetB9dXcSbyeyselGpHwJmVxGfnY+l9ZdIi8zj9yMXHIzc5XX9f8x7Q/+fkBUQBQ6RjroGOqgY6SDrokyxEZSKGeuX1r+ElByyIF+N31MfzHF3NxcObDPUfbDgZwDPIp6hN1bdrRu0BpLD0vkW+XIPeRQoDnVtTQe05gj7x0hdK+y6qyUr0wgfWnFS1g1tsLCzQJtfW0CFwQStj9M3ddBy4OKjXtX9ePC3IV8veBrXm7/Mj3X9qTn2p4s1VnKnrw92GOP1Q4rDKwNuPbgGpM2TkJ3sy49e/ZUV0W9vur60/CqKV7EX4vXcJyav9ec3t8rY3Nu/3a7xEHsw+MPeXTyUakJnh82+pCMQRnUV9RHJpeRcCOBS10vsTd/LwAJTRJwe9VNGRamVReXfBcc2jsQHRTNz01+JiU8hbwn/1+F/v9D4lR1QCorhMSmuQ3vPXiPxcaLCVIEkUsukZcjubbxGvnZ+exevZvf+I3Q0FDOnz+PTCbj/KLzXFtyjabjmnJq5inuH7qvvt9yHTlvnHrqcD6PnTKZjK+++kr54lW4/vN1cnNy0dHVEc6A4IWl1jkE+/fvV/99+PBhzMzM1K/z8/M5duxYsdJ1/wXW3VrH5T8vs7fX3kKFNiqCzxwfPo77mIPfHMThjkOlLnfK5XKsPKyIPBNZ+s5lIDU1lQEDBnAn+A6uLV0rpU0VHh4e+LzkQ9rNtOduKyYmBmdnZyxMLJCHK/ugspj440Sa2jfl/OLz6tnSit6vP/74g1GjRtGndx/6nO5Dr+96Pbd9zybhAfzW8zcUuYoKOwVr1qxh9erVDPUZymCHwbgPdX9uO5+dnVcpNrxx6g22ddrGpXWXkMlkdPuqW6FjAxco5Qiduzrz8OhDJCSipCjq6tZl4DZlzGnn+Z2LPXd5rr+48By5tmb+gupZsNCx4I89f/DkyRPg6aDH6ZETs2bNok6dpyFhZQlbUr2+s/OORuJznY51ioztVr3f/N3mha4zcEEgd3bdeZo8XYZVo1dffZU+ffpweM5hDTu3NNiCUbgRzYY2w8PDg6BlQRg5GilDTCSJtp+15fA7h7m55Sba+to8PPoQt6FuuA505dpP19Q2JN1JwmO4R4l9reqD4lZ0XvJ7CT0dPXXyZOCCQALzAjnHOTzwwNLDkms/XyPrVhZNTZtinGrMre9vUW9xPXWegksvFx4ce0CgXyAWjSzUqxRaulpqZ6Dg/SwKlY11u9ctNcHT8oIlryheUfeD+zB33jz3JnoL9LDItkCeISc1OhUAWb4M5ODY3hGzBma0mNACswZm3Np2i0C/wHLdz/IS/GUwhgpDPtX5lGu51zBVmHLp20vEXIjB1taWVrGtGNdwHKkPUjk29Rj3DtxDrivn3sF71O9bH3M3c2IvxVapjYELAsnIySBJOwmHHIcqOYdAUB3UOodAFfspk8kYO1YzdENHR4d69eqxYsWKGrCsdhCXHUdkaiS3bt2qFIcAYMTqEUSuiQSFcqanMr/MLNwtuPrj1Uppy9bWln379nHgjQOVEjJVqP2WtoR8FVL6jqXQoUMHHj58yIUfLnDr51uVupIhk8nosqgL55ecV8+WVvR+RUZGoqenh4uJCybOJlg0fP4QrKIGTZ0WdmJnr514f+ZdIcUJAwMDzEzNsLlqQ+fdnZHJn19soLiBVW5uLmlj0giNCUXxtTJEp9vKp06BapBo6mLKhdUXsGtlRwAB7GMfvXJ60X5B+0r7/JR1sF5w5pwc5WDvWRucnYsuaFiWsKXy5gwUVROiLPsUx5WVV7j31T0NO4PvBavbyPPL491773J53WX85/oTMFc5yAZw7OBI1+VdcWzvyNkvzuL/TdE2qPq0pL4uy4yyqk3v173xbu6N4rKC0J2hdPTrSL219WiwugF3tt/h2pJrXF92HUkhIdeWo6WnhdsrbvTb1I8bm29oOFflGWCWx8ai+mHGFzPU+wcuCCQqIEpth76lPq2mtFK/F+gXWKH7WVaKs7OjX0cG7xlM2P4wWqxoQfL2ZDZs3wCAS08Xui7vik1zG+X9ruAzV14bm33cjMFfDq6ScwgE1UWtcwhUBcnq169PUFAQ1taVlzj6b2DmhzM58dkJ+vfvX2ltBvgFKLWideSVPotyOvw0S24uIXpRNDNnzayUNuOvxdN4VOVpVau4ln6NrRe34n3Tm0aez1+PIelKUqUlFBckcEEgSJBNNno5ehW+X59++ilTpkzh0NRDmPSteO2BghQ1IDH0MMSwnaFyNWNJ+fMclixZQm96E302GpeeLpVhZrFs3bqVCRMmYG9vzxdtviDkqxBkMmVoyaF3Dilnl/W0aDquKfnZ+ZxbdA7d9rpIZyU8+npU6mCgMgbrpVHaALI0p+TRyUc8PP6wXDHtRe1Tkq1l6QddI128P/GmxYQWfGPxDVKecnZ9pL9SgKG06ygt0bUsjmxx4XsF78nAXweSsTKD7xy/U4axaMuZkjpFnSD/vPezIjYW52QWZ4dq36rUnS+rnS0ntlSvjmnpavHa36+V6/jqsFEgeFGodQ6BivDwcPXfBau5VYS1a9eyfPlyoqOjadGiBd98802hyoovCv2G9yNsQhiGssqRHt0/Yz87v9yJncyOpRlLOb/kfKV+mWWQwR3pDqeOnXpuh0ChUCDlSSTdTqpUhSGV2tL3J7/nWN4x+u7uS6P/PdWhrqjaUkxIDC3eb1Fpdqrs+d33d3ZY7OBJ0hN2frTzue6XgYEBCScTaP5V80q1U8WqVav4+OOPGT5wOBnfZdDm4zaY1CnZ+XhW9jAjOoOra67y+rHX1dVzq0rfe+TIkXz//feMGDGCke+NZFeXXQSvDCb4q2CQwPklZwbvGczFby5ybtE5dWjUxJMT6dq1a7EynRXheQfrlWFDaYPxB8celGkg/TyD7fLEeod8FaJ2BgpObpTFqSiur8rah2XVY7/y/RWN8CuVPn9tuJ9FOZnP2lEZzlNl2AlorI6V935Xl40CwQuDVEvJz8+X/Pz8JEdHR0lLS0sKCwuTJEmSZs+eLf3www9lbmf79u2Srq6u9NNPP0nXr1+Xxo8fL5mbm0sxMTFlOj4lJUUCpJSUlApdR1WwxmaNFHUu6rnbCfALkN7kTQmQ3A3dNbYvZ7kU4Bfw3Oe4c+eO9Jb9W9L+b/Y/d1stW7aUPFw9pE8MPpEUCsVzt6dCdb1T+kyROll3kn6a9ZPG9vL0Q1pamtS6dWtp7Jix0nL95VLc9bhKt/PvWX9LcrlckiGTDnxy4LnuV2JoorRSd6WUnZZdaXYW5NChQxIgDRs2TNr32j7pyIQjpR6jup5Tc09JERER0t+T/5b2DNlTqc9lSRR8thT5CmmhfKG0nOXSCt0VkiRJ0pMnT6TJvSZL+2cU/UwH+AVIZ+aeqVIbJUmSzsw9U2xfVJcNtYlnn4/qel7KQ0k21pb7WVvsKI0X4X5XJlU1FsnJyZEiIyOlnJycSm1XUHso6z2utQ7B/PnzpQYNGki//vqrZGBgoHYItm/fLrVv377M7bRt21aaPHmy+nV+fr7k6OgoLV68uEzH1zaHICcnR5rZZKbk+5bvcw+Kz8w9Iy0ZsURq6txUGuIxROO9yvzi3z1otxT8VfBztZGdnS1pa2tLgLTKa1Wl2FUQ1Y/JL96/SCc+OVHhH5ezZ89KgGRrbSt9bfS1lJ+XX2k2Fvyh/ueff6SgTUHS+gbrJYVCUe77NWbMGKlfv37Sxo83Sjt67Kg0G58lLy9POn/+vCRJkhR/I15aqbdSSgpLKvW4AL8AaTSjJRkyqaW8pXT0g6M18mP/1+d/SaaYSl3kXaRFLJIC/AKk1157TQKk8ePHV6stguIp7vNamwaJL4KNLwr/xb4UDoGgopT1HtfakKHNmzezfv16evTowYQJE9TbW7Rowa1bt8rURk5ODiEhIcyc+TRURS6X07NnTwIDA4s8Jjs7m+zsbPXr1NTUCl5B1SBJEktvLEVxXcGERRNwcHCocFsd53WkIx3xGOaBQ1vNdioz9tGykSWJtxMrdKwqfKT97Pbcu3eP7TO2426sVJmpzPLwBZfEo4OjQSo6zKE03N3d2b17Nzf/vIntLVvkWpWXUFzwOrt06UJeuzwCP1Qm/pXHzvz8fA4cOEBSUhJdUrvQfXDJ8prPg6okPICVpxWNhjci0C+Qfhv7lXiczxwf1h9dD6fAWmHNxTUXq13fO3BBID8t+YlUUolrFIfPMB/8ff3p+W5PAuoEFNK7F9QcL0L4xotg44uC6EuBoPKptQ5BZGQkDRsW1kVXKBTk5uaWqY34+Hjy8/Oxs9NM7LSzsyvWqVi8eDHz588vv8HVhK6uLt4NvSFTmVtRGcRfjaf5u1UTQw6QY5PD34f+xvmWMx4eHuU69tmKsA2fNMTGx6ZKysOr4ngzczPR09Gr0ODTwsKCV155BZNjJmi10ao024pCW08bjzc8uL75OnU6lr3CsFwu58SJE/x95G9kc2XUX1c9Mr5ZWVnYj7Xn1MunaPtZW6w8i5djTX2YSm9FbyyxxBzzSle/Kg3V8zXNbxrjvMdhbW1NmzZt0NPRw9/Xn62+W+kytku12SMomcrSvq9KXgQbXxREXwoA3nrrLTZt2lRoe58+fTh06FCVn3/evHns3buXS5cuVfm5qoPKm76sZBo3bszp06cLbd+1axdeXl5Vdt6ZM2eSkpKi/vfo0aMqO1dF2bxkM5PtJldKPYbczFySQpOwblZ1ak4//vMjy68uZ/v27eU+1meOj7p6Z+CCQOKvxRN7KbZKysMHLghkde5q5jCH+7n3lWo+FSQmJKZKFIZUSJLEunXrWHt3LSHbQsjLyiv1GP95yj6UyWS0aNGC11q9hpGFEdZNrZUD4Hn+pbZRUQIDA6lfvz5vT3ubxmMbKwt5FUPo3lA2t9xM3pM8HHDAWNdYnTBYXRScgezbt69a4lf1PGrJqtbZEwgEAkHp9O3bl8ePH2v827ZtW02b9UJSax0CX19fPvjgA5YuXYpCoWD37t2MHz+eL774Al9f3zK1YW1tjZaWFjExMRrbY2JisLe3L/IYPT09TE1NNf7VNizcLUi8k4gkSc/VTnJyMk51nfhe63v0bPQqybrCtOnQBgccMDGsmLSlzxwfYgbEsMJ3BY/uPeL6xutV4gz4+/pT16MuANn1s9VOSFnwn+dPgF8Av/zyC0Hng4i5GINdG7unbVfyYFsmk7FmzRr2Hd1HhFEEYQfCSj1Gtdqiuqb7h+9Tr289tTJOZYY3PYuHhwfp6elE348mSZHEvT/uEXspVmMf/7n+/Nr2V9a8uQaLLhbEhMTQ0a8j07KnaTiF1UHHecU/Xz5zfKpM5UggEAgEZUdPTw97e3uNfxYWFpw8eRJdXV2NieVly5Zha2urHhMeOnSITp06YW5ujpWVFQMGDCAsTPO3NCIighEjRmBpaYmRkRFt2rTh3LlzbNy4kfnz53P58mVkMhkymYyNGzdW56VXOrU2ZGjw4MEcOHAAPz8/jIyM8PX1pVWrVhw4cIBevcpWUVVXV5fWrVtz7NgxdcEzhULBsWPH+OCDD6rQ+qrF3NWc3PRc0h+nY+JYcf340NBQYhNiydHNQVdXtxIt1OTjmR+jt1yP13u+XuE2tl/aTgQR2GGHha5FlTgDHf06MnTUUJKCkgj+NJim85uWWe5PriXnoO9BvuALtLW1WaK3BEt3yyoJbVIxYcIEkpOTcY935/rm6zR6reTaCT5zfMjOzWa272wGhw3GOMQYm8Y2VbLa8iwWFhacOHGCzAOZnPc7j6OPI2fmnOHVA68CcPzD41xYfQFtB202SZtYv2893038Tuh7CwQCQQ2RkZEBgKGhITKZsiBkTk4Oubm5aGtro6enV2hfAwMD5HLl5FJubi45OTloaWlpSMcXtW9l89JLL/HRRx8xevRoLl++zL1795gzZw47d+5Uh5FnZGQwffp0mjdvTnp6Or6+vrzyyitcunQJuVxOeno6Xbt2pU6dOuzfvx97e3suXLiAQqFg+PDhXLt2jUOHDnH06FEAzMzMquRaqo3qyHCuSbZv3y7p6elJGzdulG7cuCG99957krm5uRQdHV2m42ubypAkSdLFixcle217qalb0+dqJzMzU/r2jW+lpYOXVpJlxfNru1+lG1tvVOjY/Px86c2Ob0ruuEsLZAsqXUXiWZm9nMwc6SuDr6S4a3HlUu/Z8sEWyRVXqbFVY2lbl23VpniRdC9JWqGzQkqPSS913+PHj0uAZIKJtIxlNaLIoeqXFTorpMjASGnPK3uk5SyXtnbcKl27fE1ytXWVnK2ci1REqE2yhwKBQFBd1ITKECABUmxsrHrbwoULJUB69913NfY1NDSUACk8PFy97auvvpIAaeTIkRr7WltbS4B07dq157J97NixkpaWlmRkZKTx74svvpAkSalO2LJlS+n111+XGjduXKoyXFxcnARIV69elSRJkr7//nvJxMRESkhIKHL/uXPnSi1atHiua6gOXniVIRU5OTnExsaqKxirqFu3bpmOHz58OHFxcfj6+hIdHU3Lli05dOhQoUTjFwlzc3Oi86JJCE8gPz8fLa2KxTMbGBhgFWtFxxFVH/5g0ciCpDtJFTr23BfnaOnfkpfcXsJrshc5qTmVOlP8bPiHjoEOdbvX5d6f98rV/shvRlLftj7+vv5E+kcScSqiWpRxzOub4+jjyO3tt2k1tVXJ+5qbM2bMGO5vvo8MWbUn64Lynt2Pu0/kN5Fs9dkKQOPRjem/WVl9+87jO0RERKCjo1PksQKBQCAQAHTr1o1169ZpbLO0tASUUSJbtmyhefPmuLi48NVXX2nsFxoaiq+vL+fOnSM+Pl49znz48CFNmzbl0qVLeHl5qdv7t1NrHYLQ0FDGjRtHQECAxnZJkpDJZOTn55e5rQ8++OCFDhF6FmdnZxYNWER9x/rPtdwmSRJxV+LovKhzJVpXNJtCN/HP7//wQ+sfGDBgQJmPU4XcdJjXgeCVwTh1ccLOS+nMVVX4yP79+/kh9gfcN7rTdkb5Klq3n90e/7n+6kqkVT2Azc3NJSQkBNO+plzffL1Uh8DLy4th2cO4zW3kunKN6p7VQXZ2Nj179sTf359PdT7FOtcaua5c7QyAUgWprA6/QCAQCKqG9PR0QBkypGLGjBl89NFHaGtrDh9jY5U5YQYGBuptkydPZvz48YUmLe/fv19o34piZGRUpCKlCtUYMjExkcTERIyMjNTvDRw4EBcXFzZs2ICjoyMKhYKmTZuSk5NTafa9SNTapOK33noLuVzOH3/8QUhICBcuXODChQtcvHiRCxcu1LR5NYqWlha9e/ZGP0ZfHddXEX745gfOxZ9Dq07VKaaolG0y5BnEZMRw48YN9XtlSbZV5Cto8lkT6r1cDwCb5jbAU7WXqtCbPnfuHH8F/UXAzQCykssu7ZqVlcWR946ABFq6WtWijDNx4kR8fHz4J+EfEq4nEH89vsT9T35ykts7btNoeCOmZ0+v9mRdPT09LC0t0ZZr8yD3AVq6WihyFNiZ2TFnzpxCK4ECgUAgqBmMjIwwMjLSGGfo6upiZGSkkT9QcN+Ck5Q6OjoYGRlp5A8Ut29VEBYWxrRp09iwYQPt2rVj7Nix6t+YhIQEbt++zezZs+nRoweenp4kJWlGMTRv3pxLly6RmFh0HSVdXd1yTU7XdmrtCsGlS5cICQkpt279fwULdwsuf3/5udr4bM5nJJHExwkfY+tkW0mWaaJStun/Wn/qG9TnnXfeAShzsm3HeR3p2LEjIStD+KTlJxpKOFU1qz148GB0dXXJ3ZzL/SP38Xi99GcwKiqK+i71qZ9Xn5ltZjI2aKz6GqvS1g4dOrB371509HVoOKQhN365QZclRevj7/t4HxdWXsC1tSsDtw/UsKs6k3VHNRhFi/wW9PPrp679MMN3BgsXLiQlJYXVq1dXuQ0CgUAgePHJzs4mOjpaY5u2tjYWFhaMGjWKPn368Pbbb9O3b1+aNWvGihUrmDFjBhYWFlhZWbF+/XocHBx4+PAhn3/+uUY7I0aMYNGiRQwZMoTFixfj4ODAxYsXcXR0xMfHh3r16hEeHs6lS5dwcnLCxMSkkKP0QlEtGQ0VoE2bNtLp06dr2oxamVQsSZIUcjREGiYfJu3atatCx+fk5Eh9W/WV3CzcpLS0tEq2ThNVEulylktpkWnlSrZVKBSSvb29BEhbPtpSpXY+yz+f/SMdHHOwTPvOGzZPAqS6srpS1Pko9faqTizOycmR8vPzJUmSpLA/w6R1ddZJ+Xn5Re7bt05fCZB8/+db6L3qStYtrj82jN8gTWGKdGLOiSq3QSAQCF40aiKpuLYzduxYdeJzwX+NGjWS5s+fLzk4OEjx8fHq/X///XdJV1dXunTpkiRJkvT3339Lnp6ekp6entS8eXPp5MmTEiDt2bNHfcz9+/eloUOHSqamppKhoaHUpk0b6dy5c5IkSVJWVpY0dOhQydzcXAKkn3/+uTovv8y88EnFS5cu5dNPP2XRokU0a9asUIJhbawPUJ2EhIWwS7GLpFVJDB06tNzH6+joMKXpFMwGmGFsbFwFFj6l4Cz0+nrrUeQWXXa+KGQyGY8ePcLP1o+Or1av9nuDlxuwb+g+JIWETF5yaFaPxj2w6mXF49DH2Ld5WuNCdY1VEdoEaHwu6vWqhyJPwaMTj3Dp6aKx3+Nzj3kc/RiA5l6Fq1JXVw5BwYJfBXl3/bsEOgdWWT8JBAKB4N/Fxo0bS9T+f7Zm1auvvkp2drb6dc+ePTXCmIFC9Z1cXFzYtWtXke3r6ekV+96LSK11CHr27AlAjx49NLZLFUgq/jfi1cqL5kbN8XKteNXm+KvxNBxUfDJOZeIzx4ef5/5MTG4MbXTalGsAmhKagmWWJU7tnKrQQk1yc3OJ1I7kduZtooOjcWjrUOL+neZ3IqpHFO0mtyuU11Fdg+3zwefR663H9c3XNRyC7NRs/hj5BxuWbKDe2HoaSVXVTUkFvYSCkEAgEAgENUOtdQhOnDhR0ybUatq0acO87vNw8XIpdd+iyEjLIOFGAtbNrCvXsGIIXBDIXmkvccRhl2tXLmWbiFMROLR3QEu36pKfn2XXrl2MHDmShhYNGX5weKkOQcqDFCJOR/DylperyUJNvvvuOyZOnEjblm154/Yb9Py2J7rGymJzxz44hnkDc9pMb1PqSodAIBAIBIL/HrXWIejatWtNm1DrsXC3ICm0Ytr+fXv15VrONZqEN6G3e+9KtkwTVXJtl9ZduBNyh5YjWpY5iXXZsmWc23iOIS8NqVIbn6VTp05YWFhQt0Fd7v5xl47zi5/ZXrduHad+OYW3jzdG9jUz+z5gwACmfzgdG5kNRq5GhO4OpcmYJlz75Rrf7PqGd996lwC/gBJn6AUCgUAgEPw3qbUOwZUrV4rcLpPJ0NfXp27dui92NnclYOFuwe1dt8nOzi5XX0iSxJVrV0iVUnFwLHnm+3kpqCb0yZxP2P7Sdhp1akSWZ1aZnILNmzdz/eZ1Bo4eWKV2PouzszPx8fFkJWSxzmEd6Y/TMXYoOtfi559/JigoiBaTW1SrjQVxcnJi17Rd3Fh6A8vellzffJ06neowZ/wcDmUf4uy6s+z13Vtj9gkEAoFAIKi91FqHoGXLliVq7Ovo6DB8+HC+//77Qhq3/xXWHV/HT0d/4ovVXzBjxowyHyeTydg5cSeXrl/C09OzCi0snETqNsSNu3vv8tqR19Tvl8T7o99nx8wddH+9e5XaWRRyuRxDG0Psve0JPxROs7ebFbnfuJfHoX1Fmzc/ebOaLdSk/5L+WBhZ4O/rj0wuY98r++jg3oHzV88zatAous4Xq24CgUAgKMyzybSCfw9lvbe1tjDZnj17cHNzY/369Vy6dIlLly6xfv16GjVqxNatW/nxxx85fvw4s2fPrmlTawxzB3NypVxu37pd7mMzbmfQo3ePQtUGK5uO8zQVZVwHu3L/xH2ykrOUxcVKCWHp7tCdSe0nUde15irX1u1Tl3sH7xX7vku4CwvfX4hzPedqtKpofOb40OzzZuxT7OPOlTvIrsrY5ruNJXuX1LRpAoFAIKhlqKoIq6rzCv59qO7tsxWjn6XWrhB88cUXrFq1ij59+qi3NWvWDCcnJ+bMmcP58+cxMjLi448/5ssvv6xBS2uOKZ9MQfs7bT6Y+kG5j427Gkfrj1pXgVXFk5OTg09/H0LzQun0Wyd83is9qTjiVAROXapPXaggsbGxDBkyhFs3bjEnfw75OfmFEptz0nK4vfM2I/1H1oiNRbHu5jqCCUYLLQbpDqLL/KILlQkEAoHgv41cLsfQ0JDU1FRAWX23pOgMwYuDJEnk5OSQmpqKoaFhqZWha61DcPXqVVxcCivouLi4cPXqVUAZVvT48ePqNq3WUKdOHdw93Em7l4Z9C/vSD/h/PvnoE27dv0Vv+6pNJn4WXV1dMjIyyCefU9tOleoQ3LlzhyvHrvDqmleryUJNrK2tuXHjBikpKcRaxxJ5JpK63Z+uVCgUCpZPW45xfWNsW1ZNpeeK8IrjKxzmMI5ajuTn5JdL0UkgEAgE/y3MzMwA1E6B4N+FoaGh+h6XRK11CDw8PFiyZAnr169HV1cpn5ibm8uSJUvw8PAAIDIyEjs7u5o0s8axcLcg8U5imffPyspi1ZpV5JHHWqO1VWhZ0ezduxd5nJyTr54kLysPbf3iH8EpE6dw5P4RDO8aMpnJ1WilErlczq5du6hfvz6hi0IJOxim4RBcvHiR2T/OxkjPiMl5k6s8/KosBC4IJG5dHCveXkHzcc3ROqFVZkUngUAgEPz3kMlkmJubY2pq+p+v8fRvQ0tLq9SVARU1P4IphrVr1zJo0CCcnJxo3lxZWfXq1avk5+fzxx9/AHDv3j0mTZpUk2bWODHGMfz8y8/0tejLe++9V+r+ubm5TBo4icvnLlO3bvXH5bdq1QpJkjhvc54Hxx7g+rJrsfumxaUhR06L1jWn3qMqkKcYoOD0rNN0W9FN/V707WicZc606tmq1jgDKkUn9eC/k/I/4RQIBAKBoCTkcnmZB4+Cfx81P4ophg4dOhAeHs6WLVu4c+cOAK+99hojR47ExMQEgNGjR9ekibWCSCLZc30P8b/Gl8khMDExYaDjQF4Z+UqNxQnKZDIaDmnI3b13S3QI/Lr4kdc5j/bt21ejdUXj0tOFA8MPkHwvGfMG5gCY3TTjmyHfMGDngJo17v95VtFJhep1aYpOAoFAIBAI/pvUWocAlIPXCRMm1LQZtZq+A/pydNtRPvzwwzIfE3cljubjm1ehVSVz7Ngxvrr8FY4XHOn1XS/kWkXPSESciqDD3A41Pvu+Z88eDhw4QK5LLvcO3qPVlFYo8hVc33Sdnmt7lpq5X12UpNgkVgYEAoFAIBAUR612CABu3LjBw4cPC0liDRo0qIYsql0079qcwU8GM6Bn2Wapz58/T9SVKLo3q35dfxUnTpzg4MmDNNVpyuOzj6nTsU6hfZ4kPiH+Wjx1Ohd+r7rZtm0bO3fupFfLXmqH4NqBa+Rm51Kvb72aNk8gEAgEAoHguai1DsG9e/d45ZVXuHr1KjKZTF1YQRXmIhJflBhYGmBgZUBSaBL2bUpWGkpKSqJdu3bIkfOW41vVY2ARjBo1iqysLFzuuBC6N7RIh+Cj8R8RbBRMyyst1XH8NcXGjRtxSHWgqVVTHv3+iJyMHKZ8NIVTsacw32GOa7irMlynlJoKAoFAIBAIBLWRWps98uGHH1K/fn1iY2MxNDTk+vXrnDp1ijZt2nDy5MmaNq/G8Z/nT+CCQECpNHTN/xqHDx8G/j+5dJ5/oWMePHiAjYUN1jrWWNlZVau9BfHw8ODLL7+k3zv9uLvnbqEqesnJyWzev5kL6RfQ0dGpISufYmhoyBsd3yBlawo6Rjrc3XMXrQhlmFDi0UT8ff2LDXsSCAQCgUAgqO3U2hWCwMBAjh8/jrW1tTrzvVOnTixevJipU6dy8eLFmjaxRpFrydXKMRl2GfT6qBcGBgYcmnGI837n6ehXeLa6ZcuW7P9kP+HB4dVtbpG49HIhPSqdhBsJWDexVm83NzdnYeOFRHhE0LVr1xq08CmqGHx/X3+OvH+EVg1a0aFpB7I3ZdPJr5OI0RcIBAKBQPDCUmunNfPz89VqQtbW1kRFRQHKwmS3b9+uSdNqBT5zfOjo1xF/X39MU02xNLCkgVUDDvsdLlJpRkX81Xjqt65fzdYWzZ3wOwQ4BxC4OVBje056DlyH+V/OryHLisZnjg+NxzQmLzMPh3AHzPeYC2dAIBAIBALBC0+tXSFo2rQply9fpn79+rRr145ly5ahq6vL+vXradCgQU2bVysoOGv9KZ+iFaFVojMAEHc1Ds+RntVlYom89957BNwJQGebDgOXDgQgPT2d+MB4TJxNMHU2rWELC9N7fW9u/HoDKU9CS1dLOAMCgUAgEAheeGrtCsHs2bNRKJS66fPnzyc8PJzOnTvz559/smrVqhq2rvbgM8cHua4cLbSQ68iLHaA+evSIVq1a8eONH7FqWnP5AwUZO3Ys/fv0xzjSmNRHqVy7dg1HR0fmzJtDnU41ry5UFEHLgkABWrpa5Ofkq/M4BAKBQCAQCF5Uau0KQZ8+fdR/u7m5cevWLRITE7GwsKixglq1kcAFgShyFMi0ZChyFZz8/CRdFnUpVG3w/PnzXLx4kTryOpjVM6shazV57733eO+999jRbQd3993lt4jfSEtLI/ROKM5vO9e0eYV4thKw6jUInX+BQCAQCAQvLrXOIRg3blyZ9vvpp5+q2JLaT8EBavvZ7eli0YW5S+eyKnEV765/V2PfLl26sPKDlTz4+0Gtc6jcXnHj7t67LP57Me2923NuxDmcujjVtFkaPOsMgGbIVsHXAoFAIBAIBC8Stc4h2LhxIy4uLnh5eRWSoxQ8pagBqnkHczL/ymTvhr00cW6iMUC1sbGhlUErmnZrWlMmF4ttD1tWT1vNwKSBtLFtQ5RlFBZuFjVtlgaKfEWR+Rmq14p8RU2YJRAIBAKBQPDc1DqHYOLEiWzbto3w8HDefvttRo0ahaWlZU2bVesoaoA6Z94c3h/2Pjfev0FyeHKhY+KuxOE6yLUarSyd7OxsnJsqw4MGfzcYB8kBpy5OtW4Vo6SiY2JlQCAQCAQCwYtMrUsqXrt2LY8fP+bTTz/lwIEDODs78/rrr3P48GGxYlCAjvMKz1a3bduWAeMG0HNlTx4ceUBmXCYA4eHhrF+/nmsXr2HT3KYmzC2W4MXBdPbojJGeEVf+ukLEqQh1uFBxBdYEAoFAIBAIBJVHrXMIAPT09BgxYgR///03N27coEmTJkyaNIl69eqRnp5e0+bVerw+8MLRx5E/R/+JpJBYuHAh77//Pr/E/oJ1U+vSG6hG5Fpyut7qyug2o9EP0SfSPxLnLs7qkChRAVggEAgEAoGgaqn1oy25XI5MJkOSJPLz82vanFrP1atX6d+/P7da3uLxucfs7L2T6dOnY2NpQz+bfuib6wO1Z/bdZ44P/f364+rviqGeIXIdOaF7QwvlRwgEAoFAIBAIqoZa6RBkZ2ezbds2evXqhbu7O1evXmXNmjU8fPgQY2PjmjavVvPnn39y6NAhTgWewuMNDx4ee8ijlY/4Y/YfdG/bHaDWzb6rqi5nJ2eTnZpNwNwA4QwIBAKBQCAQVBO1Lql40qRJbN++HWdnZ8aNG8e2bduwtq5dYS61mffee4/r16/TunVren3Yi7SINK79dA2LRha4v+pepDpRbcBnjg+BC5U1FUQFYIFAIBAIBILqQybVskxduVxO3bp18fLyKlFpZvfu3dViT2pqKmZmZqSkpGBqalot56xMJEliY/ONJFxLQK4tR5FXtHxmTaNyVFQVgGujjQKBQCAQ1AQv+lhEUPupdSsEY8aMqXWSky8yMpmMkf4j+cbiGxR5tXP2XVQAFggEAoFAIKg5ap1DsHHjxpo24V/HhVUXQIF69j1wQWCtGWiLCsACgUAgEAgENUutcwgElUttn30XFYAFAoFAIBAIahbhEPyLeRFm30UFYIFAIBAIBIKaRTgEpaDKuU5NTa1hS8pPWmYaLWe1pMmHTTTsb/JhEzKyMkjLTHshr0sgEAgEgv8Sqt/qWqYDI/gXUetUhmobERERODs717QZAoFAIBAI/uM8evQIJyenmjZD8C9EOASloFAoiIqKwsTEpMrUj1JTU3F2dubRo0dCTuw5EX1ZeYi+rBxEP1Yeoi8rD9GXlUd19KUkSaSlpeHo6IhcXjuKigr+XYiQoVKQy+XV5o2bmpqKL+ZKQvRl5SH6snIQ/Vh5iL6sPERfVh5V3ZdmZmZV1rZAINxMgUAgEAgEAoHgP0yVrxBkZGSwZMkSjh07RmxsLAqFpozkvXv3qtoEgUAgEAgEAoFAUAxV7hC8++67/PPPP4wePRoHBwdRhbgI9PT0mDt3Lnp6ejVtyguP6MvKQ/Rl5SD6sfIQfVl5iL6sPERfCv4NVHlSsbm5OQcPHqRjx+L15gUCgUAgEAgEAkHNUOU5BBYWFlhaWlb1aQQCgUAgEAgEAkEFqHKHYMGCBfj6+pKZmVnVpxIIBAKBQCAQCATlpMpDhry8vAgLC0OSJOrVq4eOjo7G+xcuXKjK0wsEAoFAIBAIBIISqPKk4iFDhlT1KQQCgUAgEAgEAkEFEZWKBQKBQCAQCASC/zDVVqk4JCSEmzdvAtCkSRO8vLyq69QCgUAgEAgEAoGgGKrcIYiNjeWNN97g5MmTmJubA5CcnEy3bt3Yvn07NjY2VW2CQCAQCAQCgUAgKIYqVxmaMmUKaWlpXL9+ncTERBITE7l27RqpqalMnTq1qk8vEAgEAoFAIBAISqDKcwjMzMw4evQo3t7eGtvPnz9P7969SU5OrsrTCwQCgUAgEAgEghKo8hUChUJRSGoUQEdHB4VCUdWnFwgEAoFAIBAIBCVQ5Q5B9+7d+fDDD4mKilJvi4yMZNq0afTo0aOqTy8QCAQCgUAgEAhKoMpDhh49esSgQYO4fv06zs7O6m1NmzZl//79ODk5VeXpBQKBQCAQCAQCQQlUSx0CSZI4evQot27dAsDT05OePXtW9WkFAoFAIBAIBAJBKYjCZKWgUCiIiorCxMQEmUxW0+YIBAKBQCD4jyFJEmlpaTg6OiKXV3m0t+A/SJXUIVi9ejXvvfce+vr6rF69usR9a7v0aFRUlDrUSSAQCAQCgaCmePTokQi1FlQJVbJCUL9+fYKDg7GysqJ+/frFn1wm4969e5V9+kolJSUFc3NzHj16hKmpaU2bIxAUy9nFZ5HL5bT9rG2h984vPY9CoaD9zPY1YJlAIBAInofU1FScnZ1JTk7GzMysps0R/AupkhWC8PDwIv9+EVGFCZmamgqHQFCrMTE0wd/XHyN9I3zm+Ki3By4I5NKiS3T06yieYYFAIHiBEaHLgqqiygPR/Pz8yMzMLLT9yZMn+Pn5VfXpBYL/DD5zfOjo1xF/X38CFwQCSmfA39efjn4dNZwEgUAgEAgEAhVV7hDMnz+f9PT0QtszMzOZP39+udtbu3Yt9erVQ19fn3bt2nH+/Pli992wYQOdO3fGwsICCwsLevbsWeL+1Yn/vKeDtmcJXBCI/zz/arZI8G+goFOwUmelcAYEAoFAIBCUSpU7BJIkFbnEdfnyZSwtLcvV1o4dO5g+fTpz587lwoULtGjRgj59+hAbG1vk/idPnmTEiBGcOHGCwMBAnJ2d6d27N5GRkRW6lspEriXXmMlVoZrRlWsJFQFBxfCZ4wMyUOQp0NLVEs6AQCAQCASCEqky2VELCwtkMhkpKSmYmppqOAX5+fmkp6czYcIE1q5dW+Y227Vrh7e3N2vWrAGUkqDOzs5MmTKFzz//vNTj8/PzsbCwYM2aNYwZM6ZM50xNTcXMzEx9HZXJs+EcIrxDUBkE+AUQMDcAZIBErXyeTp06hZubGw4ODjVtikAgENR6qnIsIhBAFSUVA3z99ddIksS4ceOYP3++Rla8rq4u9erVw8en7IOUnJwcQkJCmDlzpnqbXC6nZ8+eBAYWHXrzLJmZmeTm5pa4MpGdnU12drb6dWpqapltLC+qQZq/rz+BCwNR5Chq5eBN8OKwauwqrm6+ihtu2Hra4vGGB/6+yvCz2vJcnT9/nq5du2JlZUV8fHxNmyP4D+A/T7nqWtRnIHBBIIp8BR3ndawBywQCgaB2UGUOwdixYwGlBGmHDh3Q0dF5rvbi4+PJz8/Hzs5OY7udnZ26AnJpfPbZZzg6OpZYJXnx4sUVym2oKA3ea8Ac3zlk5GTwuu7rtWbQ9l8lNDSU8+fPM3z4cLS1q+zjUWk8ePCArVu30rx5cywvWLJi8woe8YihukPpEtFFw+mE2uEUqEQGEhISyMnJQVdXt4YtEvzbUYVoAoUUuFSrsgKBQPBfpspHPF27dlX/nZWVRU5Ojsb71bX0tWTJErZv387JkyfR19cvdr+ZM2cyffp09WuV9m9VEbQiiGMcQ4aMgTkDCVwQWCsGbf9Vxo4dS1RUFN26dcPR0bGmzSmV8+fPM2vWLNq3b8+SXkto164dGdcyGPjSQOIOxpGdmq1+nhT5ihq2VslLL72EQqEQ8nmCaqMox1iEaAoEAsFTqtwhyMzM5NNPP+W3334jISGh0Pv5+fllasfa2hotLS1iYmI0tsfExGBvb1/isV9++SVLlizh6NGjNG/evMR99fT00NPTK5NNz0vggkBuLb9Ff+f+uDi40K5Xu1o1k/tfQ6FQqMPPMjIyatiasuHg4MCoUaNwc3Ojq29Xuvp1ZXv37XgO9OTE8ROkR6ajZ6pX654n4Qz8O3iRQnEKOgVnF54lPydfOAMCgUDw/1S5lM2MGTM4fvw469atQ09Pjx9++IH58+fj6OjI5s2by9yOrq4urVu35tixY+ptCoWCY8eOlZiLsGzZMhYsWMChQ4do06bNc11LZVJwdmrFshV0lHWkx8IehXTkBdWHXC4nNjaWkydP0qBBAwD279/PokWLUChqx+z6s3Tq1IlffvkFX19fQKnqFXchDrs2dug66hJ/r/bF6CclJan/vn79OqGhoTVozb+bqpY3ftHU0nzm+CCTy8jPyRcKXAKBQFCAKv+2PnDgAN9++y1Dhw5FW1ubzp07M3v2bBYtWsSWLVvK1db06dPZsGEDmzZt4ubNm0ycOJGMjAzefvttAMaMGaORdLx06VLmzJnDTz/9RL169YiOjiY6OrrIugjVjSL/aQKxhbsFSaHKQZJKR762hHf817CxsaFr165oaWkRGxvLmDFj+N///sePP/5Y06aVieSwZPKe5DF25lgmh01m/x/7a9okDZ48eYKVlRXW1tZMnz6dpk2bsmTJkpo2619LVQ/YX7RieIELApEUktopEBMvAoFAoKTKQ4YSExPVs62mpqYkJiYCypnNiRMnlqut4cOHExcXh6+vL9HR0bRs2ZJDhw6pE40fPnyIXP70B27dunXk5OQwbNgwjXbmzp3LvHnznuOqnp+Cy+gWbhbEJ8bzz+F/6Nqna637Ef2vYmtry8qVK9m6dStvvfVWTZtTJJmZmRgaGqpfx4TEYN3MGnMLcwCiHkbVkGVF8/snvyNJEgqFgsGDB7NmzRpyc3OB2hdi8m+gOmLnC54jwC8AKU+qtc6Av68/2gbaOPo44vySswjRFAgEgv+nyh2CBg0aEB4eTt26dfHw8OC3336jbdu2HDhwAHNz83K398EHH/DBBx8U+d7Jkyc1Xt+/f7/8BtcAD6IfMJ/5LBuyjLT0NLS0tGrapP8kP/30E6mpqQwaNEjtxI4bN4633367Vsa8q3SpHR0duXv3LgYGBkQHR2Pfxp61C9cyzmkcsvTaZbervStf8AWuo13p3Lkzjx8/xsrKSqi9VCHVIW+scjQUuQrkukXnFNQkquer3ax2nFt0jpT7Kbw+53WgdilwCQQCQU1R5SFDb7/9NpcvXwbg888/Z+3atejr6zNt2jRmzJhR1ad/IWjQoAEGWgbYmdsVW3VZUPWsXbuWadOmceXKFY3ttdEZAAgLCwOUifkGBgaAcoXArrUd1tbW2LrakhaRVpMmFsJnjg/d/LrxaPUjzn1xrpAz8F8elGVlZXHx4sUqadtrihcAipzKrV69adMmpk2bhv98fxS5CvU5yhOKU9V5DvA0RNPtVTeQQdqjNCSFJEI0BQKB4P+p8hWCadOmqf/u2bMnt27dIiQkhIYNG5aq+PNfQUtLix2jdmDhZCEqt9YAKqWUoUOH4uLiQosWLdTvBS4IJDY5llCHUDIzM9XJu7UBLy8vEhISiI6OBkBSSMSExPDSipcAMHEyIT2y5vNlnqXgjLX/XH+QoOWsljSf/t/+Pti5cyfTp09n6dKljBs3rlLbPvXZKQBk2k9j5yvDKTh79izfffcdeeTh3cqb2AuxeI7yLNese3XUCFCFod3acQvbFrbEXool/XE6JnVM/tNOqEAgEKiodgkIFxcXXn31VeEMPINdYzsS7yTWtBn/SVQDkm753di9ezf169cHng5IMnIymDFjBsuXL0eSpBq2VhNLS0saN24MKBOK87PysW5iTVRUFN//+T077uyoYQs1mTNnDn5+fji/7YxcWw4SHJYfZuDKgfzyyy81bV6N8vjxY+Lj49m9e3elthu4IJAr65WrXh18O1Sqklngn8o27F63Q8qXkGnJqPtS3XKdQzVLf8b3DG1d2+Lt7c3peaefe9Xo6tWrjB49mlWrVqm3JYclY+lpiZGDEakPqq4KvUAgELxoVMkKwerVq8u879SpU6vChBcOC3cLbm0rW8VlQeVSWuJl689asz9uP56enmRnZ5dY2K4miQ6OxqaFDVq6WqSkpPDl919igAG5T3LRMXi+SuGVgSRJrF69mtTUVFzjXFHkKcM09BX6ZGVlcebMGSZMmFDDVtYc/fv35+DBg/Tq1avS2lQ9x06dnYg4HUHqg1T6/NAHqHjsfFZWFomJiTg6OjLn1TkY6RvRfXZ3vjH7hjqd6pD+OL3cxfB85vhw9eFVgn4IgntwMvgkPf16Ptfs/eXLl/n111959OgRb7zxBnZ2diSHJWPuak7q/VRSH6RSp0OdCrcvEAgE/yaqxCH46quvyrSfTCYTDsH/k2ORw9dXv2Z9p/WcOXOmps35z9FociNy83I1wlgKzk5u3769hi0szOLFi9HS0uLNN9+kTp066vwBACcnJ959910e/vSQtIg0LN0sa9hayMvLY9asWZzacYqHax5iVc8KbX1teub1xPWuK6+7v17TJhbLhQsXaNmypYaKWWXTtGlT/vnnn0ppS5Ik1q9fj1G0ER3mdyDuchyWHpbqWfHnqV49depU9u7dy44dOxj61VAAIgMiMbAywKGtA+lR6RrnKCuvLXuNIz8e4Yn0BGNd4+cO5fHy8qJfv3789ddfvPzyywQHB5McloxTZyeS7yaTel+sEAgEAoGKKnEIwsPDq6LZfzXOzZy5lH8JyV/i8ePHIpegmvnoo4/YunUrg2SD6CB1AECuI0eRp1CGttRCvvzySxITE+nTp4/aIWg8Shk+ZGJiwoYNG9hwbAPpkem1wiHQ0dGhS04XtC5q0WFeB4KWB9Ht626c/vw0r89/nYC5AchksloX0x0WFkbr1q0BGDBgADt37qz0VaIrV64wZswY2rVrx/fff//c7d28eZMJEyagr69PUlIS29pso17veoT/9fS7uSL9nJ6ezvnz54mPjycvL0+9PSY4Bntve4wdjXn0z6MK2XxrzS3aS+0BKiXPoUmTJvzwww/UqVOH0FBlDlBKWArmDc0xvWUqQoYEAoGgANU20snJyeH27dsaPyKCp5hamjLSaiSbl26ukByr4Pm4f/8++fn5mEgmyLSUqkLBK4LZ4rOF+Ovx+M/z56TvSSIiIgodW1lKKOUhPz+fDz74gBEjRtCwYUN1QrFqhUCFiZMJ6RGVl1j8vIowGmovQONRjcl9kov7UPdaq/Zy+/ZtTE1NAfjjjz/UqmmVya1bt7h8+TLXrl0jNzcXf3//58pXefLkCR07dsTKyopzZ8+RHJpMvT71SH2YiqSoeLvGxsYEBgayb98+evXqxcOHD1myZAnfbvoWe297jByN1CsE5UEV2mTkaEQuuTR4u0Gl5Dk4OjqqHRhduS5pkWmYu5pj6mJKyoOU52pbIBAI/k1UuUOQmZnJO++8g6GhIU2aNOHhw4cATJkyRVQofYbBrQbT3Ly5WkJSUH0s6bmE//E/Wli3YMieIXT068iT+Cdo62vzq/evHNh7gO4LutPXp6/GcZVV8bW8aGlpMX/+fLZu3YqRkRFJd5PIz8nHqomVxn5yOzkxYTGVdt7nqXwbERFB02lN8ZnjQ/T5aOza2KGtrywSFbQ3iO/DvmfD/Q2VZmtl0b9/f+Lj45k6dSorVqygTp3Kjzvv1q0bf/zxBzNnzsTJyYlOnTpx586dCrfXunVr/Pz8iIyM5N1x7yJJEk5dnMjPziczNvO5bDUwMGDgwIEAREVFMXPmTPZc2YNdGzuMHY3JeJxRrvYCFwSyzHcZipEK9qfsZxaz2BG247mTn69cuUJycjLe3t7o6OiQEp6CtoE2RvZGmLqIFQKBQCAoSJWPYmbOnMnly5c5efKkxjJ7z5492bGjdimg1DQW7hYkhSbVtBn/OQIXBBIwN4B+vv2QkiSsm1urlU8iz0TSeHRjDFMNkZB4HPFYPUCpTfr5McExyoRinadF7UaPHs3wXcPZeXRnpZ1H1S8FB2pl7YcJEyZgbm7OTz/9xONzj3FopwyLc+riRERgBJs2bWLnzsqztTLR0dFh1apVTJ8+HScnp0pv38bGhpdffpkBAwbQokULrK2tuXfv3nO16e7ujo2NDZZGlpi5mqFrrIuhnWGFB8I3btwgNjYWheLpKk6DBg148403aZXXShky5KB0CMqzCpHxJIPDuof5bOtnxGXEARD1IOq5agRkZ2fTsmVLLCws1LVdksOSMW9gjkwmw7Se0iGobaphAoFAUFNUeR0CVfJZ+/btNQo8NWnSRF1YSaDEpIEJx/cc59Evjxg9enRNm/OfQRXG0mBAAy6suoBpXWV4SMHEy0+vfErzWc0J+zYMf19/zi48S35Ofo05A/Hx8RgbG6ud7OgQZYXiglhZKVcL4mLjKvXcFa18m5SkdHbr16/Pw9UP1fs7dXEi+LtgFi9ejJ2dHQqFotKSd48dO4arqyv16tWr0PGSJFV7YbotW7ZgaWlZ4Yrl6enp6Orq4uTkRGxsLMErg4k4rQx1U4XKqJyxspKbm0uTJk0AiI2NxcbGBgBbW1sWvbeIvwL+wsjWiFzjXBR5CjLjMzGyNSpT214fezExeyKBpwN5Ofhlvt/8PZcXKcOyKvrZio2NxdbWloyMDGxsbFiyZAm///Q779d/H1D2Q15mHk/in2BoY1ihcwgEAsG/iSpfIYiLi8PW1rbQ9oyMjFpbAbamkNnLmHVmFmPHjiU9vfYVlPq3EuEZwdbYrezfuh+b5jYaz6XPHB86zuuIvrE+Q1YPYWTASECZ9FiZFV/Ly9SpUzE0NOTbb78FlCsEz+YPzJs3j5BfQxhkMqjSz+8zxwdk5at86+/vT0pKCt4tvYm/Fo99W6UD49DOgbz4PCa8PoG333670pyBnJwcRo8ejaura4XVeyZOnEjXrl05duwYAKmpqZw4caLSP59bt27lzJkz5OTkYGNjg5aWVoXzNb755hssLS1ZsGABAIm3E7H0UCaVm9atWKhMcnIy1tbWaGtrqx1NFdFBT51RHUMd9Mz0yhU2ZGVlxYoVK9i1aheW9pZ49PQg8VYi2anZ5bZThbOzM9HR0cTFxSGTydi9ezfBocGEyZWTULpGuhhYGYiwIYFAIPh/qtwhaNOmDQcPHlS/Vg22fvjhB3x8apeaSE3j3s4dJ5kTffv2Vc+mCqqeP//8kzVr1hAYEIh1c+sS971/+D4Acm25WgmlJnj8+DGSJOHg4KBMKL4QU2iFwNzcHAc3B9Ii0ir9/IELAkECZJSrH0xNTUm+loyhrSEmTiYA6BjoYO9tT8Spwgnbz0NcXBxNmzbFwcGhQt81kiSxf/9+Tp06pQ6TadWqFd27d+fHST8+V3J1QVJTU3nzzTfp3LkzT548UW+Xa8k57Xu63PkawcHBZGRkqMUJEm8lYtno/x0CF1PSHpb/ebCxsSEuLo4nT54Uctiig6Kx9LJU217RxOK0yDSM6xhjZG+EiZMJMSHPn/uiWkH76KOPeLfJu3Tp1EX9nsgjEAgEgqdUecjQokWL6NevHzdu3CAvL49Vq1Zx48YNAgICKk1z+9+CqYsp07SmMW7NOMydzWvanP8Mr7/+OnZ2dpgcN8GmuU2x+214bwM/bfiJurZ1+fjjj8nPzq9wcafn5fjx48THx2NoaEjinUSkPAmrxlaF9jNxMiEzJlO9olEZqAakoHTwvT/1Llc/PD7/GIe2DhorMU5dnLhx5Aa0AXt7e6ytS3bMSsJ/nj8yuYwOvh04cuQISUlJ6OrqKt+b709EQgTDVw8vtR2ZTIa/vz9//vknXbooB5KtW7cmNzeXzNzMIq+5YD5FWUlNTaVXr14kJiZiZmYGwF9//cXcA3Op06YOcl+5+jxlydfYuXMn165dw97ent27dzM7cDYvN3iZVW+twtTFlAd/Pyizbc+irV34J2PJX0sI3BXIN+bf8MEHH5QrsTgoKAgTExM8PDxIj0zHpI4Jy5Yt44T2CVxOuFC3W90K21qQkSNH8mT+EzzaeKi3CYegaPznKZ3Nop6vwAWByhDLeWV/vgUCwYtBla8QdOrUicuXL5OXl0ezZs04cuQItra2BAYGqrW9BUrk2nLMXc3/1YnFWVlZHDx4kOzsiocDVDYvv/wyS5cuxfKBJbYtCoe3gfKH8OSGk5zlLDf1bpJwM6HIBNvqQiaTYWNjg5GRETEhMdi0tClULyE5OZkvv/+S/bL9FZqxLQrVgLT5hOboW+pj09wG25a2pfbD1q1beeutt9i/fz+Pzz1WhwupcOrixMJ9C2nWrBn79+9/LhvlWnJGzB2Bvbk9Z86cwcLCAoC9n+yl97zevPXdW2V+/urXr8/kyZPR09MD4JdffuHBgwfM3DazwsnVz+Lk5MSRI0cIDg5+eg1yOUFBQQRHB9Nhfgf8ff35Su+rMrUvl8tp3rw5tra2xD+OJyw3jGvh14DKHwRnxmWilaF0NKOiogAwdjAu8/M2Y8YMPD09+fHHH0mPTMe4jjFr167lUPghrpy+UmG75syZw9ixYzl37hygzANKCU/B3NVcvY8qsVigyfMoiQkEgheXKl0hyM3N5f3332fOnDls2FD75ARrIxbuFiTdScKll0uVVkWtKQICAhg9ejQdOnRg3759FU6crGwyojN4kvCkkGynCkW+glc+eoV6hvWwzbQlMTAReL6Kr5VFUfkDoAx5me83H4C4sDjM6pk997lUCdgW7hbEXojFuasz9/++T98f+6rfL4pjx46xadMm6tevj8N5B1q830Lj/Tod6qCfqY+lheVz1yrxmeND2tI0klKSCNsaRqdOnQhcEEjoilD0jPTIk+cRGhpK06ZNy922aqVBdR4of3J1WejatSs//vgj/fr1w8HBgcD5gRXKW/Gq48VbJm8xbfU0oOIOwe+//86+ffvo168fI0aMUG+PDo5maIOh7Avap3a8jByNyrRCkJeXh4mJCTo6OvTu3ZvLxy5j3dSa999/n5hbMeQczSm3nSoOHjzIxYsXee211wBIi0jjSf4TTl07hVW0FZ06dcLUxZSHxx9W+Bz/Vgo+16rXtUlRTSAQVA1V6hDo6Ojw+++/M2fOnKo8zb+LOjBiwQji5sYRGxtb5BL9i8Szy88tWrSgXr16HD58mJ3Td+Js4Vyjy88xMTGkpqYiC5Vh7mqOrrFukfupbBzGMGIvxbL9p+1qBZrq/oE8cuQIe/bsoWfPngwdOpSYkBiajis8uDU3N2fixInEHIgh5VHlFGFS9cPZL85i0dCCer3rcejtQ0iSVGI/jBo1ivr169PBqwMX5l8olO+gZ6bHB14f0PbTtni84VFMK2Xn2p1rHJx3kKh1UaxYvwIpX6KTXyfOvXGO+vXrl/q5iomJYd68eQwYMICXX3652P3az26P/zz/ciVXlwV9fX3GjRsHwD+f/qOU8ZSVXsH33XffxcrKiilTpuDk5IRhqiF9WvahefPmgNIhyE7JJjslGz0zvTLbc+7cOX755Resra01HYKgaNzau2Fp+bQStrFD2aoVa2trc+DAAVJSUjAzM8M/0p96feoxa+wsctJyWG22mvTH6Rg7GJfZThW+vr7cvHmTli1bApASlkKQWRCzX5nNq6++qnYIKmOFIDc3lwcPHtCwYcPnbqu24DPHB0kh1QpFNYFAUD1U+RT0kCFD2Lt3b1Wf5l9Dveb1eJD4gKSkpOcqTFRbeHb52crKigULFvDrpF+JWB1R48vPmzZtwt3dnQ/+9wE2LYrPHyiIRSMLctJyyl2AqbL4559/+O677zh69CiKfAUxF4peIZDJZHz77beM9RmLIr5yVzCS7iZh3tCcOp3q8CT+CYm3E0vcv1u3bsyePRsXhQuWHpZFDkaduzqr5TGfF0dHR8avH4+eth5SvnIw3WpqK9zc3MrkZP/111989913+Pr6FnrPz8+PZs2aceDAAXYP3A3/37UVTTJ/+eWXad26Nf7+hRORAxcEErQ8CCNHI2RyGe3ntC82NCs1NZWNGzeybNky8vPzAWVCsUUjC/U++ub66JrqlnsgPGjQIJYuXcqAAQM0tscEx2DvrenclTepWJU3kR6Zrk401zXRxcrTiuig6HLZqWLIkCHqIm+grEHg3dAbV1dX9cC9og7Bo0ePOHPmDKAstmdkZESTJk2ee2WrtlGvdz2g5hXViuJ5K6YLBILCVPn0s5ubG35+fvj7+9O6dWuMjDS1qadOnVrVJrxQWHlYMdlmMpPOTKJ+/fo1bc5z4zPHB0mSmOk7kzGRYxj33TgsL1hyc/VN9YxTZerOl5f09HT09fWxVliXmFCsIjc3l7v375JdJ5uEmwkYO5Z/9vJ56d27NzKZjPbt25N0JwlJIWHlWXSoEygTiytbaSj5bjIuPVzQ1tfGqYsTD/5+gJVH8TaoUCUUF0WdznUImBtQaTYGLghEkacAGSDBjx4/MubCmDLNODdr1oyJEyfi4VF4teLhw4dcu3aNbfO20epCK9yGunF3z131YB3Kl2R+4cIFoqOj1XkKKgL8Alg3dx235bf5+cef+eetf6jfpz5aOlpFnkdLS4uff/6Zy5cv4+LiAiglR/Pc8vj9999p2rQpjRo1Ug+Ey/K8q+jUqROdOnXS2CZJEtFB0TSZ0oTFixcTHR3N119/rS5OVh4kSVLnEICywr1eMz2iz0fTcNDzz7wnhyXj3cqb/333P/U2UxdTspOzyU7NRs+0bKslJ0+epE+fPtjb23Pnzh0cHR3R1dVFLpez95O91LGq869Jxv3nM6Xoh0xLVurKVHWjmmiC50/qFwgESqrcIfjxxx8xNzcnJCSEkJAQjfdkMplwCJ7Bws0Cx1hHXJz+PTkEER4R/MM/BHwfwKMNjzBSGKmdgWvXrjFy5Ei2bt1aoZju58XPz4+5c+fyY4sfyzRAmjt3LosXL6Z33d70u9kPlx4u1WClJl27dqVr164AXP/lOrYtbQslFBdEbi0n8lxkpdqQFKpcIQBw6eXC/SP3aTWlVZH7xsXF8ejRI9zd3Yk+H43rYNci98t0yOSba98QMC6A9T+tr7BtQUFBbPHdQu6hXDq074BDOwf0zPUInB/Iz41/RvGJgj1/72HatGkMHjy4yDZat25drOjB+++/j2uqK1k7s3Du5szA7QP52uBrmoxpUuxApSSOHz9OaGgonp6eGtslhcQ/Rv/wKOMR11Ku4dDOgcfnHhebt2JkZMTo0aM1ihom3kpkb+Je9i/dz5IlS/jss8/Uxcmel/TIdDJjM7FvZc+s3rMAZaiOSmVIUkjI5EXXmgkPD8fb2xt3d3cCAgLISsoiLysPkzom7Ny5k9dff51WDVvxeeLn5bYrIiKCuLg4GjRooF59SA5LLlSMTd9CH10T5WqJTbOyOUft2rXDxsaG+vXrExcXh5OTE2FhYdja2nJ24dl/zSA1cEEgkacjMXI0wrG9I7YtbWtMUa0oRJ6DQFD5VKlDIEkSJ0+exNbWFgMDg6o81b8GY0djtPS1SLmXUuKs74tEt27d6O3VG+mihJFCuUJk4W6BJElMHDaRq7evMm3aNP7++2+N46ptVi0f0m+nl8khaNy4McbGxuiZ65F4s+QwmeogJiQGuzaFw4VUfPzxx6xcuZKBjgMZychKOWdOWg6ZMZkaDkHA/IBipU0PHjzI22+/TY8ePRgcMphOizoV2gdAoafgAheI2BvxXA7Bhs83sOH4Bga2GohxHWNMnE3w/tgbmVxGwNwA9s/bz+m801hkWBTpEJT23Hl7e3Mj9AbZLtm89vdryLXkmDibkHo/tUJJ5p6enoWcAYC2n7Wl/Yr29H61N+7u7mS3zebx+cdA2QZlinwFyXeT8R7kTcyTGHVBsYqEyty8eRNLS0tsbGzUExXRQdFYNbHC1MqUSZMmYWFhgUwmw8jBCEWegicJxVcBfvjwIQkJCcTHxwNK50LXRBddE13s7ZUhSJlSJtFB0eWuFr1lyxY+//xz3nzzTX799VdA6RB4jnzaxwkJCVhZWSn74n7ZHQIDAwOCg4Oxs7NT22Rnp/z8/VsGqSqbZXIZvdf35si7Rxi0S1ncsLY6BYELAlHkVl5Sv0DwX6TKHQI3NzeuX7+Om5tbVZ7qX4NMLkOvgR7r165H7izns88+q2mTnpu8e3n0udaHPPLQ0tUiPyefw+8e5sLqC3zY7kOybmfxcZuPNY6pzlm1xFuJaBtol0mF54033uDNN9/k2k/XuLHlRpXb9iyZmZk8ePCABg0aoKenR0xwDM3GNyt2fxsb5UAnKbnypGyTw5LRM9PDwErp5Ns0s0HHSIeos1E4d3EutH9WVhY2Nja42LmQ9ySv2MFX/fr1ebfdu1hbVLwGAUBdq7r0aNqDIZOHkPZ9GiavK+PSO/h2QCaTkXcwD9tgW9yC3QqFQQQuCGSV7ypem/Zase3f2n6LtIdpjL08Vp0DY1bfjJRw5ax7ZQ1Irv10jZddX2bMrjHIZDLux9/n6o9Xi9w3ISGBw4cP0717d/WAOvV+KpIkMWvBLGZrz1bva1rXlOjgssfmKxQKmjdvTl5eHg8fPsTZWXmPo4Oj1fkDa9eu1ThGz0yP9Kj0Yh2Cdu3aceXKFTIylKFFBcOF2rVrR3JyMkb6Rnxj9g3Jd5OxcLMosp2ikCQJW1tbGjRooH6dHJaMuas50dHRdOnShaioKNLS0iq0WqLq36IoOEh9UZNxFfkK6verjyJPQd3udXkS/4TUBxVzdqsanzk+BMwLQJFbuUn9AsF/kSqNSZHL5bi5uZGQkFCVp/nXYeBiwKy1s5g1axY5ORWX3qtpoqOjyUrKYlefXShyFXT268y07Gl09OtIbnouch05EbsimNx8MteWXHtuPffycvLkSfr168eXS7/Eupl1seENBdHW1kYmk2HpaVkjKwQhISE0btyYJk2aKBOKLxadUKxi8uTJRN6IZEjWEGU8fQU5fvw469at49SpU8qEYjdz9QypTC7DpadLsQWvJkyYQGxsLFN7TcWulV2xBdIsLCyYPnU6jeMbV9hOgNm/zebo1aOMGzeOtEdpmDqbqt/zmePD52c/Z8HmBdjo2BSqI3DA9wC75LsYvno44eHhhdpOvpfMkfeP0GhBI3Yc2sHZs2cBpaZ9yv3yh+GcOnWKzZs3FxIQyM/NJ2h5EO0+b6fuZ/s29qSEp5AZl1monSNHjvDmm2/Sr18/9baEWwmYNzQvFE5W3mrFaWlpWFhYIJfLsbV9WqcjOii6kFqUitKkR/X19WnWrBnt27cHNB0CXV1dzMzM0NbTxralrXpVpKx8/vnnapUogCcJT8hJzcGsgRmWlpbcvXuXjIwMYmJiyrVacuDAAX7//XeiozWdqcjISD788EPeeustANp+3haoncm4ZaHjvI7kZ+fjOsgVHQMdbL1siQpQ1pjwmeNTq/IgAvwClApcVDypXyAQKKnyIPUlS5YwY8YMrl27VtWn+tfQoHkD2tdtz4QJE3jy5ElNm1Mhbt68ibu7O682fJUnKU/oML+D+odRVdAr4p8IWk5siU0zG+Q6ytjrFborqm2JPSgoiEOHDhEUElSuBEsASw9LMqIzyErOqiLriiYhIQFjY2NcXV1JvKV0SEpK5jUxMcGuoR2SJJERXTFVpOjoaIYNG8akSZPYsmULyaHJWDS0IDMzk6NHjyJJEvV61yu1Am58SHyhgmTPUqdzHWIvxJKT9vyOcH5OPhnRGRg7FU4i9hzpyat/vIqWrpZG0a9mU5rRo2cPunXrViipPz83n4MjD9JkTBMO3jnI+PHj2bZtGwBm9cxIvV9+xZpNmzYxduxYtm/frrH99o7byLXluA9zR6FQcOvWLVJyUrBwtyhSeUdHRwcvLy969eql3pZ0O6nIZ6O8IUNmZmbExsaSnZ2tTnyWJKmQwlBWVhbJyclA+YqTgbJOgMohKIh9W3uiz1dMaUgV2pQSloKhnSG6xrro6uoSEhJCbGwsdnZ25SpO9sUXXzBs2LBCalAymYzVq1fz66+/kpWVxYHhB/7/jecbpGZmFnb8qkNdJyspi4hTEbgOVOb6OHZwJDKgcnOQKoPABYEEzA1AJpfhNcULi0YWNVIkUiD4t1DlDsGYMWM4f/48LVq0wMDAAEtLS41/gsJYulvyYf0PWbt2rTop7kXj0KFDpKWlEZoSSpsZbejg20HjfZVToGOsw8u/vkzrza35lV/Zmru12mbVBg0axPfff08How5llhwF+OGHHxg0fBBXTa5W+yrBkCFDSE1NZffu3cSExJSaUAygpaOFkb1RhZWG7O3tiYuL48MPP6RVq1Yk3U3CqL4RDg4O9OrVi9DQUFx6uhAdpFwRKo6SFIZU5BjkkGqfys0jNytkqyRJasnN9MfpyGSyIlWFMjIyuJJ1BenTp7OLMi0Zg2YN4vDhw8xsO7PQwMJ/jj95T/LQt9TH9rEt3bt3V8f+FwwZKg+NGzeme/fuar18UCYTn1tyDu9PvZFry3nzzTfx9PRky5YtOLR1KHLGfNiwYVy4cIGlS5eqtxWUHH399ddxcnIiJCQEUxdTMqIzyMsqn0xmQbnWlHsp5Gbkqh3pFStWYGBgwMcfK0P/jB1Ldgi2bdvGL7/8wuPHymtJj0zHpI6J+v0ffviBCRMmkGiTWO4VgmdJupukUaHYy8sLGxsbZDJZuZyj1q1b065du0LKUw4ODsycOZMffviBwIWB3N1zl7rd6yKTy/D+zLtCg9SQkBAaNGjAwYMHNbZXRxXh8EPhWDW2wsxF+dvj2MFRvUJQW1Bdr9tQN+za2NFuZjvSHqTh9YGXcAoEggpS5SpDX3/9dVWf4l+HhbsFyaHJFT4+KiqKWbNmsXLlyhpzut7o9AZXdK/w0e6PaPFyiyL3KTjof3j6IZe5jBZaDM4ZXC0Sd40aNaJRo0Z86/ttuVYI7t69y9GjR+nl2IuEWwk4+jhWoZWFkclkGBkZKWO4iwnZUJGbm8vChQs5lXeKTuGdcGxfMVu1tLTUn+XtW7fTtEtTvL29CQ0NVSoI9XDHqrEVD48/xH2ou/q4+/fvM3r0aJo1bYbbJbdCSi/PMnjwYAKiAtDZpkOLoUU/NyWRnJyMjY0NderU4cTGExg7GhfpMF25coXBgwdjYmDCbGajra2NIk/Bd3W+o+HghmgbaXPh1wuA8jm9//d9Lq65SNNxTQn0C6SbXzdmzZmlbq+iIUMff/yxehCtIuyPMLISsmgypgmgLOa3b98+kpKSsG9nz72D94ptr2DybeLtRHXBusePHxMZGcmdO3do5dUKLV0t0h6llSs2vyDRQdHYtLBRh3+pEpZV4TSlhQwtWLCAmzdv8vfff+Pg4EBaZBo2LZ9+Bvfs2cOff/6J50JPFBcVxSasP8ujR48YNWoUHh4efP/99wDq/IGiMHMp+8rOs3kSKmQyGYsWLVIOUr/wR9dMl6GHhrKj6w7MG5jT0a9jmZJxc3NzGT9+PL169eLgwYPExMTwzTff0L9/f/V9rY7E5bADYbgOeqoEVqdDHeIux5GTnlNs4cbqRlUxPSM6A1NnU4wdjGk8pjHZqdl09OtYKXkOzxbVLMiLKCMrEJRGlTsEY8eOrepT/OuwcLcgPSqd7LRsFNqKcis0DRs2jMDAQGQyGT///HMVWVk8WclZ/DH8Dyb4TSjWGShI4IJAYr6NYULPCdjetsWrg1e1qVlkxGSQGZOJddOyJ7IOGzYMd3d38g/n16jSUExIDC3eL7l/tbW1Wbp0KdnZ2Ty4+YBmFJ+AXFaS7ypDhn7//XdMTU3VgxWV/GhBh+D06dOcOXOGpNgkGhs3xqxBySte9vb2WBhbEHMtpkK2PXz4kPz8fDIzM8mOzsbE2aTI/by9vWnk0AjLx5Z4zfLCe4Y3C0ctxOqgFU8SnpB8LhldU138ff1Ji0zj7t67uPR04eI3F4sceJnVNyM9Kp287Dy09Sr+tSpJEucWn6P19NZo6yvb+eCDD/j444/R0dHh8fnHBMwN0FDeSU1NxcTEpJAST+LtRCwbKScEli1bhlwup3HjxsjkMkzqmpD6ILVMDsH+/fvZuXMnvXr1YsyYMQA8DnqsoW41bNgwBg0ahIWFsr3SqhV3794dR0dHXF2VA89nVwiGDx9O69at6dCrA2e/PEv8tXjsWhWfK6Pi7t27nDp1SiPO/1mH4MaNGxw8eBBHR0eG9BhCZmwmuU9y0THQKbX9klDkKTC0NaTDvA5o6WjhPsyd0N9DGXZ4mPL9Ugap/v7+bNq0iYMHD/Lo0SMaNWrEtGnTCt1Xnzk+5KTnVIm6Tn5uPuF/hjPsyDD1NhMnE4zrGBN9Ppq63euW2kZ1DKRVx2/22ky7We0A8J7hzcamG3nnzjuY1jUt6fAyIWodCP5rVIvQfX5+Pr///jsLFy5k4cKF7NmzR72sLyiMgZUBVw2vYmVvxZtvvlnu41euXEn79u3pbtC9yuNNC8a07t+/nxEjRrDvrX1YeliSn51f6jkKfrmu+3sdPUb2QEtHSz2rVlVLv1lZWfz999+EHA7BrL5ZmQsTAbRp04Zx48bRumNrEm5WX8K8QqFgwIABTJ48meTEZGIvxpYoOQrK2cvp06cztt1YcuNzK3ReX19f3n//fS5dukRORg7pUemYu5ljZmamMVhROQSSJKm3vfLKK+zYsYNRHUZh39a+VPnIXbt2EXYhjAbhDcod0gLKgmKRkZEcO3ZMGZdeRP4AQNDiIN59/C4r/FbQ64terF27lhUHV/B7/d+JOBVB8/ea8/KvL2PRyIIr31/hSdwT7u67W2jglZ+fT0pKCsYOxmjpaJUrWVehKDxAjDgVQeKtRA1Hz9jYGB0d5WDVpoUNOWk5pNx7uhrx1ltv4ejoyL59+9TbspKzyIzJVDsEPj4+tGvXDhMT5aC7PKEyQUFB/Prrr5w/f169LSZIM3/A2NgYS0tL9f0tbYVgzZo1HD16VJ2nUTCpGJShpn5+fni39ca+jX2Zw4YaN27M1q1b1QnFoMwhKOgQBAcH8+mnn/Lzzz9jaGuIll7p963gM10UCoWCLPss7ivu0/Rt5aqM21A3Hh5/yJOEJ2VKxq1bty6zZs1i8uTJ6OvrM3fuXExNCw9sM+MzCf9TmfBeEXWd5cuXExFRdEXwyDORaBtoF1p5LE8eQWlhTRH/RFTK71JOWg5xV+LUK7QWDS1wHeRK8MrgMh1fGqqw1meFB140GVmBoKxUuUNw9+5dPD09GTNmDLt372b37t2MGjWKJk2aEBYWVtWnfyGRyWTYONmQkZnBvXvFhwc8S25uLkOHDuXw4cOcOHGChg4NqzzeVPXlf2z2McaNG8f27dvZenwrNs1tCJgbUOo5VEu/qi9Xt1fdCDsQRtvP2lba0m9R3Llzh969ezNo4qBy5Q8UpLqVhnZM3cHBgwf56aefePLwCTK5TD3gK+mHdNGiRUx6dRLaiRWbud61axfr168nLi6OlLAUdE10C8lJ5uXl4dTFiYyoDJLDktXbjY2Nef3112me17zUcCFQPvvmDc3Rt9SvUOy4XC7H0dGR5s2bk/YordgVgmefOxcXF+rVq8f0hdPp6NcRSZJwHejKO7feQa4rR1JIhQZe69atw9zcnNmzZyOTK+PRy5NHsHv3biwtLXnnnXfU284tPofXZK9iHdRnlXckSeLcuXNER0fj4PC0f5NuJ2Foa4i+hX6R7ZRHbrN///4sW7ZMXbNBka8g5oKmQ/As5UkqzsvO40n8kyKTiqF8icV2dnaMGDGCESNGqLc9u0LQvHlzRowYwcsvv6y+b6U5R4sWLcLJyYklS5YU+f7p06fpOaknv+b/ql7ZMXMxw9bLlrv775bJ9gYNGvDFF19oODMA8fHxTJkyhS5dupCZkMmuXrs0HJTyJC7//vvvfPrpp7Rs2ZK0tMJOUNj+MFwHuhZSXKvToQ5RgWXLIyhtIF23e91K+V16fP4xxk7GmDg9/Yy3/awtVzZcITO+cEJ2RSh4LSv1VgpnQPCvpspDhqZOnYqrqytnz55Vx7MnJCQwatQopk6dWihpSqCkvVd7tvXdxpClQ8p8zI0bN9i9ezfHjh3D19dX/aV1xPcIULnxppIk4e/vT5MPlHHO/r7+/G/w/9j0xybGDx3P+aXny3SOZ2fNsm2yOaQ4RNSkKBb/sLjC9pVGZmYmnp6eyBPk5VYYAoiJicH/vj93790lLytPPQioSixNLRnOcGy62pBwKQFbL1vkWvIyLWGbOJkQtr9iDvjs2bO5desWTZs2JSlQWaFYNRP822+/MX/+fPr27cuKFStw7OjIg78fYNFQMxTl8fnHeIzwKKr5QshkMpy6OBFxKqLIugZlJe1RGs5diz6+4HN3//59hg0bxhtvvIFMJkNL62mseuCCQBQ5CnX9jIK5LQqFgvT0dCIjlTOn5c0jOLruKElJSWpp4ZiLMUSciqD/L/0LhVb88ccfrFmzho4dO+LT1ofo89F4jvBEJpMRFhZGQECARmXlhFsJamcRIDs7m0OHDnHv3j0++ugjTF1MNRy3kvDx8cHH5+nnOPFWIpIkFVIw+vrrrwkNDVVWQ3ZUJi4XVa342UJj6VHpyLRkGNpqOpkZGRnEx8fj0NaB0/87XSZbnyUnI4eM6AzMXJ+GqrVs2ZKtW7eqX5fFIbhz5w6RkZFFruoAGEcbo4MO9i725OTkoKurjLV3H+bOnV13aPZ2xUP1jIyM+PHHH3ny5AlfdfkKB4UDibcS8fH14cLqC7i/5l7mEEsPDw/at29Pjx49MDEx4cSJE+zatYs1a9YAcHf/Xbp/3b3QcQ4+DgTMCyix+nRBCuY6BPgFIOVJtP6oNW0/b4uWjpb6PdW+5f1devz4MXP95vKqz6vqbTt37uT777/H0MYQ7zXelRbf33x8c/x9/VHkKJDrFh0KJRD8G6jyUcw///yj4QyAMgFtyZIldOwoYvCKw7GxI8l3k9HXL3qGryhsbW356quvyMzMVP/gXrO/xjLdZTz0fVjueNPiYkHv3LlDD58eRCRG8O233/LO9HeIvRxL6O+hjNYaza2NtyrscFy+cpk/0/4kcGsgC75boKFsUpm0b9+eGzdusNlrc4UcgqlTp/Lbb7/xsu7LJN1JqlAb5aXPoj6YGpji7+vP5eTLOLR3KNMPqSRJSBYSjx9UTK1l5MinFY4f3X2krlCs4saNG2qJR5X8aMuJLVmyZAn29vb069aPpDtJpUqOAly6dImVK1dCAgw8NbDctm7ZsoW4uDj69+9P2qPiQ4ZU9OjRg+PHj/Pnn39qaPhD4fAA1WtQDmKGDx9Onz59cHRUhiyY1S+f9OgbHd/A6LgRbSzaAHB+yXmavduMy99dLuTgxcfHc/jwYTIzM3n13Ve5/P1l9Xv6+vp07645iEu6nYSlx9PvXIVCwSuvvIIkSYwaNQpTF1MeHn9YZlsLEh0UjV0ru0LJ2hs2bODGjRu88sordO3QFUVu0dWKv/32W+bOnctbb73Fl19+SXpEOsYOxhozw9euXaNZs2ZYWVlx/+p9Em4kkJOWg65JyUmtJ06cwMLCAk9PT/T09Ei5l4KOsU6xBdLg/1dLSnHkvvrqKyZNmlRkUTJJkri96jYnF52kw0xNNTX3oe6cmX2GrOQs9M2L/y739/fHwMAALy+vQmF1BgYGLPJbxMMNDzHPNicxLFH9TKZFpKFjqFPmxOUmTZpw5swZ8vPziY2N5eWXX+bJkyd4eXkxpMMQMh5nULdH4TwB25a25GXlkXg7ESvPwlK2igQ+pwAAZdVJREFUZ86cYdOmTTRu3Jhp06ap7fCf64+UJ4EMLq69yIVvLmBa1xRzV3Ps2thVKA8iJycHHx8fHjx4gMVgCwahrKJ88+ZNjh07xqhBo7j4zUW8Z3ija6TLtWvXaNKkSbmqXauQJIkd3XcAynorihwFp2aeosviLuVuSyCo7VS5Q6Cnp1fk0mR6erp6FkVQGEt3S8L/KlwYqSQcHBz46KOPNLZdv3qdzJxMggnGM9ezXPGmxSVVxW2Lo3dib46ZH+Pc+nPkzMjBtJ4pMi0ZUn7h0Iry0L9/fwa8NADbC7bk5+VXmUMAygS6hBsJFQoZ6tSpEzdv3sQqxYqEmwnV4hCA8j5IkkTA3ABiQmJQ5JX+Q7p8+XI+++wzvGXeTFNMK9MMX3E8WzW2d+/e/Pbbb/Ts2RNQ5hGcW3yO1ORU5s+fT1ZWFn+s/QOzBmYYWhc/KFORmJjIL7/8gnsDd1rHtEaRpyhVVrUg33//PadPn1Yq10RoFiUrirp16+Lt7U1urmZ+RVGOVlEKL9bWT5PRzeqZEXclrsy2vuT3Eno6evj7+nNcfpy7++7ScnLLIh28Hj16sHbtWjp06ICDgQN/v/83+bn56tnWZ0m8nYhjh6eKUgYGBrz88suYmJiQlZVVrhyCmzdvYmFhga2tLXK5XKNCcUHefvttkpOTcXZ2RsdQp9hqxQ8fPiQhIeGpPOwz+QOA2snKzs5G10oXkzomRIdEU/elkpNahw8fTlxcHBcuXMDLy4vkMGUCfFGDwezsbCRJwtTFVF3TozgsLS1p165dke89OvGIxFuJ6uThgpi7mmPdxJqwA2E0Gd2k2PY///xzzpw5w+eDP2fxXs2V0ZyMHAzXGOKp5YnHCA+09bTVz4bnCE/+HP0n70e8DxSfuKxQKNROu5aWFlpaWtja2vL1119z8uRJRo0axeWvL+PS0wUdw8LJ1Vo6Wth72xMVEFWkQxAWFsYPP/zASy+9xLRp00hLS2NK/yn8Lf3NR9ofIcuT0f5/7WnyVhNSwlJIvpdMclgysRdiUeSWb+ZdV1eXWTNn4TfJj1dHPV0heO2113B1dcXb25vzo85z9YerWA6ypFmzZjRp0oT9+/erq1eXlT/e+IPEm4m0/awtnRZ2YlPLTZxfch5k0GWRcAoE/y6qPIdgwIABvPfee5w7d045UylJnD17lgkTJjBo0KCqPv0Li4W7BWeunWH27NmFCuGUFUmS6BzTmTct3uRN3kQml5Ur3vTZWFBJkjg66SiB8wJpZtSMafrTeKvHW4w4M4JGrzdSOwPPU4xHR0eHfX/vo5VuK+KCyj64qghJd5KQacswb2Be7mM/+OADrly5wvCXhldLYrEkSezevZvw8HD0zJXx5Yq8siUU2tgonZVMKZPM2PLF1kZERHDz5k2yspT1BZJCkzRWCMzNzXnttdfU6jJ2XnZo6WgReT6SmTNnMmDAACwSLEqtP6DC09OTZcuWMX/hfOQ6cmIulk9tqH///rz++uu4ubqRGZNZbA6BiqlTp5KSklIoefPZHAMVqs9EUQOvikiPqtq7sOoCilwFIStDijyvs7MzkyZNomXLlli4WaClp8WDcw/o168fy5cvJy9PMwE78VaiRsgQKCvtbt26FWdnZ0zrmpIekV6mHB1vb28cHBzU+UzRQUU7BJ988gkLFy6kUaNGQPGJxbNnz+bq1atMnToVgLTIwis5FhYWJCcnk5qaiq6uLvbe9kUWZCtIbm4uDRo0wNraWp2snByWrBEupOLNN9/EwMCAbdu2lbtQ27MELgyk1Uetil29UIUNFUd+fj52dnYY6BqgtU9L47sz90kum5ptIvVBKp5vetJ5YWeNZ8P5JWckhUTEPxHFJi5nZmbSunVrvv3220IhT++99x5btmxBX1+/kNzos5SUWNyhQwdmz56tXh248OUFfj/zO1FE4bLFhY5+HQmYF8CNzTeo270uzd9tjo6hjrLKsAwUOYoSfzPu37+vUTn8lU6v8JHuR3QY/HRFxtPTkzfffBN3d3faft6W4BXBXAy+iJ6eHm5uburvwbJy6vNT3P7tNu6vudNlSRfk2nLGXByDpacl5xefr3AYm0BQW6nyFYLVq1czduxYfHx81EoZeXl5DBo0iFWrVlX16V9YLNwsCE4PJviLYPT19UsNr1IoFJw8eRIvLy/14CxoeRCRhyJpmdqSJm814eGxh+p4SCibpGfBWVH/uf4gKZVOui7rSt0eddUx7AFzA4oNrSgvcm05DQc3JHR3KE6dncp9fFkYPHgwqY9S6d+wf4VmzFUzjlaeVsRejK1s8wpx7949hg4diq6OLgulhciQFRnXXhRvvPEGr732GpsabCItIg0je6Myn3fDhg34+fkxfvx41q9fr5YcLQ6ZXEbdnnVJCkzCd64vALsH7salh0uZzufg4ECHjA7I78rR66xHxKkIHLyfOhOlyRZ+/vnnACTfS+ak9kkM7UpelfDy8uL27duFtpcUf1ywr3/44QcePHjAtGnTylWcLDMzk2XLluHm5saI/43A39e/yMTlopDJZdh723Nw+0EOHTrErVu3+OSTT9TvK/IVJN9N1ggZehYTJxMU+QoyHmdoJGU+S1ZWFkZGRjx58gQ7Ozvyc/KJuxRXav0LKD6x2MTEhKZNm6pfPys5CsrPV8GijGVJLNbR0eHs2bMa24qrQWBiYoIkSTx48IBePXqV6BA8ePCA3377jWbNmtG3b1+N9yL9I4kJjmHw74MJDg5m7ty5WFpa8ssvv6j3cR/mTuCCQLJTs4tMFtfS0mLXrl3k5OQQsjRE/d3pPcObTc03kRKeQvvZ7XEZ78J3331HkyZN6Ny5M6D8rmz0eiNubb9VrCToDz/8wKVLl1i8eDGjR49WK02pkMlkZMRm8PjcY1wXuBbK8VBRp0Md/vn0nyLP4ebmxoIFCwDlZzTIL4g3HN6gXud6DB06VJ2Xo7o21d8d/Toi05Jx45cbxf5m5OTkMGjQIPT19Tlz5gy6uro8DnyMUzunYlfIGg5uyOmZp3HPdCciIgJLS0v1CklZCTsYhqWHJQN3PA1d1NLRYuylsWxsvpFrG6/R9tO26JmVXaFOIKjNVPkKgbm5Ofv27ePOnTvs2rWLXbt2cfv2bfbs2fPCVuGtDnRNdGlm1ozRr4ymTZs2pe5/9+5devToQZ06dcjPz+fen/c4M/sMOak5dPTrSI9vepAelU6Tt5qUW9LTZ44PMi0ZaVIaX8u+JntSNnV71i2U0FowtOJ5ZUPr9K/D5l828+uvv1bo+JKQJIkjR45w8uJJbBo/X6iPlacV0dfLpoDyPCQlJdGiSQvq5NVBliejo19HpmVPK1M/GxgYYGysVOMob7Xi3NxcjI2NcXV1JfdJLmkRaYVyCHJycvj5558ZO3YseXl51OulzCMAZV9Hn48uU/6AClWoWn52PhGnnsojlkeFJO1RGkaORpWipFUSc+fOZeHChdy7dw+zemZkxig17UsjNDSU+fPn8+GHH3Jm9hkA5LryElfXUlNT+euvv/j999+xb2uPeZw5X3/9NZ988onGAC71fiqSJGFWr+jv17y8PLR0tTB2NC51ZlxfX5+YmBhycnIwNjYm7moc2obahZ4BFVlZWeoaAMaOxiVKj6ooKmToWYqr0Fwaz0qOqpg7dy4xMTFKaU8XU9Ij08nPLVoK+/z583z66af4+fkVei9wYSBeU7zQt9BHoVDw559/cvToUY19LBtZYuFmUWJBOVCGwhT87vza6GuS7ybT7n/t6LSgE9988w0TJ07khx9+0DjOY4QHd3bdIT+naPs/+OAD1qxZw/r16ws5AyrC/ghjh+kO2vdoX8h+FQ4+DiTeTuRJwhNAGfY7cuRITp8+raF6pMhX0G5mOxonNGbigolqZ0B1bQ+PP9T4zWg4pCGpD1JpP6d9kd9lN2/eJDIykvDwcJKSkgCICoiiToc6xfalXEtO20/bcn7peawsrTScgdDQ0GKPU3F3313SI9J5/fjrGp+ty5cvI9eRM/byWHQMddjYbCM5aTmFjq8sWW+BoDqpljoEAA0bNmTgwIEMHDiQhg0bVtdpX2h6N+/NZ0M+KzQrVRRxcXHUr1+f5s2bkxyazMGRB3Ed6Kr+0p05dyZr9NZwfOvxEsMeiiJwQSBSvsRZ+VkipUhWL1it/oKtSGhFWQhODmZjwkYmvD+Bx48rlgxbHAqFgv379/Nek/do7NO4wu1s376dlya+xA83f6gyeVQVzdybMT5mPBOliRV2viriECxatIjU1FQ+/vhjZYKmkU6hFQYtLS0++eQTNm/ezLlz50hzSuPs2bM8SX5C6oNUshKzsPWyLfM5Xd93xX6yPbeO3OLBkQdICqlMydO5ubnq0Jmy5A9UBm+88QYTJ07E1NQUQztDtPW1y5RYbGBgwDvvvEOXBl04v/g8uia6TM+eXuK9DAgIoH///sycOROHtg7kXc/jww8/ZPLkyRr7JdxKwMLNolDuxblz53BxcVFPMJQnVEZLSwuZTEZMcAz2bYquJ3HkyBEMDAzUydlGDkaFVghUlbM3b96sztsoziHYu3cvEyZMYM+ePdi1tiPtURoZ0aU7GAVJuptUpEPg4OCAra0tMpkMY0djkFHsZ0MlZeqp66lxXx4HPSbydCStP2pN4IJAUvemsm7dOnbu3FmobkFxYUO5ubmkpGiuKqkmYFAoncTOC5WrAQMGDKBjx46Fchkc2zuiY6LD/SP3i7RfLpczefLkQknzBQn/I5wGng3Q0tLiwoULRe5jaG2IhZsFUWeV8qM///wz27Zt45133tG43o7zOipzJ5pbY+muuUrlM8cHp65OGp9jK08rjOsYY9/avsjfjBYtWnD9+nXlc2CnrLsSFRClkSPzLP7z/El9mEpOSg5hB5TqapIk4evri0cjD5aPXF7ssVlJWfw98W+6fd0NY4enz+Uvv/xCq1at8PX1RVtPm0YjGnHo0SHWN1lPTvpTp6AyZb0FguqkykOGhg4dStu2bfnss880ti9btoygoCB27txZ1Sa8sFi4W5AUmlSmfTt27Mi9e/dIiUlhV5ddtJjYQkMJ4eLFi9zPvM+ZI2cY8tmQMofyqL7cZHIZP1z+gS9nfEnGoQx1mEpZQyvKy5i3xvDN/76hQ9MOGomblYGWlha9evUiLCkMR6/if1RKw8TEhMjHkeTJ8kgJTykxlOZ5UOQrODjyILqmunh94FWk86Xarzi++OIL/EP9ec/lPVrRqlznl8lkaGtrk3w3WUNyVIWWlhYffPABoEwG/d///se2/G1IEyQmvToJm+Y25aoC261bN27cuMHXo78m95dcvtL/qkwqJHv27GHkyJEMHjyYT70/LTV/oDJYsWKFxmtVHkFRiZcFcXd35x2Xd/D/0Z8GAxqgyFHeu6ISl1W0bdsWd3d32rdvj3Ur62KVd5JuJxXKHwBlYuzDhw+Ji4tDoVCUySF4VmksOihaXQzv2fAtlQJPQoIyp8bY0VhjhQcgMjKSOXPmoKury6hRo4D/zyEowiEICAjg+++/R19fn1deeQVLD0uig6JxHVh0nLufnx/Hjx9n8uTJvPbaa+Tn5pP6ILVIh6Agcm05Jk7Kys3m9Qvv26VLF7p06VIoFPLcF+doMaGFhirUhAkTijyH+zB3zi85T056DrrGT+/X8ePHGThwIMOHD1eHGR2dfBQpX0KuI1fH1vvM8aFLly6cOXOmUNsyuQyPNzy4tf0WrgOe9k1xoT/PkpeVR/jhcHz/8GWh88ISJ+wcfRx5HPgY15dd6dOnD++//z5t27YtFI5zY8sNGr+pnGzZunUrK1asYMuWLXh4eBT6zZDJZDQc0pDQvaH0+7lop8Xe3l79fGXGZ5J4JxGH9sXnJalWGev1qce5JedwHeSKTCbj2qFrKCQFN6JuFHvsyY9PYtPChiZjNJPAk5OTUSgUREdHI0kSf2T/wV72cv3RdXSa6vD29bcJWRkiahUIXliq3CE4depUoUIrAP369Sv0YyrQxMLdgujgaOLj4zE3N9dQ3ClKElSRr+Dvt/4GQK6j+QU9Y8YMhngNgcNlP7/qB9BzlCdRAVE4N3Vm1V+rnjtHoCzI5XK2rN5CwNwAde5JZZIZn0l6VDrWzSrubHTu3Jnjx49za9ItEm8mVplDcGb2GZLuJDE6eHSxhaZKuw/r16/n4cOH9HTpWWE7nk0oLsj8+fPVf5uYmGCgY0DDvIblDhcC5extXFwcrq+7cuvXW2Wuxvro0SPy8/OVymYlFCWrSsojPapaXcuIzkBL/2ksdHEOnqWlpTrfISQkhIdWDwn3D6dR30Ya+yXeSiwyf6B+/fqcOXMGd3d3ZLKyFeQ6F3aOX3/9lX4X+vHFni+IDorGZ66mbryKxo0bk5iYiLm5OaBMKn52hUAul/P222+Tm5uLXK4s+JYRlVEohwCgT58+GBgYqOPl7b2VFYuLcwiCgoL4559/1EXJ0h6mIZPLinwO8vLyWLVqFeHh4Xz55Zdl6ouCzlpGdAb3D9/Hy92rTANAq8ZWmNYzJfyvcBq99vR+BQYGkpubi6GhMtfFf54/l769hFNXJ944+UaZv2s9R3iyrdM2cjNz1SpBGzdu5Mcff+STTz5hyJAhxR778MRDDG0M8XzJs1QHwrGDI7e23wKUTu13331XaJ+0yDQiT0fy8paXAdixYwcXLlxg9erVfPvtt0W26zbEjb2D92qoiv300080a9YMb29vjX0fn32MpbtliaplBe+Vlr4WEacjiPgngrZBbenwZgc++fWTIo8LPxzOnV13eOvaW4X6YsqUKXh6etK9e3dkMhn9+vXju+++47Wur5F2II1vTL9BUkjCGRC8sFS5Q1CcvKiOjg6pqRVXdvgvYOFmQb/Z/UjcmcjNmzfx8Hha2KkoSdBTn58iJjiGJ/FPCiVb9evXj07NO7H+6/XFJrc9i2rAkpOWQ91uTxPWyjIjXRk06NeAQ2MOkXAzAUsPSyRJKndiWFFcvHiRm8dvghMlaoOXhqmpKd26dSO1SSoJNxOKHaiUheJqPtzcdpMjK4+wTnsdf43/i507d1ZIT3vSpEk8OvcInaiyO1e3b99m6tSptGzZkqVLl5aaUKy6jrec3uLDbR/i/7k/j2Me0+wdZVGm0hKCVfz999/IZDJleIYEMi1ZmZKnP/roI0aMGEF+fj7Bk4Nx6Vm2RObnRZIksrOz0dfXV64QlCGxOCcnR90P21/aTpOxmrORpQ0oVq1axS/xv5CyNIVNfTdpvJd4O5GmHZsWOkZbW1tDnMDUxZS7l0quovvE4wkXuYjOXh36+fYj/no8UQFRBK8ILjTw0dbWVgsaQNFJxXXr1uWnn35Sv86MzyQ/J7/IFYIePXrQo0cP9WuHtg4lFtdbuHAhI0aMUIfUJIclY1bPrMjQDS0tLebOnUtGRgZTp07FtF7xDkF2djZ6esrvy4IDTZlcRtDyII1+SExMJCgoCC0tLbUULyhnwd2HuXPn9zsaDsG8efMYOXIkcrkyHytwfiCGtoZqCdOiVozy8vK4fPmyRiE6mxY2yuKDf4Th8bryd2LTpk34+/szYMCAYvsMClQnLvC9EhcXR0JCgsZvDigdghPTTpQoB3x7x22cujqpw20+//xz2rVrx/vvv1+sDQ7tHZBpyYgMiMS5izPXr19n4sSJ5OXlERISQsuWLdX7RgVG4eBTumpZwb7b8dIOkKDz3M7qz53/PH+QQdtZbdHR0SE7NZsj44/QdXlXrm+6jiJfQWwLZa0G1Rim4D196aWXCAsLw9rampV6K9UFDIUzIHhRqfIgt2bNmrFjx45C27dv307jxhWP3/4vYOFugX6+csD66NEjjfcKxo7vn7EfNyc3PvvqMzLjM4udoTCpY4JJXRMeny1bTH7Hecp2/tj7B8suLdOQPy0tXKgy0DXWpV6fevy17i+6devG+vXrK6Xd1atX8+Ynb3LBqOhY2fJi5Wn13NKjKgevYIxydHA0f439i4c5D0nMTCQ8PLxCzgDAZ599xozJMzCINyjzMTdv3uTIkSMcP34c+P947GJWCECZm3E7+ja7fHeReDGRtPtpPD77GPu29uWKq1U5A/6+/rj0csHtFbcy5UloaWnh6OiIs7MzaRHVs0Lw+++/Y2RkpJZQNqtvVibp0VatWmFpaYm/vz8J1xKwblK+lSpTU1PszOxomFc4vCPxdmHJ0SLbKMOseO/evVm+fDnDRw3n7IKzSPlSkc5AURg7GqurFRdHemQ6euZ6RWrfP4t9W6X06LPx+SpatGjByJEjcXVVOubFSY6C8hmbNGkSM2fOxNDQUNkXRazsZGZmYmhoSN26dUlPVzo3Td9WOltFqUL99ddf9O3bV2PFTIX7MHfu/XGvUNK5u7s7DRs2JD0qHbm2nFf/fBVtvadzdQXzsdLT07Gzs6NNmzbq5G3V9XiM8OD29qeKWVu2bGHJkiWMHj26yD4ApTP7rNzogQMHqFevHu+++26hvrZubM197jPlnSnqCt3PcnPLTTzf9Hxqv48Ps2bNwsqq+DA6uZYc10Gu3N2rdFDt7OwYOnQo/fv3p0WLFhr7lpZQXBCfOT7IdeXw/5dxac0ljk4+SlRgFDn5OXww7wNeaacs2Hfq81OYNzQnIyYDf19/tgRs4dVXX2X48OHqmhnPYm1tXWQ1c4HgRaTKVwjmzJnDq6++SlhYmLqa5rFjx9i2bZvIHygFc1dzxinGMfXuVGxcC6vhqH6INvhu4C53sceeTn6div2hvn79OrccbmF/2J56veuVyYas5Cz2h+7nDndo8XuLaq8u7faqG0tmL+GfR/8QFhbGO++889whRHZ2djibOePZxLP0nUshJiaG/ff2c+fkHfpRfNJeaTw7E9js3Wb81v03FLkK3p79Nu/3fZ/s7OznslWVVFzW2OLWrVvz008/YWCgdCKeLUr2LP/73/9Y8v0S+rboi8MXDpi6mJKVmMWdnXcImBdQ5qX0guEoNi1sOPO/MwzaqRxwlzVUrbpChkxNTXny5Il6cGRWz4zbvxWWMX2WiIgIUlJS0M/X50nCE6wal5xzoOLRo0d0796duLg4Lu2+xKExhzTez0rOIjMms1iHIDg4mFOnTtG0aVNaubQi9UFqic+Dt7e3OmRjxbb/a+++45o6uziA/8IeIWFPZQuIoiIURBT33hOttc7WWvV129pWHPXVqtXWVlvrbmsdddetdTMUFdFq2SB7yCZskvv+kTdXIhsSAnK+nw+fD7m55D65udzcc5/nOWd7nYUHjx49ioCAAPj6+sLL3atKteKKigqpoY+CZEGtaU8LCwvx1+d/wdLQEp6fe6K8sJwtNgbU3utUU8pRia1bt7K/51jlICUgpco6MTExEInEF+JcrviO9/VPrgNAtWl/u3Tpgo4dO7K1GCoz6mIEbTNtvLr2Ch3GdpB6TlguRFpwGjw+84CpW9UhdpX3t6S4VkREhFTlZKcpTni4+SFK80qhzleHhYVFlbl7b/dEZjzNQFl+Gdr3ac/uS/d57hCJRCgtLUVOTg709d8cSxwlDvy1/fHktycQagqrDBnKCs9C5stMOIx3qPIe6mI/xh63/nMLfbf3haGhIY4ePYrS0lKpY1NYLkRqcCoG7BpQyyu98fbFut0YO4jKRTg97DQStRPxDM/w/Olz/DTuJ1T8XYEuH3VhU2h38+qGA3cPoHv37jX2TNdVzZyQ1kTuAcGoUaNw7tw5bNq0CadOnYKmpia6dOmCv//+G3369JH35ls1FQ0VtLdqj5KkEqCG0Sguc1xg7WeNWZgFjgqn1pPQzJkz8fjxY6AAbLn3uiTdS8J06+lIGpLEFhJqTrYjbdFldhcs/GghVn61UibzCb755ht0+rsTPKZ4NPm1cnJysO3oNqhCVWpoQWNUV/Oh5/qe6OnXs46/rBvDMBDpiJBTmiO+QKtH1eD27dtj1qxZAMQTD/MT8mvtIRg8eDC++eYbCHQE7B19cNCgYAAAnr96jr+7/g2BUID/OP4HOVE5EAlFdQ5V++qrr2BgYIDpU6ejOLO41gtNWenZsyeio6PZyro86+rvNL8tPj4eycnJUE1QBd+GLzXRtDZmZmZISUlBUVERBLoCCFLEP1xz8cVqTkQOtIy1apxrcunSJaxbtw4fffQR+u7oi4qiinodD5JMYxyV2odvXblyBUeOHIG1tTV8fHygzldHYWohGxCMGTMGDx8+xN69ezF+/PhaU45WVFSAx+NBJBJhDdZASUkJRt2MkBacBj17PamLseTkZAQHB8PJyQkdO4oD/dyYXLTzqV8dk5p6Szp37oyMjAw201nQ10GIuxQHm+E2mHBpQpULQBcXF/z7b/UTVtlhQ6ci0WFsB/j6+sLU1BQrVqxA0q9JqCitQI81Peps67lz52Bqasqm85TQd9SHgbMBos5GofPMqkPGgKpDTaP/iob1UGsEbwlm96WZmRlCQkLg5OTEXoyXlZWxFY6nDJ6CshtlbBGyysKPhcN2hG21ufmfPn2KrVu3on379tiyZUuVINRygCVi02KR+U8mW/n97fPp6+evoayqXOekfaDmi3XvDd6YnzYfsZdjUbSxCBXPKlB8vhgcZQ6efC9dGDAsLKzG6sb1rWZOSGsh94AAAEaMGIERI0Y0x6beOXoOesiJzEH7Pu2rPFdeXI4jHkegBS24qLnUOc7ax8cHqowqBC8EtY4BrSzxdiI8h3riq5+/avJ7aQxNfU1Y97dGf+f+sLSsvvBOQ4kqRMh6mcV+6TSFo6MjfCf5ouBkAXISc2BqX/0E2prmCABv7nJ2X9Qd5YX/H07AiO9AyiIYAIDffvsNM2fOREeVjliYtLBeAUFleXF5UNFQkUrD97Z+/frh1KlTcHBwgIuLCx5sfABhmbDB42qNRxrj2sFryNfKx5ov14ARMuIMMLa6Nb5OcXEx/vvf/wIAxvQeAyVVJWgZN+w9Noa2tjY7RAUQDxkqziyukk3mbXw+H3w+H09uPIFBp/r1DgDicfq3bt2Cg4MD9PT08Mj5EdIepcF+jHjoUFZ4Vq0Fydzd3TFlyhR4enpCjasGDX0NFCQU1Hg8hIeHI+pgFMK2hUFZSRlzIucg7EhYjRc8Y8aMgbW1NXr2FB+3konFkv+1hIQEZGVlsfnwa8owJHmvJiYmKCgogMMMBwT4BcDUQzyxODcmV+pi7MiRI5g+fTr69evHDnHLjcllh/fUpLS0FLm5ueBZ8VCQWABGxEgVKuRwODAyMoKRkZFUxrVBewZJvf/6XgA6THTAn/3+RHJCMpuedO7ouXi46SGm3JsiNVSoJhYWNQ+XcZrqhNO7T2P73e34+OOP4eVVfUYySXtjL8SCZ8OrcmErCaoAoFevXnjw4AEePHgAd3d3TJo5CTx/XpVeEIZhEHY0DD5bfFCd58+f4/jx4zA0NMTSpUthZiY9D+Dq31extWQrkhcm49e7v1bba5USKJ4/UFcxyfperO8avwvF2cX42eRnlFeU44bSDXw470P2dWoKBoDaU25LniekNWmWgIA0ntBCiG8PfguzaDNs2bKFXc4wDP7o8QcEyQJ4+XnBe713nd2V27dvByNisEt/FzKeZVTbNf22hFsJ6PFV3Xet5KnD+A4IPxoOtyXiSXT1HfJSk5xocSrX2u521xeHw8HxP49jr81elCWUATVk7KtuEjjw5ourXZ92eLLjCVtZV9LFfXHVRbzQfwEPDw92yF1jSPJ3l6uJi4sZd6u7LsCdO3dgZmYGOzs7ccpRO906v4gnTJjAvi9JMFCfCcGVdevWDdu2bYOdnR2UVZWha6eL7Ihs6Nrq1vg35eXlWLFiBVJTU6GcrwyddjqNqkDdVJoGmlDVVkXeqzwYda474Mx8kQnDzg2bP1A5D72Zp7hglyQgqCnlqMTbN2ckd8ZNuptUv63unsgvzseuYbugGasJXRvdWi+CJ06ciIkTJ7KPuWZcCFLfTCy+f/8+EhMTYW1tDaDuomTR0dFsBp4gE/H/StqjNICB1MWYhoYG3nvvPbi6ugIQnyPyYvNq/R+/cuUKRowYATc3NwT5i4/XwrRCtrflbSKhCPZj7SFIFkjVuKjpArC685RJdxNoGGgg73Eezpw5g6chTxG6KhRuS93qVf35bW9vw8nXCZ989gmePn4KLS2tKgFB5fZKPr/0kPRae/CUlJQgFAoRGRkJd3d3mHqYIj8hX6pnChCnpC3KKILt8OovomfMmIHy8nJ07969SjAAAJGRkRAxIsS9jKvx/dZ3/kBDLtZDd4dCVCFCuko6blbcRB/XPniR8KJKD8zb5JVymxBFocoZLZy6hTp+f/A7du3aJTXB69SQU8h8ngmnRU54afEST548qVeRKo4SB+Y9zasdL/u2+PB4HP/nONJ56TJ7P41hP8YeyYHJSAhLwJw5c9CxY0e2AFVD3b59Gz0G9cBl3mWZFo6pa2Lx258NwzC4/+V9cVo8dWUoqSjB0dcRudG5UlWIT247idWrV2P58uVNal///v1RWFiIzT6b61WcTCgUYsiQIXByckJSUhJyonNqnT9QWeW7c/WtplyZjY0NVqxYgXHjxgEA9J30kRNRez0OHo+Hbdu24ciRIxAkCZo15eiJEyewZs0ahIeHg8Ph1Jl6NCgoCBs3bsSNGzeQ9TKrwQFBZWYeZkh9+CZJQHZE9SlHa1LbxGKhUAhVZVUocZRgZGgEm2E27HP1LTzINeeiMOVNMTFdXV24uLiwPQSCZEG1KUclJMGAZJuSCaJKqkpVApHg4GA2lXVRehHKC8vBt6l+UjEgrpnBMAzS09Ohoq4CbTPtKhPCv//+e+zYsQOJiYnwXucNUYUIHcZ3qPJalZMsnDhxAo6Ojvjoo4+k1glYF4AHGx/AYYID4v+Kx9ixYzFMYxgqSiqgpKLUoMq258+fh5eXF8aOHcsWeAMAniUPY1zGYIzXGMyZM6fGv3ea4gT8P46oqwdv7969SEpKYtO5qvPUYeRihJQg6e+QsKNhcJjgABWNmu8zzp07F927v6mDEhsby86NWr58Oc7/eR6j80ajILH6c1RyYHKtBckkJAkxqlP5s6p8rsqfkQ8tNS30SOmB4E3BdW6DkHcNBQQtXEePjvDR9YGfnx974o+5GIPEO4no+klXaIzWwLx58+Dr6wugfl/U5j3NEX8/vs5tn9pzCleZq1iyeolM3ktjcc24MO9hjow7GTh//jwiIiJw9+7dRr1WREQEopOika8h25S32vbauHvrbo0ZUIA3n42fnx+MlIxwY9MN8Kx4mHhtItr3aY9/9v9TpYu7x+we6Iqu6MrrWuPr1oeamhq0tLSg004HgiRBnevn5ubCyckJenp6aN++PVuUrC41ddU3NCioTM9RD9nh2fVevyCxoFnmD0js2bMHGzduZCu81pV69ObNm1izZg2OHTsm7iFoYIahytjMO//P5JMdng09x7oDt4qKijqLkykrKyOzIBOlZaXIvpMN66HWUs/XlGmspKQEcXHiu7zVVSuurCCp5iFDb5NMEOUocyAqFyFwfWCN6+bG5IJrwa21IJ6zszPS09MRHy8+F1a3L3bs2IHly5cjMTERZQVliL8RX21AUJmKigoiIyMRGxsrtVzSS1iaX4ro89FIf5qOBxsfwKK3BYI2BDXoBoWamhoePHiA7OzsKneyR388Gr7KvlIX3pVFnonEYZfD7LDEujLjODk5wcLCQqonwrynOZID32QZEglFCD8ejo7v1z9RQ0xMDFxdXbF48WKIROLvq9GTRsOqjxWiz1dNh1uQVABBkgBmHnWnHK2Pt89V+/btQ1ZeFj7e8HGjz1WEtGY0ZKiFM3E2wdiisVi8YjGUlJWQ+TITl6ZdwvAjw+E02Qn37t3D4MGD2S54oPbuylmzZuHMyTP4UP1DjGXG1jr0hnnFoLddbwyZMESWb6lBJGPvO4zvgFfnX+GHH36AhYUFevXqVe+89pWNGzcOyYeSYeNlU/fK9VRWVoZxv4xDSVkJxiWMg5VVzfnvvdZ4wXKtJf5m/sZfnL/wNO4pOBwOEm4nVNvFPf/AfHSz7iaz8ag67XTqlSffwMAAz549Y4ck5ETlwGFi3ZlDZDWuNi0tDenp6XBwcIC+oz7+/b3myqKAuN6JhoYGVFRUmi3lqMTo0aPRsWNH2NiIj6m6Uo9269YNc+bMgUdnDxT8WtCgO/pvM+xsCFGZCNmR4sJ4udG5daYcdXZ2RkREBF6+fAmeFQ/J96tPHymRG5mLksySaucxvS09PR2mpqbgcDgoLS2Vqlb8/PlzXLhwAa6urhg+fDiAuocMBQQE4Pfff4dGvAbaXW0H7w3ecF/hjj3mexC4LhAcpeoTKeRE59RZoVhVVRXGxm+Gzr0dEDAMgxkzZiAyMhIODg6IvRILXTtd6DvUvn99fHxw4MABTJs2TWq5pJ0n/E4gVSMVeQPyYOduh+e/PG9wMathw4bhxIkTcHFxYTPgCIVCCAQCOExywO0lt5GfmC81tElYLsS9z+4h9KdQCEuFTcqMY97THKE/hbKPE24lgMPhoH2/uo8R9m8SElBRUYHU1FSUlpay2czsx9oj+lw0ui+SDmhSglJg6GJYpTJ3Y719ruJwONDQ0KA5AKTNooCghZJcCHuu9gQjYpCfkA91njrOjj4LY1djZP0rHp7i4+MDH5/qJ3FVp6SkBPmF+YgtjkV+fD741jV3qetH6uPAtweqpMhrTpK7am5L3ZBwKwGfHv8UGroa1VZKrQ8TExO0S2uHgWMbX7H3bWpqanC0c8SriFdISEiQCgiEQiH27t2L1NRUbNiwAXdW3AGf4cOWY4tBzCA82PigzpoOshqPeuDAAVy8cRFupW71TpEqCRjrU5QMkN24Wnd3dyQnJ+PRo0cwczKrs4dg6dKlOHToELZu3QrrROsmDcNpqLezrfCt+Uj2r/kie+TIkRg5ciRir8TiToc7tQ6xqIuyqjKMuxsjLTiNTRJQ2/80IP5MRSIRkpKSYGllibAjYbWu/+rqK7Tv275e7TQyMoKKigrU1NSQmZkp1UPg7++Pr776CmPGjMHw4cNRXlSO0tzSWgOCmJgY/PLLL+iADvh1w6/sMTT8yHBcmHhBfMe9vBQzf5sJGxsbXLp0CVpaWnWmHK3O2wEBh8PB119/zT4OOhNUZ++AZB/Mnj272ue81nhhx6UdOPXwFF6VvMKk+5MaXdl28uTJUo9XrlyJ7777DqeXnoZlf0tEnIjAeyvEKWMLkgpwtOdRlGSXSAUDkjYBDcuMY9HTAtfmXENFSQVUNFQQdjQMTlOcGtTL0a9fP9y9exenT59Gfn7+m4BgjD3uLLuDkpwSqWxZKUEp9RouVF80B4AQaXIPCIRCIQ4fPoybN28iIyOD7RqUkGSEINIqT0Ll2/IR8yAGj3c/hqq6KpLuJsFqQOOqsH755Zf4/PPP8XTuUyT7J9d48VCYVoiciOqzGzWnyl9W2mbaiL0Yi7y4vCrDUmpTOcNPSU4JChIK2KwnjellqM7Vy1fxu83v8Ogincr033//xaeffgolJSU4ZDsgbXcaXDq5YMeLHXXemSspKUFFRQWb/7ypLl++jHMB56Bh3LDqzMIyoTjLjwwmYdeXmZkZysvLUVhYCP3O+ihMK2Tzq1cnOTkZQqEQenp6zVaDoCY8ax7+PVJ7jwYgnlDckAxDNTHzEE8s1jDQgK69bp3Zw86cOQMejwdjY2O8Dn1d45Chv//+G4cOHYJ2iDY++fSTerVFSUkJmZmZ4PF44HA4EJoL2UnFDg4OmD17NjuURZAsgLKacq0Zr9zd3TGrzyzYmdpJ/Y/YjbCD9RBr5CfmIyU7BfHx8cjOzmYvKvNi8upV2+HixYu4evUqBg8eDEtrS8RcqL4SckVJBWIvxWLKvSn12g+VlZWVsZVuAWDQ7EF4HPwYnZhOMqtsKxKJcO/ePQDA+u/WY/3Y9Qg/Fo73VryHV9df4dy4c6goqoCFjwWsB1o3uQePb8uHhp4G0p+kw7i7MaJOR2HSzUkNbre7uzvc3d2llvEseTDqYoTYS7Fw/uBN8dKUwBS4LnRt8DYIIfUj94Bg8eLFOHz4MEaMGIHOnTs3KTtMW1L5QjjYMBhz358LTw1PTCyZyF4IS6on1pUNobLOncVp+LJ7ZyM5IFnqhFtZ4IlA6Ljo1JjPvDlV3hdXZlxBnigPmYMyIRAK4IW6v0wrB1dBRUFINkyGMle50b0M1TG1NoW2sTaSniYhVycXbm7ijEguLi7w9fVFO0E7pOxOgYqKCkYeGyn1vm743ZB6LHH9+nWMHTsWo0ePxrlz55rcRl9fXzhYOKBsX1mdmZpWrFiBFy9eYMWKFehu2R1KqkrNOi4/ODhYqn2ahprIjsiucfzwhQsXkJ6eDm1tbfy+/PdmbSsgHl5SWFgILpcrHjJUy7AsSfXbxmQYqo6Zpxke73gMXVvdeg0/qpwukmfFQ3FWMcoKy6CmLT0UIzQ0FEePHkV3pe6wGVr/IXZ8/pubDFxzLgpTC8EwDAYOHIiBA9/0zBUkF0DbXLvWbFDOzs44eOdgtc/1+74fDnU6hFHbRyHg/QBkZ2e/6dGKyYXdqBqKt1Ry79497N69GyoqKvjP4P9IDfXKy8uDpqYm1NTUEP93PDQNNeuVnUsiODgYK1euRIcOHbB//352uUu6CxYwCxqVgasmSkpK8Pf3x61bt6AWqIbQ/4aCo8zBjU9v4J/9/0BUXv1Qvsoa0gYOh8POIxCkCqBtqt2oLEk1sRtjh6hzUez3U3lxOdJD0mXaQ0AIkSb3gOD48eP4888/2TGjpP4kJ+hnfs8AAAUlBVIn9fv372PEiBEYPHgwzp4926DXtuhlgcB1NU/K+2r7V3ic9Bjaf2hXGQurCJKxrqJyEfKU8/DzjZ/BDeLis88+Y+8K1va3APC339/wgx8AYNTXoxCyMaTR3fXV0XbQxsz/zER4ajji4+PZDCnHjh1D4PpAvMp8BW47Loxc3qSjDNEPwRa1LeBH8asEN+Hh4WAYBrq6ujJp3+TJkzFmyBj8+OOPKM0rhYZuzcHe/fv3ERwcjHnz5tU75agsvR2s6Dvq1xoQKCsrw9zcHGWFZSjJKWnWHoKnT5/C29sbxsbGePXqFfjWfJTmlqIkt6TKPi4pKYG2tjZ0dXWxxXoL7EbWfdFaF1MPU2SEZkC/o36d8wfepmmgCRUtFeTH58PQWTo46d+/Pz6f/TkElwX1zjBVmUgkAqPDvKlW/FZPQF0ZhurCt+ajxxc9ELA0AB88+kCqZyQ3Jhd8u9qHTgHAoEGDoKysjH79+oFnwZOq3Lx69Wr88ssv2LRpE7pGdkWHcR0adEOroqIC9+7dQ0hICL777jvo6OjItbKthoaG+Ht2OKCtro0AvwA82/OsSorWppL0uJr3NEdKYApSg1Lh9L64iJmselw7jO2AR1sfsUOS0p+kQ0Nfo9asUYSQppF7liE1NTXY29eQnJ3UyWuNFzqrdsZ/8V/MVZsrdVKPjo5GcXFxg6v3Xr9+HQf9D+LFPy9QkltS5XmGYZCangoRI2J7FBRNEgxwVDhoJ2yHfg79xHUVasnqU5nXGi90WtwJjnCENaxlHgwAQKAwEGnpadDS0kJ09JssGRwOB10/7orXz15XKTQWHh6OorIiPGYeV3m9VatWIS0tDevXr5dZG9V4alDlqtaZenTHjh3Yv38/PDw8kBOV06zDhaqj76Rfr0xDgiQBlNWVoWlYe5AoSwYGBiguLkZqaipEIhE09DSgzlevdihOcrJ4bkFZWRkKwwubNGQoYJ04Ewrfhg91njpizsewGYaCvg6qMY1lbGwsduzYgV9++QUcDqfGTEPdu3fHAM0BGDNhTIPbdunSJTg6OuKzrz4TVytOKURxcbHUOnVNKJYoLy/HP//8g4yMjCrPua9wR3lhOUL3hLLLSvNLUZxZXK85BIMGDcLmzZsxePBg8Kx4bOVmAEhMTIRIJIKhgSFizsfUa/5AZT179sQPP/yAsLAwNhj40+9PeK71lFkGrppUTtEqq2FJEpIe1+yIbCTdS0Ls5Vh0fL8jG9zIIp2zoYshtIy1EH9TnAFKUn+ARhgQIj9yDwiWL1+OnTt31vvCjUgL+joIyuXK0FTTrJIebu7cubh27Rq2bt3aoNf88ccfseW7LUgxTKmSSxoQT0BbLFyM8OfhcHFxafJ7aKrKd9WWly9Hu17tMDxyOJzinKTylNemoqQConAR5nLmYgEWyPxLEgBGDByBqTZTER0djS5dukg9F7wlGDbDbKpUR960aRP279+PX3/9FYB46MvEiROxc+dOAOJJ0LVlLWoogUCAXMNc5CfUnnbV29sbc+bMgYWFRb1TjsrSrVu38MEHH2Dbtm0A/p96NKL6gCAuLg7Lli3Dvn372JSjzXnhYGFhgZiYGOTm5rIZX2pKPWpnZ4e8vDzcPX8XjJBp1J13CcmF2YOND2DqYYrSvFIYOBnUeWEWFRWF5cuXY9euXeK2WvFQkFA1QGQYBnFX4qTqD9QXl8tFdHQ0Ll26BA1TDeQm5UJHRweGhobIzMwEUP+UoxMmTECXLl1w+vTpKs+pqKtA+L4QP3/2M+L/FV885sbkQl1XHZr6DQsKJZWbJcHRX3/9hdTUVHiaeIKjwoG5V8OHqyxatAjt2rUDAGQXZOMHzg8Y++NY5ObmsuvUt6ZDQ0hStNYnrWhDSdr74sALlOaWwtDFEBEnIho0r6suHA6HzTYEiAMCGi5EiHzJPSDw9/fHH3/8ATs7O4waNQrjx4+X+iE1q0+Bp0GDBkmlHK2PkSNHYvbs2XDp7lJtgbLE24kwdTeFo4sje4GjKNXltZ9ydwr0O+oj+JtgBKytu5iPsEyIvyb9hcyXmfXOvd0YXXp1gWWmZZUeG0GqAM/3PYeXX9UvSh0dHcyZM4fdz3FxcTh9+jQ7QVCWKioqoK+vj3Wv1iH2RWzdf/B/DSlKJivx8fH4448/2KQDBk4GNRYne/nyJb777jvs2bOn2VOOAuLhSra2tlJD12orTsbj8aBbKB7vr6xa//k/b6t8d7miWFyoL/qv6DovzBwcHDBlyhS2ojDPsvoeguBrwUhOSoZ574ZfiPn4+OCPP/5AREQE+BZ8xP0bB6FQiPz8fOjri4c11beHoFOnTuDxeBAIqq9nsOvMLuwv2o9f/yMOqhuaYaisrAzR0dEoLS0F35rP7gsOhwNTU1Ok30hHh7EdmnznW3u4Nvi6fLRv377KMMC6Mo01RFMLA9aH5NhjRAwynmbINBiQsB9rj5i/YiASisQFyRoRkBFC6k/ucwh0dXXZiqOk/qormnI/6j4cJztisd9ifF30NYZsblx9gHnz5mHevHl4vu85wo5WTTmYcDuhQfmk5am6vPYcJQ6mP56On+1/xolfT0A4QFhj6lVhuRAXp15Eekg6ClMK5TJ2V0K/oz7y4vLYca8Sj7Y+gvVg63pNSOzfvz927tyJbt26yaRNlamoqMDS0hKZyZlIjEmscb2YmBhERUWhY8eOsLKyqnfKUVnq0aMHtm3bho4dxYWO9Bz1kBOVA5FQVOXCzNraGitXroShoaHCMwxJ8K1rn1jc1IJkEm+njHy46WGdF2Y2NjY4duwY+5hnxUPWy6pVtkdNGYXXZa8xKmoUO0m+vjgcDt5//30A4onFvFIecnNzkZqayga/9Z1D4Ofnh02bNtXY6+Pm5oayjDKU3CtB0v2kKgFBXePa7ezskJSUhEePHomHT73Kx8uXL+Hs7AwwQNSZKAw50PhaLFlZWfjiiy9w9+5dpKSk4PXr141+rbrUVBgQaFha0frwWuOFoI1veiJkGQwErAsAh8MBI2Tw75F/UZpbChM3EwCyywxHCJEm94Dg0KFD8t7EO+ntC+Hr16/j1KlT7PNfn2l8QCBh7m2OW4tvQVguZO9UZmVlYdaRWRg0bBB6VvSEiopiS1XUdNJX1VJF+vvp+HH7jwj5KAT+Ef5V1hEJRbjy4RUkBySjKL0Ilztdxm8nf8P2HtsxaM0gALL9ktRppwMVLRXkROawQ4MK0wrx7JdnmOo/tV6v0blzZ7nO23jx4gWebHqCwtTCGtc5e/YsVq5cialTp+L3X39HXlxesw8Z6tixIxsMAOI77oyIEac/tZVuS+fOndlhc9c/vq6QgODKlSsICAjAoEGD0KdPH/Bt+Oz458pOnDiByMhI6PjroIdPD5ls22uNFx5sfABhmbBRF2Y8Kx7iLsdJLWMYBqJSEZSVlGFiYtKk9mmbaSM/JR98Pl8qA1F9ewjqShpw6NAh9kL4r4l/wXaELXu81ieTmKWlJbKysvD69WvwrHjwD/TH5198jmnTpmHdnHUoF5TDsp9l/d5sNbS0tHD27Fm8fv0aN2/exIgRIxr9WnWRVWHA+qhuWJKsggLJcDgjVyME+AXAxM0EKhoqMs0MRwiRRoXJWqi3L4SnTp0KNzc3GBkZYdeuXThw/ECTt8EYMqjQqEDG0ww2e8vF4xeRXpGOJ9FPFB4M1GXa7Gk4fOQw1KPV8dekvzD65Gj2OUbE4Orsq4i/GQ9de124LnDFviP7EBkZyb4vWX9Jcjgc6DvpIyssiw0IgrcFw2qgFUy6N+2iSlY0NTWh004HaY/SalyHy+Wic+fO6NSpE/Lj88FR4ij8rruyqjJ07XSRHZ5dJSCoLD8xH8au9U8NKSvnz5/HL7/8AmVlZfTp06fGOQQnTpzA2bNnMd18OkZ+OlIm2w76OogNBhpyYVZRUQGRSFTtpOKKkgp8ha8w7fk0GFo0vifj0aNH+OLiF+AWczEYg9nlIqEIglQBuO1kU2PDa40XREIRgtYH4eWvLzF43+Bq75ZX5+rVq+ByueBwOHgS8QSxd2NRXl6OrKwsRJ2Lgt0oOyirNX5ol6amJnbv3g1TU1P06tWr0a9TH81VbEue2ZIqv4bkNR0mOtT78ySENI5crvi6d++OmzdvQk9PD66urrVO8AsJCZFHE945ledbzJ49u8mTJidOnIjTp09jYdeFSA5IZgMCRzhiecfl8Pq65Z9wnZ2dkZiSiCszriDsSBiufXQNQ/YNASNicH3edcRejEVJdgm6L+oOrzVeuDjlImJiYuDq+qa4jay/WAw6GrDZcArTC/Fsz7NGFTOSJ247bq1Zhj755BN88om4EFXc1Tjo2urKJHNIQ6WkpCAjIwPOzs5QU1MTZxqKyIbtcNsq6xkbG0NFRQWCJIFCgpf+/ftDWVkZnp6eAMRDhvJf5Vep9zB8+HDo6epB+zdtmRQla+yF2QcffIBjx45h3759mDhoIgQpAqmewqS7SdAy0YKhs2GTzjUlJSUIDBOnN/7qq68wYcIEuLq6oiijCIyQAde8fgHBzp07ce7cOaxYsaLGO+ze67yRn5CPl4de4sYnN+qVex8Qz+OR4Fnx4M644+bNm/Dw8MCxbsfgs6X+leBrMmlSw4t2tVTNNSzJa40XhOVCPPj6AZ7+8BSiivp9noSQxpFLQDBmzBioq4srio4dO1Yem2jTZJFBpX178RyBUqNSJPsnw32puFpkVmAWxk0aB+8JraNLVklJCSN+H4HS3FL8s/8fqHHVICwXIvJ0JEpzSqW+QDp06IAOHRqWOrChDDoaIOOZOD3io28fwbKfJUzdZFewp6kiIyOxbv86JEQlYBZm1bl+TnQOdDvoyr9h1XB0dIRAIEBkZCQ6dOggrkXwVupRhmHg6OiIkpIShIeHK2wOweTJkzF58mT2Mc+ah7ICcU2Eytlu5s6di7E9x+LI8SNNzqnelAszTU1NiEQiJCUlgWvOBUeJg4KkAuja6AIQB4I2Q22afK7p1asXvvjkCxzYfwD//e9/YWVlBVdXVwiSBdA01ISKev2+gv755x/cuXMHvXr1kgoINm/ejL1792L+/PlYtWoVhh0chrAjYRCVN2xce05ODjQ1NcGzFveW9O3bF5kvM1GQVADrIdaNeevvrOYcltRrQy8EfxPc4M+TENJwcgkI1q5dW+3vpOX44osvsGHDBuQ9y8NfE/9CSUkJ5s2bB50rOlh1ZpWim9dgLjtckJ+RjyffP2GXNefdJEmxHkMXQ4QfD0dhRiFCfwqF7x3fFjUJrri4GMfOHYMWtFBWUAY1HbVa11fEhGIJMzMz5Ofns9ll9J308eLwC6l1cnJyUFZWBqFQCCOeEUrzShU+vAkA1Hnq0NDXQF5cXpX0l1kvs2DgbNDkXpemXJitX78eGzZsgImJCZSUlMBtxxXPz/h/QHD19FWE24YjcnckFixY0Og2cjgcrFyxEhF7I6AzU4edLF/f+QMSM2bMQK9evaoMuYmKisKrV69QVlYG4E29koYMn0pKSmJvkKTEpqAkpwSl+aWIOhMFm6E2UNVqWJ2Xd11zDUsCGvd5EkIap9kGiT9+/BhhYeKMNs7Ozg3OWkFky8hIPMZd010TpTmlOPPrGfz2228YpDwI5WblCm5dw/Tv3x+3b9/G5UuXkTU6C4yQqXI3KSQkBE+fPoWbm5tcMvhIJsF1/093ZEdk49G2R2jfpz1eXX3VoibB2dvbY926dYj+bzTyk/Jh2FF6fHhsbCyGDx8OZ2dnnDlzBjlROVWG6DSXiIgIqTvU1fUQ6Ovro7i4GGlpaSh7XQYVTRVo6NVcgVmeGIZBbm4u9PTEAZQk9aikh6i8vBxlZWXIfJEpk+FCTbkwMzeXTuFYeR5BbmwuolOicSHpAtSN1ZsUEADiScU9RD3w6bZP2WrF9a1BING7d2/07t27yvJvvvkGc+bMgbm5eaOHT6mpqUFdXR0cDgc5xTlQ5aoiPz4fUWei4L7cvYHvlsiKvOcpEEKkyX1gcFJSEnr37g0PDw8sXrwYixcvxnvvvYdevXohKSmpwa+3e/duWFtbQ0NDA56enggODq51/ZMnT8LJyQkaGhpwcXHB5cuXG/tW3kkqGiowcTMBP5ePDwd+iHjteMQm1D9HfUtgb28PVVVVXN99nQ0G3q4zcP78ecydOxc//fSTXNogycsd8kMIROUihHwfAp32Oi1uEpy2tjbWrl2LPlZ9UJhcNdNQUFAQIiIikJIirk+hiKJkEm8PV9Fz1ENRehFK80qllispKcHc3JydP6CIaqZ5eXnQ0tKCvr4+SkrE1b/fnlj86NEjcLlcfLDzAxh2bnrKUVmqHBDEXY1DD7ce2L59O6ZNm9bk11bVUmWrFUvUN+VoXYyNjeHt7Y20I2nVDp+qTw5+Y2NjHJhzACcXnYSzszN4Vjwk3klE5stM2I60rbXqM5GPmobDyaOmAiFETO4Bwdy5c1FeXo6wsDBkZ2cjOzsbYWFhEIlEmDt3boNe68SJE1i2bBnWrl2LkJAQdO3aFUOGDKm2pD0ABAYGYurUqZgzZw6ePn2KsWPHYuzYsXjx4kW167c1AevEJ1aLXhZQjVHFBL0JOLzyMAYNGtSqvgTXrVuHSysvweKyRY3FeGxtbTFkyBC89957cmtH5WI9IqEIz/c+b1HBQGU67XSqnVg8btw43LlzBxs3boSoQoS8uLxmL0pWE019TWgaadZYsViRNQh4PB5bjT0tTZzBiW/DR96rNwGBJMhSLVNVeEBQUFCAHTt24PPPPwcgXa047koc+k7si2XLlslsDpi2mTYEqW8KixUkN6yHABAXqzt37hzi4uKqPFfb8Kn6VAG2NbVF+LZwBH0dBJ4VDyE/hMCynyWe/vi01qrPRD6a+nkSQhqBkTMNDQ0mJCSkyvLHjx8zmpqaDXotDw8PZsGCBexjoVDImJubM5s3b652/cmTJzMjRoyQWubp6cnMmzev3tvMy8tjADB5eXkNamtrELghkNmGbcyFqReYAx0PMLuMdjFJ/kns8sANgYpuYr3U1F5FvY/tKtuZbdjG7FDb0azbrYv/Wn8mcEMgIxAImN0jdjMnlpxgnwvcEMj4r/WXWj8nNofZrrKdEZYLm7upDMMwzMWLF5n333+f2b17N7vsaK+jzItfX7CPf/zxR2bJkiXMw4cPGf+1/szlGZcV0VSGYRgmPj6eKS4uZh+H7AphTg0/JbVOVnoWs4azhsmLV+z5JD8/nwHAAGDy8/OZZ/ueMX8O/JMpLylnvtP6jsl4niHT7Z3of4J5fvD5m8cDTjDP9j1r0GuMHj2aAcDs3LmTYRiGSU1NZb777jvm6tWrMmmj5Hzxu8fvzDZsY86MOtOqzoPk3fYuX4uQlkHutz3at2+P8vKqY9KFQmGVcay1KSsrw5MnTzBw4EB2mZKSEgYOHIigoOq7D4OCgqTWB4AhQ4bUuD4AlJaWIj8/X+rnXSW52xJ+LBzZYdmoKKrAqxuvWtwwl7pUvpvE/P8uLaCYu0lBXwdBVCGqdtiSoknmOSwasQgLLi3A4auHAbzpnn/7LmhuVC74NnwoqSjm7mhUVBSOHj2K+/fvs8skqUclTp8+je+//x5RUVEKr1JsaWkJDY038xckcwgqq0ipgCHXUOETn3V0dDBz5kysXLkSQqGQHTKUfD8ZGnoayFLNQnJyMioqKmSyPW0zbalieIJkAXTaNWwfeHh4wNXVFVyuuGchNDQUS5cuxcqVK2XSRsn5Ii1Y3MMTcyGmVZ0HCSGkKeT+Tb9t2zYsWrQIjx8/Zpc9fvwYixcvxrffflvv18nMzIRQKKxSNdPExITton9bWlpag9YHxGnsJBU1+Xw+m33iXSX5EgSA8qJyBK0PanVfgt7rvKE/WR/jxo3DkCHS1Zt7fNUD8Q7xKCoqkns7Ko97rW7YkqJJPuuiu0XgqnIhKhJJtVl1mCq2bduGp0+fAvh/ylEFzR8AgL59+2L79u2YPXs2u+zticVz5szB8uXL4ebmprAaBDXhWfOQ9ypPKkjNfJkJw85Ny+0vK4cOHcLWrVuhq6srDggS8hF7ORbtB7eHh4cH2rVrJ7PhlVxzLgQpb4YMNTTLEAB8+eWXCAkJYY8HPp+PiRMnYtCgQTJpIyD+H1FSFX8tUppLQkhbIvcsQzNnzkRRURE8PT3ZCrEVFRVQUVHB7Nmzpb7ss7OrHxvcnFavXo1ly5axj/Pz89tEUBC4PrDa7DytBZfLxblz58DhcJCRkQFjY3G12tu3b2PatGmwtLREdHQ0VFXlk0KwuYr1NJXXGi+IRCK4r3MHEsRt675YXLjtiy++wObNmzGy+0hceHJBnHK00vyB5k6f2q1btyoZod5OPfrBBx/ggw8+AAAEJgYqNCAIDAzEpUuX0KVLF/j6+oJnxUNFUQWKXhdB21gbW7ZsQeSlSPhYNr3QlaxI0uW+t/I9CEuFCDsaBqe1TrAMtkRSXBIKzhYA3Zq+Ha45F0n3xUkkSvNLUVZQ1uCA4G1eXl44efJk0xtXCaW5JIS0VXIPCL7//nuZvI6hoSGUlZWRnp4utTw9PR2mptUXfjI1NW3Q+gCgrq7OFlVrK4K+DqqSnae1fQlaWFjgp59+gqenJ5tSFRDn3be1tcWwYcPkFgwAzVusp6m813ojeFMwhGVCQAkI/SkUaY/SoOusi94OvaEboougr4OQG50Lq0FWAKQDHkXSd9RHblQuREKR1BAnhmHEQ4YaOAxFlh48eIBNmzZhypQp8PX1hZq2GrSMtZD/Kh/axtr46aefkJCQAJ8lLScgEEGE2363AQBaJloozixGz6k9sS9zH2763WRv4jSVtpk220MgSBZARaNp6WGZtypAywKluSSEtGVyDwhmzJghk9dRU1ODm5sbbt68yWa+EIlEuHnzJhYuXFjt33h5eeHmzZtYsmQJu+zGjRvw8qKTu8S79CU4f/78KstGjBiBoUOHyn3IUHMW62mqoK+DICwTsgGg+yp36LTTAfM7g3GvxoHvzEeAXwA0DDTQdX7Xans/motAIMDz589hamoKW1tb8G34YBgG+a/yIdIToaCgAO3atUOFoALlgnKF9hB4enpiwYIFUucXSepRMw8zfPzxx7j6zVV09u6ssDZWtn37dqz6ehVGuo2Ehp8GuBZcmHuZs5l1BmwYILPPu/KQIclwocZc0H/22Wc4evQotm7dCl9fXygpyWbUa2vp4SOEEHmR+xyCkJAQ/PPPP+zj8+fPY+zYsfjiiy/Y6pL1tWzZMuzbtw+//vorwsLCMH/+fBQWFmLWrFkAgA8//BCrV69m11+8eDGuXr2K7du3Izw8HOvWrcPjx49rDCDamraS61lZWRk6Oi1nbLkiST7ziH4RuNjrIrRmayH4m2CUZJdg2oNpmPF8BhzGOUCNr4aSrBKcG3tOoZPMFy5cCG9vb/z2228AACUVJeja6yIrPAvHjx+HtbU1Jk6ciPzEfKhyxfnuFcXb2xu7du2Syt1fOfXoysUrMUYwBk69nBTVRCl6enoQiUSoMKmA9wZvCJIFSApMgr+fv8w/b8mkYoZhGpVyVCI/Px9JSUl49uwZjIyMYGdnh8TExCa3j9JcEkLaOrkHBPPmzUNkZCQAcRVUX19faGlp4eTJk1i1alWDXsvX1xfffvst/Pz80K1bN4SGhuLq1avsxOGEhASkpqay6/fs2RNHjx7F3r170bVrV5w6dQrnzp1D584t4w6dor2LX4IhISH48ssvcfr0ady8eVNqQmdbVzkAzNDJwK1bt8B0Z+C9wRuX/C7h1ppb0HfUR6+NvbAoexGUVJUgKhMpdF6Jq6srzMzMoKyszC4zcDJATkQO8vLyoKqqChsbGzbDUEuYrFsZ35qP/DhxpqGsf7OgaaAJLRMtBbdKbOLEiUhNTcWFCxfgtcYLymrK+Ff0LzZhE66UX5HptrTNtCEqF6E4q7hRE4olFixYgPv37+Pjjz9GdnY2YmNjpYYINpb3upoDIK81Xs02b4YQQhSFw8j5ionP5yMkJAR2dnbYsmULbt26hWvXriEgIABTpkyRyd0decrPzwefz0deXh54PJ6im0Pq8NFHH2H//v3s43nz5mHPnj0KbFHLIZlA6rXGC9euXUNGRga8vb1ha2uL3h17IygyCIcOH8L06dPZ4EEyrEhRPQRCoVAqGACAe6vvoSS7BIN/GQyhUIjS0lJEHYlC5KlITLo+qdnbWBnDMMjOzgafz4eKigqe/fIMUWejMPTUULz8/SXiTsRhyp0pCm1jdSSf93ml8/AX+WPce+NwJviMTLfxo+6PmHJ/Cp7teQYVLRX03da30a8lEomQlpaGhIQE9OjRQ3aNJKSFomsRIm9y7yFgGAYikfhO899//43hw4cDENcnyMzMlPfmSRszceJETJ48GZ07d4ampiZGjx6t6Ca1GJXvgg4ZMgTTp0+Hra0tAECgIYBQJISDg0OLSp/6djAA/L8Wwf9TjyorK0NLSwsFSYqtQSBhaWkJQ0NDhIWFAXgzh+DQoUPo8WkP7E/ZX8crNL/Kn/e1gmvYMX0HrB9Zy/zzlkwsbkwNgrcpKSnB3NycggFCCJERuU8qdnd3x8aNGzFw4EDcvXsXP//8MwAgLi6uSo0AQppqyJAhbC2CrKws6OvrK7hFrcPTp0+RkJCA+IPxeLj+YYueXKnvKF2cDAAKEgvAs1L8XTN9fX0kJSUhPT0dLi4u4uJk8fnITBPf/DBp17LOeStHrcTDiw+xbPky9nNd+ttSBHWQfXIBrjkXhamFTZpDAAABAQEIDAzE0KFD4eLiIpO2EUJIWyf3HoLvv/8eISEhWLhwIb788kvY29sDAE6dOoWePXvKe/OkDTMwMGhxY8pbCqFQiJcvX+LChQvsPAtLS0soQanFzSv55Zdf4O7ujp07dwIQBwSv019j9MjRWLFiRYtIOSpx/fp1lJSUsBXSeZY8CEuFWL1wNXaY7cDnyz9XcAulnX54GvdxH9pDtKWWy+PzluohsGj8Z/Xdd99h1apVGDVqFDs/jRBCSNPIvYegS5cuUlmGJLZt21btcABCiPwJhUK4uLiAYRipit4tMX1qVlYWnjx5AgcHBwCAhp4GCvUKceHSBViEWuDbb79lJxUr2tu9nioaKtA200bG0wwIU4Ww97JXUMuq9/Ey8eRcCwsLbN++HSoqKpg0aRLMzc1l/nlzzbkoSChAYVphk3oIBg0ahNOnTyM+Ph7BwcHscUEIIaTx5B4Q1ERDo/FFaQghTaOmpoYuXbpAWVkZPj4+GDBgANasWQMzMzNFN62KCRMmwMHBAe+99x67zLqDNb5w+AKmHqbiHoIWModAori4GOnp6bC2tgbfho/YS7HQNtOGpr6mopsm5fPPxT0WIpEIffv2xevXr+Hq6gpzc3OZb0vbTBuJdxLZ3xtr3rx5yM3NxaNHjyhjHCGEyIjCAgJCiGKFhoYiLS0NZmZmiIqKwubNmxXdpGo5OjrC0dFRapl9V3u4GLqg96LeKM4uRkVRBXjtFT+HAABevnyJ8ePHQ11dHY8ePQLPiocNv22AmbEZPiz8ENrajb8YlpeysjKsXLkS9+7dk9tEXa45F6+fvYaWsRaUVZvWO/zZZ5/JqFWEEEIACggIadN0dHRw+vRpREVFgc/nK7o59abnqIeUgBQA4gnFajw1qOmoKbhVYsbGxshIyoCKsgpiY2PBmDC4L7gPCIDfVX9H0NdB4hogLSS3fUVFBQQCAVauXImVK1fKbTtccy6EZcImzR8ghBAiHxQQENKGaWtrY/z48YpuRp1iY2Ph7+8PW1tb9OrVC+lq6Yh6EQWhUNhi5g9IGBkZYdu0bUjZl4LcU7nQtdbFIAyCyUATPNnyhE3x2RLcuXMHAwYMgLOzc7VzvWRBUv/Caaq4QnPl+QMtLTgihJC2Su5ZhgghLdOTJ08wcOBATJ48WdFNqdPBgwcxY8YMHD58GAAw/5v5WBm1EkEBQS1u/gAAzN07F4M2DEKAXwAEoQIMxmBMt53OBgOKTt0qYWxsDJFIhBcvXqC0tFQu21BSVkKAXwBe/voSAMBtJw4IJPUPlJTpa4gQQhRN7j0EQqEQhw8fxs2bN5GRkcEWKZO4deuWvJtACKlGRUUFbt68CSMjIzAM06JTtHp5ecHHxwfOzs4QCoXQ4mpBCUowVDXE68TXLWb+QGWSi/7f/H5DClKAvWhRwQAAODg44I8//sC0adPQqVMnREdHy3wbletYKKsrQ8dCR6oYWkvaH4QQ0lbJPSBYvHgxDh8+jBEjRqBz584t+qKDkLakW7du6NatG8zNzVv8/+WIESMwYsQI9nFERAQOOB+ASpYKChILoGuvq7jG1UJ7jDb2+O0BF1y4qbq1uItfFRUVKCsrw9TUVK51YSoHBQHrAsBUMBQMEEJICyL3gOD48eP4888/MXz4cHlvihDSAOrq6nj69Kmim9Fohh0NkRORg4KkArTv117RzalW4flCuMAF/+Af/Fv+L4K+DmpxF8G+vr6YPHkyBAKBXLfjtcYLQRuCIKoQQVlNucXtB0IIacvkHhCoqamx1YkJIaQpGIZBSUkJNDU1oe+oLw4IEgta5JAhybCYn9b9BM2Rmii+WIwAvwAAiivyVhMOhwMdHfnOwwj6+k0wICwTtsjgiBBC2iq5z+Zavnw5du7cCYZh5L0pQsg7bPfu3dDX14eWlhbGjx+P0KJQZIVlQZAkYCeqthSVx8j3WtsLbm5u6LW2F7w3eCPALwBBXwcpuonNqvL+WFq6tM3uB0IIaank3kPg7++P27dv48qVK+jUqRNUVVWlnj9z5oy8m0AIeQfo6OggNzcXAHD27Fl0X9YdwidCVJRUtLgsQyKhqNox8pLHIqGouj97J1U3gbjynILKjwkhhCiG3AMCXV1djBs3Tt6bIYS840aMGIHQ0FDc3XEXrzJfYfjE4bi14xY09DSgpi0uStZS8trXtv22dvFLwREhhLR8cg8IDh06JO9NEELaAAMDAxgYGKDIvghlv5WhtEcptEy0oG2iDUD6TjRpOSg4IoSQlo8qFRNCWpXKw014Vjxw23Eprz0hhBDSBM0SEJw6dQp//vknEhISUFZWJvVcSEhIczSBEPIOuH79OjZt2oRhw4bBZ4MPAvwCUJBYgLjLcRQMEEIIIY0k9yxDP/zwA2bNmgUTExM8ffoUHh4eMDAwQGxsLIYNGybvzRNC3iEXLlzA3bt38fnnn8NrjReU1JTAiBjKa08IIYQ0gdwDgp9++gl79+7Fjz/+CDU1NaxatQo3btzAf/7zH+Tl5cl784SQd8jGjRsxa9Ys/P333+IJxGXSee0JIYQQ0nByDwgSEhLQs2dPAICmpiYKCgoAANOnT8exY8fkvXlCyDuEz+fj4MGD0ArUorz2hBBCiIzIfQ6BqakpsrOzYWVlBUtLSzx48ABdu3ZFXFwcFSsjhDQY5bUnhBBCZEvuAUH//v3x119/wdXVFbNmzcLSpUtx6tQpPH78GOPHj5f35gkh7xjKa08IIYTIFoeR8216kUgEkUgEFRVx7HH8+HEEBgaiQ4cOmDdvHtTU1OS5+SbLy8uDrq4uEhMTwePxFN0cQgghhLQx+fn5aN++PXJzc8Hn8xXdHPIOkntA0NolJSWhffv2im4GIYQQQtq4xMREtGvXTtHNIO+gZgkI7t+/j19++QUxMTE4deoULCws8Pvvv8PGxga9evWS9+abRCQSISUlBTo6OuBwOHLZhiTyp16IpqN9KTu0L2WD9qPs0L6UHdqXstMc+5JhGBQUFMDc3BxKSnLPB0PaILnPITh9+jSmT5+OadOm4enTpygtLQUgHoqzadMmXL58Wd5NaBIlJaVmi8Z5PB6dmGWE9qXs0L6UDdqPskP7UnZoX8qOvPclDRUi8iT3MHPjxo3Ys2cP9u3bB1VVVXa5t7c3VSkmhBBCCCFEweQeEERERMDHx6fKcj6fj9zcXHlvnhBCCCGEEFILuQcEpqamiI6OrrLc398ftra28t58q6Curo61a9dCXV1d0U1p9Whfyg7tS9mg/Sg7tC9lh/al7NC+JO8CuU8q3rx5M44cOYKDBw9i0KBBuHz5MuLj47F06VKsWbMGixYtkufmCSGEEEIIIbWQ+6Tizz//HCKRCAMGDEBRURF8fHygrq6OFStWUDBACCGEEEKIgjVbHYKysjJER0dDIBDA2dkZXC63OTZLCCGEEEIIqQUVJiOEEEIIIaQNk9uQodmzZ9drvYMHD8qrCYQQQgghhJA6yC3L0OHDh3H79m3k5uYiJyenxp+2bvfu3bC2toaGhgY8PT0RHBys6Ca1OuvWrQOHw5H6cXJyUnSzWoV79+5h1KhRMDc3B4fDwblz56SeZxgGfn5+MDMzg6amJgYOHIioqCjFNLaFq2tfzpw5s8pxOnToUMU0toXbvHkz3nvvPejo6MDY2Bhjx45FRESE1DolJSVYsGABDAwMwOVyMWHCBKSnpyuoxS1TffZj3759qxyXn3zyiYJa3HL9/PPP6NKlC1t8zMvLC1euXGGfp+ORtHZyCwjmz5+PvLw8xMXFoV+/fjhw4ADOnj1b5actO3HiBJYtW4a1a9ciJCQEXbt2xZAhQ5CRkaHoprU6nTp1QmpqKvvj7++v6Ca1CoWFhejatSt2795d7fNbt27FDz/8gD179uDhw4fQ1tbGkCFDUFJS0swtbfnq2pcAMHToUKnj9NixY83Ywtbj7t27WLBgAR48eIAbN26gvLwcgwcPRmFhIbvO0qVLceHCBZw8eRJ3795FSkoKxo8fr8BWtzz12Y8A8NFHH0kdl1u3blVQi1uudu3a4ZtvvsGTJ0/w+PFj9O/fH2PGjMHLly8B0PFI3gGMHJWUlDBHjx5lBg4cyGhpaTGTJk1irl69yohEInluttXw8PBgFixYwD4WCoWMubk5s3nzZgW2qvVZu3Yt07VrV0U3o9UDwJw9e5Z9LBKJGFNTU2bbtm3sstzcXEZdXZ05duyYAlrYery9LxmGYWbMmMGMGTNGIe1p7TIyMhgAzN27dxmGER+HqqqqzMmTJ9l1wsLCGABMUFCQoprZ4r29HxmGYfr06cMsXrxYcY1qxfT09Jj9+/fT8UjeCXItTKauro6pU6fixo0b+Pfff9GpUyd8+umnsLa2hkAgkOemW7yysjI8efIEAwcOZJcpKSlh4MCBCAoKUmDLWqeoqCiYm5vD1tYW06ZNQ0JCgqKb1OrFxcUhLS1N6hjl8/nw9PSkY7SR7ty5A2NjYzg6OmL+/PnIyspSdJNahby8PACAvr4+AODJkycoLy+XOjadnJxgaWlJx2Yt3t6PEn/88QcMDQ3RuXNnrF69GkVFRYpoXqshFApx/PhxFBYWwsvLi45H8k6Qex0CCSUlJXA4HDAMA6FQ2FybbbEyMzMhFAphYmIitdzExATh4eEKalXr5OnpicOHD8PR0RGpqalYv349evfujRcvXkBHR0fRzWu10tLSAKDaY1TyHKm/oUOHYvz48bCxsUFMTAy++OILDBs2DEFBQVBWVlZ081oskUiEJUuWwNvbG507dwYgPjbV1NSgq6srtS4dmzWrbj8CwPvvvw8rKyuYm5vj+fPn+OyzzxAREYEzZ84osLUt0z///AMvLy+UlJSAy+Xi7NmzcHZ2RmhoKB2PpNWTa0BQWlqKM2fO4ODBg/D398fIkSOxa9cuDB06FEpKcu2cIG3IsGHD2N+7dOkCT09PWFlZ4c8//8ScOXMU2DJC3pgyZQr7u4uLC7p06QI7OzvcuXMHAwYMUGDLWrYFCxbgxYsXNC+oiWrajx9//DH7u4uLC8zMzDBgwADExMTAzs6uuZvZojk6OiI0NBR5eXk4deoUZsyYgbt37yq6WYTIhNyuyj/99FOYmZnhm2++wciRI5GYmIiTJ09i+PDhFAwAMDQ0hLKycpUsBOnp6TA1NVVQq94Nurq6cHBwQHR0tKKb0qpJjkM6RuXD1tYWhoaGdJzWYuHChbh48SJu376Ndu3asctNTU1RVlaG3NxcqfXp2KxeTfuxOp6engBAx2U11NTUYG9vDzc3N2zevBldu3bFzp076Xgk7wS5XZnv2bMHPB4Ptra2uHv3Lj7++GOMHz++yk9bpaamBjc3N9y8eZNdJhKJcPPmTXh5eSmwZa2fQCBATEwMzMzMFN2UVs3GxgampqZSx2h+fj4ePnxIx6gMJCUlISsri47TajAMg4ULF+Ls2bO4desWbGxspJ53c3ODqqqq1LEZERGBhIQEOjYrqWs/Vic0NBQA6LisB5FIhNLSUjoeyTtBbkOGPvzwQ3A4HHm9/Dth2bJlmDFjBtzd3eHh4YHvv/8ehYWFmDVrlqKb1qqsWLECo0aNgpWVFVJSUrB27VooKytj6tSpim5aiycQCKTuBMbFxSE0NBT6+vqwtLTEkiVLsHHjRnTo0AE2NjZYs2YNzM3NMXbsWMU1uoWqbV/q6+tj/fr1mDBhAkxNTRETE4NVq1bB3t4eQ4YMUWCrW6YFCxbg6NGjOH/+PHR0dNhx2Hw+H5qamuDz+ZgzZw6WLVsGfX198Hg8LFq0CF5eXujRo4eCW99y1LUfY2JicPToUQwfPhwGBgZ4/vw5li5dCh8fH3Tp0kXBrW9ZVq9ejWHDhsHS0hIFBQU4evQo7ty5g2vXrtHxSN4NCs5y1Ob9+OOPjKWlJaOmpsZ4eHgwDx48UHSTWh1fX1/GzMyMUVNTYywsLBhfX18mOjpa0c1qFW7fvs0AqPIzY8YMhmHEqUfXrFnDmJiYMOrq6syAAQOYiIgIxTa6haptXxYVFTGDBw9mjIyMGFVVVcbKyor56KOPmLS0NEU3u0Wqbj8CYA4dOsSuU1xczHz66aeMnp4eo6WlxYwbN45JTU1VXKNboLr2Y0JCAuPj48Po6+sz6urqjL29PbNy5UomLy9PsQ1vgWbPns1YWVkxampqjJGRETNgwADm+vXr7PN0PJLWjsMwDNOcAQghhBBCCCGk5aDZvYQQQgghhLRhFBAQQgghhBDShlFAQAghhBBCSBtGAQEhhBBCCCFtGAUEhBBCCCGEtGEUEBBCCCGEENKGUUBACCGEEEJIG0YBASGENNHMmTOpejMhhJBWS0XRDSCEkJaMw+HU+vzatWuxc+dOUI1HQgghrRUFBIQQUovU1FT29xMnTsDPzw8RERHsMi6XCy6Xq4imEUIIITJBQ4YIIaQWpqam7A+fzweHw5FaxuVyqwwZ6tu3LxYtWoQlS5ZAT08PJiYm2LdvHwoLCzFr1izo6OjA3t4eV65ckdrWixcvMGzYMHC5XJiYmGD69OnIzMxs5ndMCCGkraGAgBBC5ODXX3+FoaEhgoODsWjRIsyfPx+TJk1Cz549ERISgsGDB2P69OkoKioCAOTm5qJ///5wdXXF48ePcfXqVaSnp2Py5MkKfieEEELedRQQEEKIHHTt2hVfffUVOnTogNWrV0NDQwOGhob46KOP0KFDB/j5+SErKwvPnz8HAOzatQuurq7YtGkTnJyc4OrqioMHD+L27duIjIxU8LshhBDyLqM5BIQQIgddunRhf1dWVoaBgQFcXFzYZSYmJgCAjIwMAMCzZ89w+/btaucjxMTEwMHBQc4tJoQQ0lZRQEAIIXKgqqoq9ZjD4Ugtk2QvEolEAACBQIBRo0Zhy5YtVV7LzMxMji0lhBDS1lFAQAghLUD37t1x+vRpWFtbQ0WFTs2EEEKaD80hIISQFmDBggXIzs7G1KlT8ejRI8TExODatWuYNWsWhEKhoptHCCHkHUYBASGEtADm5uYICAiAUCjE4MGD4eLigiVLlkBXVxdKSnSqJoQQIj8chsprEkIIIYQQ0mbRbSdCCCGEEELaMAoICCGEEEIIacMoICCEEEIIIaQNo4CAEEIIIYSQNowCAkIIIYQQQtowCggIIYQQQghpwyggIIQQQgghpA2jgIAQQgghhJA2jAICQgghhBBC2jAKCAghhBBCCGnDKCAghBBCCCGkDaOAgBBCCCGEkDbsf8eMvXM6oqFZAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -675,18 +666,18 @@
       "Trotter step with Lie-Trotter\n",
       "-----------------------------\n",
       "\n",
-      "                  Depth: 7\n",
-      "             Gate count: 17\n",
-      "    Nonlocal gate count: 5\n",
-      "         Gate breakdown: RZ: 6, RX: 6, RZZ: 5\n",
+      "                  Depth: 17\n",
+      "             Gate count: 27\n",
+      "    Nonlocal gate count: 10\n",
+      "         Gate breakdown: CX: 10, U1: 6, R: 6, RZ: 5\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1123.61x535.111 with 1 Axes>"
+       "<Figure size 1541.66x535.111 with 1 Axes>"
       ]
      },
      "execution_count": 19,
@@ -752,19 +743,19 @@
       "Trotter step with Suzuki Trotter (2nd order)\n",
       "--------------------------------------------\n",
       "\n",
-      "                  Depth: 14\n",
-      "             Gate count: 33\n",
-      "    Nonlocal gate count: 10\n",
-      "         Gate breakdown: RZ: 12, RX: 11, RZZ: 10\n",
+      "                  Depth: 34\n",
+      "             Gate count: 53\n",
+      "    Nonlocal gate count: 20\n",
+      "         Gate breakdown: CX: 20, U1: 12, R: 11, RZ: 10\n",
       "\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 2126.94x535.111 with 1 Axes>"
+       "<Figure size 2210.55x1120.39 with 1 Axes>"
       ]
      },
      "execution_count": 20,
@@ -795,7 +786,7 @@
     "\"\"\"\n",
     ")\n",
     "\n",
-    "# And finall\n",
+    "# And finally\n",
     "circuit.draw(\"mpl\")"
    ]
   },
@@ -815,10 +806,10 @@
       "Trotter step with Suzuki Trotter (4th order)\n",
       "--------------------------------------------\n",
       "\n",
-      "                  Depth: 70\n",
-      "             Gate count: 165\n",
-      "    Nonlocal gate count: 50\n",
-      "         Gate breakdown: RZ: 60, RX: 55, RZZ: 50\n",
+      "                  Depth: 170\n",
+      "             Gate count: 265\n",
+      "    Nonlocal gate count: 100\n",
+      "         Gate breakdown: CX: 100, U1: 60, R: 55, RZ: 50\n",
       "\n",
       "\n"
      ]
@@ -863,7 +854,9 @@
    "source": [
     "from qiskit.synthesis import SuzukiTrotter\n",
     "\n",
-    "trotter = TrotterQRTE(SuzukiTrotter(order=4), num_timesteps=num_timesteps, estimator=Estimator())\n",
+    "trotter = TrotterQRTE(\n",
+    "    SuzukiTrotter(order=4), num_timesteps=num_timesteps, estimator=StatevectorEstimator()\n",
+    ")\n",
     "problem = TimeEvolutionProblem(\n",
     "    H,\n",
     "    initial_state=initial_state,\n",
@@ -890,7 +883,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -948,17 +941,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7ff5c8c89b10>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1400x200 with 2 Axes>"
       ]
@@ -984,7 +967,8 @@
     "plt.yticks(np.arange(L))\n",
     "plt.ylabel(\"Site $i$\")\n",
     "plt.xlabel(\"Time\")\n",
-    "plt.colorbar(label=\"$\\\\langle Z_i \\\\rangle$\", aspect=1.8)"
+    "plt.colorbar(label=\"$\\\\langle Z_i \\\\rangle$\", aspect=1.8)\n",
+    "plt.show()"
    ]
   },
   {
@@ -1010,7 +994,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.0</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td colspan='2'>Mon Feb 19 11:25:35 2024 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>2.0.2</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.4.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.13.3</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td colspan='2'>Tue Jun 17 10:45:23 2025 CEST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1022,7 +1006,7 @@
     {
      "data": {
       "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1056,7 +1040,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/qiskit_algorithms/algorithm_job.py b/qiskit_algorithms/algorithm_job.py
index b1215574..0ec841bc 100644
--- a/qiskit_algorithms/algorithm_job.py
+++ b/qiskit_algorithms/algorithm_job.py
@@ -20,26 +20,6 @@ class AlgorithmJob(PrimitiveJob):
     """
     This class is introduced for typing purposes and provides no
     additional function beyond that inherited from its parents.
-
-    Update: :meth:`AlgorithmJob.submit()` method added. See its
-    documentation for more info.
     """
 
-    def submit(self) -> None:
-        """
-        Submit the job for execution.
-
-        For V1 primitives, Qiskit ``PrimitiveJob`` subclassed JobV1 and defined ``submit()``.
-        ``PrimitiveJob`` was updated for V2 primitives, no longer subclasses ``JobV1``, and
-        now has a private ``_submit()`` method, with ``submit()`` being deprecated as of
-        Qiskit version 0.46. This maintains the ``submit()`` for ``AlgorithmJob`` here as
-        it's called in many places for such a job. An alternative could be to make
-        0.46 the required minimum version and alter all algorithm's call sites to use
-        ``_submit()`` and make this an empty class again as it once was. For now this
-        way maintains compatibility with the current min version of 0.44.
-        """
-        # TODO: Considering changing this in the future - see above docstring.
-        try:
-            super()._submit()
-        except AttributeError:
-            super().submit()
+    pass
diff --git a/qiskit_algorithms/amplitude_amplifiers/grover.py b/qiskit_algorithms/amplitude_amplifiers/grover.py
index 56fd2ad1..3ebdb34f 100644
--- a/qiskit_algorithms/amplitude_amplifiers/grover.py
+++ b/qiskit_algorithms/amplitude_amplifiers/grover.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -18,16 +18,14 @@
 from typing import Any
 
 import numpy as np
-
 from qiskit import ClassicalRegister, QuantumCircuit
-from qiskit.primitives import BaseSampler
-from qiskit.quantum_info import Statevector
+from qiskit.primitives import BaseSamplerV2
 
 from qiskit_algorithms.exceptions import AlgorithmError
 from qiskit_algorithms.utils import algorithm_globals
-
 from .amplification_problem import AmplificationProblem
 from .amplitude_amplifier import AmplitudeAmplifier, AmplitudeAmplifierResult
+from ..custom_types import Transpiler
 
 
 class Grover(AmplitudeAmplifier):
@@ -116,7 +114,10 @@ def __init__(
         iterations: list[int] | Iterator[int] | int | None = None,
         growth_rate: float | None = None,
         sample_from_iterations: bool = False,
-        sampler: BaseSampler | None = None,
+        sampler: BaseSamplerV2 | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         r"""
         Args:
@@ -136,6 +137,11 @@ def __init__(
                 powers of the Grover operator, a random integer sample between 0 and smaller value
                 than the iteration is used as a power, see [1], Section 4.
             sampler: A Sampler to use for sampling the results of the circuits.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
 
         Raises:
             ValueError: If ``growth_rate`` is a float but not larger than 1.
@@ -165,9 +171,11 @@ def __init__(
         self._sampler = sampler
         self._sample_from_iterations = sample_from_iterations
         self._iterations_arg = iterations
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
 
     @property
-    def sampler(self) -> BaseSampler | None:
+    def sampler(self) -> BaseSamplerV2 | None:
         """Get the sampler.
 
         Returns:
@@ -176,7 +184,7 @@ def sampler(self) -> BaseSampler | None:
         return self._sampler
 
     @sampler.setter
-    def sampler(self, sampler: BaseSampler) -> None:
+    def sampler(self, sampler: BaseSamplerV2) -> None:
         """Set the sampler.
 
         Args:
@@ -234,23 +242,29 @@ def amplify(self, amplification_problem: AmplificationProblem) -> "GroverResult"
             # sample from [0, power) if specified
             if self._sample_from_iterations:
                 power = algorithm_globals.random.integers(power)
+
             # Run a grover experiment for a given power of the Grover operator.
-            if self._sampler is not None:
-                qc = self.construct_circuit(amplification_problem, power, measurement=True)
-                job = self._sampler.run([qc])
-
-                try:
-                    results = job.result()
-                except Exception as exc:
-                    raise AlgorithmError("Sampler job failed.") from exc
-
-                num_bits = len(amplification_problem.objective_qubits)
-                circuit_results: dict[str, Any] | Statevector | np.ndarray = {
-                    np.binary_repr(k, num_bits): v for k, v in results.quasi_dists[0].items()
-                }
-                top_measurement, max_probability = max(
-                    circuit_results.items(), key=lambda x: x[1]  # type: ignore[union-attr]
-                )
+            qc = self.construct_circuit(amplification_problem, power, measurement=True)
+
+            if self._transpiler is not None:
+                qc = self._transpiler.run(qc, **self._transpiler_options)
+
+            job = self._sampler.run([qc])
+
+            try:
+                results = job.result()
+            except Exception as exc:
+                raise AlgorithmError("Sampler job failed.") from exc
+
+            circuit_results = getattr(results[0].data, qc.cregs[0].name)
+            circuit_results = {
+                label: value / circuit_results.num_shots
+                for label, value in circuit_results.get_counts().items()
+            }
+
+            top_measurement, max_probability = max(
+                circuit_results.items(), key=lambda x: x[1]  # type: ignore[union-attr]
+            )
 
             all_circuit_results.append(circuit_results)
 
diff --git a/qiskit_algorithms/amplitude_estimators/ae.py b/qiskit_algorithms/amplitude_estimators/ae.py
index 6444b4b2..1e037743 100644
--- a/qiskit_algorithms/amplitude_estimators/ae.py
+++ b/qiskit_algorithms/amplitude_estimators/ae.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,17 +13,21 @@
 """The Quantum Phase Estimation-based Amplitude Estimation algorithm."""
 
 from __future__ import annotations
-from collections import OrderedDict
+
 import warnings
+from collections import OrderedDict
+from typing import Any
+
 import numpy as np
-from scipy.stats import chi2, norm
+from qiskit import QuantumCircuit, ClassicalRegister
+from qiskit.primitives import BaseSamplerV2, StatevectorSampler
 from scipy.optimize import bisect
+from scipy.stats import chi2, norm
 
-from qiskit import QuantumCircuit, ClassicalRegister
-from qiskit.primitives import BaseSampler, Sampler
-from .amplitude_estimator import AmplitudeEstimator, AmplitudeEstimatorResult
 from .ae_utils import pdf_a, derivative_log_pdf_a, bisect_max
+from .amplitude_estimator import AmplitudeEstimator, AmplitudeEstimatorResult
 from .estimation_problem import EstimationProblem
+from ..custom_types import Transpiler
 from ..exceptions import AlgorithmError
 
 
@@ -66,7 +70,10 @@ def __init__(
         num_eval_qubits: int,
         phase_estimation_circuit: QuantumCircuit | None = None,
         iqft: QuantumCircuit | None = None,
-        sampler: BaseSampler | None = None,
+        sampler: BaseSamplerV2 | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         r"""
         Args:
@@ -77,6 +84,10 @@ def __init__(
             iqft: The inverse quantum Fourier transform component, defaults to using a standard
                 implementation from `qiskit.circuit.library.QFT` when None.
             sampler: A sampler primitive to evaluate the circuits.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
 
         Raises:
             ValueError: If the number of evaluation qubits is smaller than 1.
@@ -94,8 +105,11 @@ def __init__(
         self._pec = phase_estimation_circuit
         self._sampler = sampler
 
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options else {}
+
     @property
-    def sampler(self) -> BaseSampler | None:
+    def sampler(self) -> BaseSamplerV2 | None:
         """Get the sampler primitive.
 
         Returns:
@@ -104,7 +118,7 @@ def sampler(self) -> BaseSampler | None:
         return self._sampler
 
     @sampler.setter
-    def sampler(self, sampler: BaseSampler) -> None:
+    def sampler(self, sampler: BaseSamplerV2) -> None:
         """Set sampler primitive.
 
         Args:
@@ -149,6 +163,9 @@ def construct_circuit(
             circuit.add_register(cr)
             circuit.measure(list(range(self._m)), list(range(self._m)))
 
+        if self._transpiler is not None:
+            circuit = self._transpiler.run(circuit, **self._transpiler_options)
+
         return circuit
 
     def evaluate_measurements(
@@ -288,8 +305,10 @@ def estimate(self, estimation_problem: EstimationProblem) -> "AmplitudeEstimatio
                 "The state_preparation property of the estimation problem must be set."
             )
         if self._sampler is None:
-            warnings.warn("No sampler provided, defaulting to Sampler from qiskit.primitives")
-            self._sampler = Sampler()
+            warnings.warn(
+                "No sampler provided, defaulting to StatevectorSampler from qiskit.primitives"
+            )
+            self._sampler = StatevectorSampler()
 
         if estimation_problem.objective_qubits is None:
             raise ValueError("The objective_qubits property of the estimation problem must be set.")
@@ -307,23 +326,17 @@ def estimate(self, estimation_problem: EstimationProblem) -> "AmplitudeEstimatio
         result.post_processing = estimation_problem.post_processing  # type: ignore[assignment]
 
         circuit = self.construct_circuit(estimation_problem, measurement=True)
+
         try:
-            job = self._sampler.run([circuit])
-            ret = job.result()
+            job = self._sampler.run([(circuit,)])
+            ret = job.result()[0]
         except Exception as exc:
             raise AlgorithmError("The job was not completed successfully. ") from exc
 
-        shots = ret.metadata[0].get("shots")
-        exact = True
+        circuit_results = getattr(ret.data, next(iter(ret.data.keys())))
+        shots = ret.metadata["shots"]
 
-        if shots is None:
-            result.circuit_results = ret.quasi_dists[0].binary_probabilities()
-            shots = 1
-        else:
-            result.circuit_results = {
-                k: round(v * shots) for k, v in ret.quasi_dists[0].binary_probabilities().items()
-            }
-            exact = False
+        result.circuit_results = circuit_results.get_counts()
 
         # store shots
         result.shots = shots
@@ -356,7 +369,7 @@ def estimate(self, estimation_problem: EstimationProblem) -> "AmplitudeEstimatio
             mle  # type: ignore[assignment,arg-type]
         )
 
-        result.confidence_interval = self.compute_confidence_interval(result, exact=exact)
+        result.confidence_interval = self.compute_confidence_interval(result)
         result.confidence_interval_processed = tuple(
             estimation_problem.post_processing(value)  # type: ignore[assignment,arg-type]
             for value in result.confidence_interval
@@ -369,7 +382,6 @@ def compute_confidence_interval(
         result: "AmplitudeEstimationResult",
         alpha: float = 0.05,
         kind: str = "likelihood_ratio",
-        exact: bool = False,
     ) -> tuple[float, float]:
         """Compute the (1 - alpha) confidence interval.
 
@@ -378,7 +390,6 @@ def compute_confidence_interval(
             alpha: Confidence level: compute the (1 - alpha) confidence interval.
             kind: The method to compute the confidence interval, can be 'fisher', 'observed_fisher'
                 or 'likelihood_ratio' (default)
-            exact: Whether the result comes from a statevector simulation or not
 
         Returns:
             The (1 - alpha) confidence interval of the specified kind.
@@ -386,9 +397,6 @@ def compute_confidence_interval(
         Raises:
             NotImplementedError: If the confidence interval method `kind` is not implemented.
         """
-        # if statevector simulator the estimate is exact
-        if exact:
-            return (result.mle, result.mle)
 
         if kind in ["likelihood_ratio", "lr"]:
             return _likelihood_ratio_confint(result, alpha)
diff --git a/qiskit_algorithms/amplitude_estimators/fae.py b/qiskit_algorithms/amplitude_estimators/fae.py
index e5639245..62b96686 100644
--- a/qiskit_algorithms/amplitude_estimators/fae.py
+++ b/qiskit_algorithms/amplitude_estimators/fae.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2017, 2024.
+# (C) Copyright IBM 2017, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,16 +13,17 @@
 """Faster Amplitude Estimation."""
 
 from __future__ import annotations
-from typing import cast, Tuple
+from typing import cast, Tuple, Any
 import warnings
 import numpy as np
 
 from qiskit.circuit import QuantumCircuit, ClassicalRegister
-from qiskit.primitives import BaseSampler, Sampler
+from qiskit.primitives import BaseSamplerV2, StatevectorSampler
 from qiskit_algorithms.exceptions import AlgorithmError
 
 from .amplitude_estimator import AmplitudeEstimator, AmplitudeEstimatorResult
 from .estimation_problem import EstimationProblem
+from ..custom_types import Transpiler
 
 
 class FasterAmplitudeEstimation(AmplitudeEstimator):
@@ -51,7 +52,10 @@ def __init__(
         delta: float,
         maxiter: int,
         rescale: bool = True,
-        sampler: BaseSampler | None = None,
+        sampler: BaseSamplerV2 | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         r"""
         Args:
@@ -59,6 +63,10 @@ def __init__(
             maxiter: The number of iterations, the maximal power of Q is `2 ** (maxiter - 1)`.
             rescale: Whether to rescale the problem passed to `estimate`.
             sampler: A sampler primitive to evaluate the circuits.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
 
         """
         super().__init__()
@@ -68,9 +76,11 @@ def __init__(
         self._maxiter = maxiter
         self._num_oracle_calls = 0
         self._sampler = sampler
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
 
     @property
-    def sampler(self) -> BaseSampler | None:
+    def sampler(self) -> BaseSamplerV2 | None:
         """Get the sampler primitive.
 
         Returns:
@@ -79,7 +89,7 @@ def sampler(self) -> BaseSampler | None:
         return self._sampler
 
     @sampler.setter
-    def sampler(self, sampler: BaseSampler) -> None:
+    def sampler(self, sampler: BaseSamplerV2) -> None:
         """Set sampler primitive.
 
         Args:
@@ -90,27 +100,31 @@ def sampler(self, sampler: BaseSampler) -> None:
     def _cos_estimate(self, estimation_problem, k, shots):
 
         if self._sampler is None:
-            warnings.warn("No sampler provided, defaulting to Sampler from qiskit.primitives")
-            self._sampler = Sampler()
+            warnings.warn(
+                "No sampler provided, defaulting to StatevectorSampler from qiskit.primitives"
+            )
+            self._sampler = StatevectorSampler()
 
         circuit = self.construct_circuit(estimation_problem, k, measurement=True)
 
         try:
-            job = self._sampler.run([circuit], shots=shots)
-            result = job.result()
+            pub = (circuit, None, shots)
+            job = self._sampler.run([pub])
+            result = job.result()[0]
         except Exception as exc:
             raise AlgorithmError("The job was not completed successfully. ") from exc
 
-        if shots is None:
-            shots = 1
+        circuit_results = getattr(result.data, next(iter(result.data.keys())))
+        shots = result.metadata["shots"]
+
         self._num_oracle_calls += (2 * k + 1) * shots
 
         # sum over all probabilities where the objective qubits are 1
         prob = 0
-        for bit, probabilities in result.quasi_dists[0].binary_probabilities().items():
+        for bit, value in circuit_results.get_counts().items():
             # check if it is a good state
             if estimation_problem.is_good_state(bit):
-                prob += probabilities
+                prob += value / shots
 
         cos_estimate = 1 - 2 * prob
 
@@ -163,6 +177,9 @@ def construct_circuit(
             circuit.barrier()
             circuit.measure(estimation_problem.objective_qubits, c[:])
 
+        if self._transpiler is not None:
+            circuit = self._transpiler.run(circuit, **self._transpiler_options)
+
         return circuit
 
     def estimate(self, estimation_problem: EstimationProblem) -> "FasterAmplitudeEstimationResult":
@@ -178,8 +195,10 @@ def estimate(self, estimation_problem: EstimationProblem) -> "FasterAmplitudeEst
             AlgorithmError: Sampler run error.
         """
         if self._sampler is None:
-            warnings.warn("No sampler provided, defaulting to Sampler from qiskit.primitives")
-            self._sampler = Sampler()
+            warnings.warn(
+                "No sampler provided, defaulting to StatevectorSampler from qiskit.primitives"
+            )
+            self._sampler = StatevectorSampler()
 
         self._num_oracle_calls = 0
 
diff --git a/qiskit_algorithms/amplitude_estimators/iae.py b/qiskit_algorithms/amplitude_estimators/iae.py
index 2121103d..82770497 100644
--- a/qiskit_algorithms/amplitude_estimators/iae.py
+++ b/qiskit_algorithms/amplitude_estimators/iae.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2024.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,16 +13,17 @@
 """The Iterative Quantum Amplitude Estimation Algorithm."""
 
 from __future__ import annotations
-from typing import cast, Callable, Tuple
+from typing import cast, Callable, Tuple, Any
 import warnings
 import numpy as np
 from scipy.stats import beta
 
 from qiskit import ClassicalRegister, QuantumCircuit
-from qiskit.primitives import BaseSampler, Sampler
+from qiskit.primitives import BaseSamplerV2, StatevectorSampler
 
 from .amplitude_estimator import AmplitudeEstimator, AmplitudeEstimatorResult
 from .estimation_problem import EstimationProblem
+from ..custom_types import Transpiler
 from ..exceptions import AlgorithmError
 
 
@@ -54,7 +55,10 @@ def __init__(
         alpha: float,
         confint_method: str = "beta",
         min_ratio: float = 2,
-        sampler: BaseSampler | None = None,
+        sampler: BaseSamplerV2 | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         r"""
         The output of the algorithm is an estimate for the amplitude `a`, that with at least
@@ -69,6 +73,10 @@ def __init__(
                 Clopper-Pearson intervals (default)
             min_ratio: Minimal q-ratio (:math:`K_{i+1} / K_i`) for FindNextK
             sampler: A sampler primitive to evaluate the circuits.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
 
         Raises:
             AlgorithmError: if the method to compute the confidence intervals is not supported
@@ -96,9 +104,11 @@ def __init__(
         self._min_ratio = min_ratio
         self._confint_method = confint_method
         self._sampler = sampler
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
 
     @property
-    def sampler(self) -> BaseSampler | None:
+    def sampler(self) -> BaseSamplerV2 | None:
         """Get the sampler primitive.
 
         Returns:
@@ -107,7 +117,7 @@ def sampler(self) -> BaseSampler | None:
         return self._sampler
 
     @sampler.setter
-    def sampler(self, sampler: BaseSampler) -> None:
+    def sampler(self, sampler: BaseSamplerV2) -> None:
         """Set sampler primitive.
 
         Args:
@@ -231,6 +241,9 @@ def construct_circuit(
             circuit.barrier()
             circuit.measure(estimation_problem.objective_qubits, c[:])
 
+        if self._transpiler is not None:
+            circuit = self._transpiler.run(circuit, **self._transpiler_options)
+
         return circuit
 
     def _good_state_probability(
@@ -271,8 +284,10 @@ def estimate(
             AlgorithmError: Sampler job run error.
         """
         if self._sampler is None:
-            warnings.warn("No sampler provided, defaulting to Sampler from qiskit.primitives")
-            self._sampler = Sampler()
+            warnings.warn(
+                "No sampler provided, defaulting to StatevectorSampler from qiskit.primitives"
+            )
+            self._sampler = StatevectorSampler()
 
         # initialize memory variables
         powers = [0]  # list of powers k: Q^k, (called 'k' in paper)
@@ -307,43 +322,17 @@ def estimate(
 
             # run measurements for Q^k A|0> circuit
             circuit = self.construct_circuit(estimation_problem, k, measurement=True)
-            counts = {}
 
             try:
-                job = self._sampler.run([circuit])
-                ret = job.result()
+                job = self._sampler.run([(circuit,)])
+                ret = job.result()[0]
             except Exception as exc:
                 raise AlgorithmError("The job was not completed successfully. ") from exc
 
-            shots = ret.metadata[0].get("shots")
-            if shots is None:
-                circuit = self.construct_circuit(estimation_problem, k=0, measurement=True)
-                try:
-                    job = self._sampler.run([circuit])
-                    ret = job.result()
-                except Exception as exc:
-                    raise AlgorithmError("The job was not completed successfully. ") from exc
-
-                # calculate the probability of measuring '1'
-                prob = 0.0
-                for bit, probabilities in ret.quasi_dists[0].binary_probabilities().items():
-                    # check if it is a good state
-                    if estimation_problem.is_good_state(bit):
-                        prob += probabilities
-
-                a_confidence_interval = [prob, prob]
-                a_intervals.append(a_confidence_interval)
-
-                theta_i_interval = [
-                    np.arccos(1 - 2 * a_i) / 2 / np.pi for a_i in a_confidence_interval
-                ]
-                theta_intervals.append(theta_i_interval)
-                num_oracle_queries = 0  # no Q-oracle call, only a single one to A
-                break
-
-            counts = {
-                k: round(v * shots) for k, v in ret.quasi_dists[0].binary_probabilities().items()
-            }
+            circuit_results = getattr(ret.data, next(iter(ret.data.keys())))
+            shots = ret.metadata["shots"]
+
+            counts = circuit_results.get_counts()
 
             # calculate the probability of measuring '1', 'prob' is a_i in the paper
             one_counts, prob = self._good_state_probability(estimation_problem, counts)
diff --git a/qiskit_algorithms/amplitude_estimators/mlae.py b/qiskit_algorithms/amplitude_estimators/mlae.py
index 44a8fb68..eac75932 100644
--- a/qiskit_algorithms/amplitude_estimators/mlae.py
+++ b/qiskit_algorithms/amplitude_estimators/mlae.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -14,7 +14,7 @@
 
 from __future__ import annotations
 from collections.abc import Sequence
-from typing import Callable, List, Tuple, cast
+from typing import Callable, List, Tuple, cast, Any
 import warnings
 
 import numpy as np
@@ -22,10 +22,11 @@
 from scipy.stats import norm, chi2
 
 from qiskit import ClassicalRegister, QuantumRegister, QuantumCircuit
-from qiskit.primitives import BaseSampler, Sampler
+from qiskit.primitives import BaseSamplerV2, StatevectorSampler
 
 from .amplitude_estimator import AmplitudeEstimator, AmplitudeEstimatorResult
 from .estimation_problem import EstimationProblem
+from ..custom_types import Transpiler
 from ..exceptions import AlgorithmError
 
 MINIMIZER = Callable[[Callable[[float], float], List[Tuple[float, float]]], float]
@@ -54,7 +55,10 @@ def __init__(
         self,
         evaluation_schedule: list[int] | int,
         minimizer: MINIMIZER | None = None,
-        sampler: BaseSampler | None = None,
+        sampler: BaseSamplerV2 | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         r"""
         Args:
@@ -68,12 +72,19 @@ def __init__(
                 argument and a list of (float, float) tuples (as bounds) as second argument and
                 returns a single float which is the found minimum.
             sampler: A sampler primitive to evaluate the circuits.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
+
 
         Raises:
             ValueError: If the number of oracle circuits is smaller than 1.
         """
 
         super().__init__()
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
 
         # get parameters
         if isinstance(evaluation_schedule, int):
@@ -101,7 +112,7 @@ def default_minimizer(objective_fn, bounds):
         self._sampler = sampler
 
     @property
-    def sampler(self) -> BaseSampler | None:
+    def sampler(self) -> BaseSamplerV2 | None:
         """Get the sampler primitive.
 
         Returns:
@@ -110,7 +121,7 @@ def sampler(self) -> BaseSampler | None:
         return self._sampler
 
     @sampler.setter
-    def sampler(self, sampler: BaseSampler) -> None:
+    def sampler(self, sampler: BaseSamplerV2) -> None:
         """Set sampler primitive.
 
         Args:
@@ -162,6 +173,10 @@ def construct_circuits(
 
             circuits += [qc_k]
 
+        if self._transpiler is not None:
+            # circuits = self._transpiler.run(circuits,  **self._transpiler_options)
+            circuits = self._transpiler.run(circuits[0], **self._transpiler_options)
+
         return circuits
 
     @staticmethod
@@ -170,7 +185,6 @@ def compute_confidence_interval(
         alpha: float,
         kind: str = "fisher",
         apply_post_processing: bool = False,
-        exact: bool = False,
     ) -> tuple[float, float]:
         """Compute the `alpha` confidence interval using the method `kind`.
 
@@ -184,7 +198,6 @@ def compute_confidence_interval(
             kind: The method to compute the confidence interval. Defaults to 'fisher', which
                 computes the theoretical Fisher information.
             apply_post_processing: If True, apply post-processing to the confidence interval.
-            exact: Whether the result comes from a statevector simulation or not
 
         Returns:
             The specified confidence interval.
@@ -195,11 +208,7 @@ def compute_confidence_interval(
         """
         interval: tuple[float, float] | None = None
 
-        # if statevector simulator the estimate is exact
-        if exact:
-            interval = (result.estimation, result.estimation)
-
-        elif kind in ["likelihood_ratio", "lr"]:
+        if kind in ["likelihood_ratio", "lr"]:
             interval = _likelihood_ratio_confint(result, alpha)
 
         elif kind in ["fisher", "fi"]:
@@ -275,8 +284,10 @@ def estimate(
             AlgorithmError: Sampler job run error
         """
         if self._sampler is None:
-            warnings.warn("No sampler provided, defaulting to Sampler from qiskit.primitives")
-            self._sampler = Sampler()
+            warnings.warn(
+                "No sampler provided, defaulting to StatevectorSampler from qiskit.primitives"
+            )
+            self._sampler = StatevectorSampler()
 
         if estimation_problem.state_preparation is None:
             raise AlgorithmError(
@@ -291,28 +302,20 @@ def estimate(
         circuits = self.construct_circuits(estimation_problem, measurement=True)
 
         try:
-            job = self._sampler.run(circuits)
+            pubs = [(circuit,) for circuit in circuits]
+            job = self._sampler.run(pubs)
             ret = job.result()
         except Exception as exc:
             raise AlgorithmError("The job was not completed successfully. ") from exc
 
-        circuit_results = []
-        shots = ret.metadata[0].get("shots")
-        exact = True
-        if shots is None:
-            for quasi_dist in ret.quasi_dists:
-                circuit_result = quasi_dist.binary_probabilities()
-                circuit_results.append(circuit_result)
-            shots = 1
-        else:
-            # get counts and construct MLE input
-            for quasi_dist in ret.quasi_dists:
-                counts = {k: round(v * shots) for k, v in quasi_dist.binary_probabilities().items()}
-                circuit_results.append(counts)
-            exact = False
-
-        result.shots = shots
-        result.circuit_results = circuit_results
+        pub_results_data = [
+            getattr(pub_result.data, circuit.cregs[0].name)
+            for pub_result, circuit in zip(ret, circuits)
+        ]
+
+        result.shots = self._sampler.default_shots
+        # get counts and construct MLE input
+        result.circuit_results = [prob_dist.get_counts() for prob_dist in pub_results_data]
         # run maximum likelihood estimation
         num_state_qubits = circuits[0].num_qubits - circuits[0].num_ancillas
 
@@ -334,9 +337,7 @@ def estimate(
         result.num_oracle_queries = result.shots * sum(k for k in result.evaluation_schedule)
 
         # compute and store confidence interval
-        confidence_interval = self.compute_confidence_interval(
-            result, alpha=0.05, kind="fisher", exact=exact
-        )
+        confidence_interval = self.compute_confidence_interval(result, alpha=0.05, kind="fisher")
         result.confidence_interval = confidence_interval
         result.confidence_interval_processed = tuple(  # type: ignore[assignment]
             estimation_problem.post_processing(value)  # type: ignore[arg-type]
@@ -460,7 +461,6 @@ def _compute_fisher_information(
         # one_hits = one_hits[:num_sum_terms]
 
     # Compute the Fisher information
-    fisher_information = None
     if observed:
         # Note, that the observed Fisher information is very unreliable in this algorithm!
         d_loglik = 0
@@ -470,7 +470,6 @@ def _compute_fisher_information(
 
         d_loglik /= np.sqrt(a * (1 - a))
         fisher_information = d_loglik**2 / len(all_hits)
-
     else:
         fisher_information = sum(
             shots_k * (2 * m_k + 1) ** 2 for shots_k, m_k in zip(all_hits, evaluation_schedule)
diff --git a/qiskit_algorithms/custom_types.py b/qiskit_algorithms/custom_types.py
new file mode 100644
index 00000000..3d98fd3e
--- /dev/null
+++ b/qiskit_algorithms/custom_types.py
@@ -0,0 +1,28 @@
+# This code is part of a Qiskit project.
+#
+# (C) Copyright IBM 2024.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Types used by the qiskit-algorithms package."""
+from __future__ import annotations
+
+from typing import Any, Protocol, Union
+
+from qiskit import QuantumCircuit
+
+_Circuits = Union[list[QuantumCircuit], QuantumCircuit]
+
+
+class Transpiler(Protocol):
+    """A Generic type to represent a transpiler."""
+
+    def run(self, circuits: _Circuits, **options: Any) -> _Circuits:
+        """Transpile a circuit or a list of quantum circuits."""
+        pass
diff --git a/qiskit_algorithms/eigensolvers/vqd.py b/qiskit_algorithms/eigensolvers/vqd.py
index 48ba25bb..08b8512b 100644
--- a/qiskit_algorithms/eigensolvers/vqd.py
+++ b/qiskit_algorithms/eigensolvers/vqd.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -17,30 +17,29 @@
 
 from __future__ import annotations
 
-from collections.abc import Callable, Sequence, Iterable
-from typing import Any, cast
 import logging
+from collections.abc import Callable, Sequence, Iterable
 from time import time
+from typing import Any, cast
 
 import numpy as np
-
 from qiskit.circuit import QuantumCircuit
-from qiskit.primitives import BaseEstimator
-from qiskit.quantum_info.operators.base_operator import BaseOperator
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.quantum_info import SparsePauliOp
+from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from qiskit_algorithms.state_fidelities import BaseStateFidelity
-
-from ..list_or_dict import ListOrDict
-from ..optimizers import Optimizer, Minimizer, OptimizerResult
-from ..variational_algorithm import VariationalAlgorithm
 from .eigensolver import Eigensolver, EigensolverResult
-from ..utils import validate_bounds, validate_initial_point
+from ..custom_types import Transpiler
 from ..exceptions import AlgorithmError
+from ..list_or_dict import ListOrDict
 from ..observables_evaluator import estimate_observables
+from ..optimizers import Optimizer, Minimizer, OptimizerResult
+from ..utils import validate_bounds, validate_initial_point
 
 # private function as we expect this to be updated in the next release
 from ..utils.set_batching import _set_default_batchsize
+from ..variational_algorithm import VariationalAlgorithm
 
 logger = logging.getLogger(__name__)
 
@@ -88,7 +87,7 @@ class VQD(VariationalAlgorithm, Eigensolver):
     updated once the VQD object has been constructed.
 
     Attributes:
-            estimator (BaseEstimator): The primitive instance used to perform the expectation
+            estimator (BaseEstimatorV2): The primitive instance used to perform the expectation
                 estimation as indicated in the VQD paper.
             fidelity (BaseStateFidelity): The fidelity class instance used to compute the
                 overlap estimation as indicated in the VQD paper.
@@ -115,7 +114,7 @@ class VQD(VariationalAlgorithm, Eigensolver):
 
     def __init__(
         self,
-        estimator: BaseEstimator,
+        estimator: BaseEstimatorV2,
         fidelity: BaseStateFidelity,
         ansatz: QuantumCircuit,
         optimizer: Optimizer | Minimizer | Sequence[Optimizer | Minimizer],
@@ -125,6 +124,8 @@ def __init__(
         initial_point: np.ndarray | list[np.ndarray] | None = None,
         callback: Callable[[int, np.ndarray, float, dict[str, Any], int], None] | None = None,
         convergence_threshold: float | None = None,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         """
 
@@ -154,12 +155,17 @@ def __init__(
             convergence_threshold: A threshold under which the algorithm is considered to have
                 converged. It corresponds to the maximal average fidelity an eigenstate is allowed
                 to have with the previous eigenstates. If set to None, no check is performed.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are run when using this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
         super().__init__()
 
         self.estimator = estimator
         self.fidelity = fidelity
-        self.ansatz = ansatz
+        self._ansatz = ansatz
         self.optimizer = optimizer
         self.k = k
         self.betas = betas
@@ -168,6 +174,12 @@ def __init__(
         self.callback = callback
         self.convergence_threshold = convergence_threshold
 
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
+
+        if self._transpiler is not None:
+            self._ansatz = self._transpiler.run(self._ansatz, **self._transpiler_options)
+
         self._eval_count = 0
 
     @property
@@ -180,6 +192,17 @@ def initial_point(self, initial_point: np.ndarray | list[np.ndarray] | None):
         """Sets initial point"""
         self._initial_point = initial_point
 
+    @property
+    def ansatz(self) -> QuantumCircuit:
+        return self._ansatz
+
+    @ansatz.setter
+    def ansatz(self, value: QuantumCircuit | None) -> None:
+        if self._transpiler is not None:
+            self._ansatz = self._transpiler.run(value, **self._transpiler_options)
+        else:
+            self._ansatz = value
+
     def _check_operator_ansatz(self, operator: BaseOperator):
         """Check that the number of qubits of operator and ansatz match."""
         if operator is not None and self.ansatz is not None:
@@ -205,6 +228,9 @@ def compute_eigenvalues(
     ) -> VQDResult:
         super().compute_eigenvalues(operator, aux_operators)
 
+        if self.ansatz.layout is not None:
+            operator = operator.apply_layout(self.ansatz.layout)
+
         # this sets the size of the ansatz, so it must be called before the initial point
         # validation
         self._check_operator_ansatz(operator)
@@ -213,12 +239,20 @@ def compute_eigenvalues(
 
         # We need to handle the array entries being zero or Optional i.e. having value None
         if aux_operators:
-            zero_op = SparsePauliOp.from_list([("I" * self.ansatz.num_qubits, 0)])
+            if self.ansatz.layout is not None:
+                # len(self.ansatz.layout.final_index_layout()) is the original number of qubits in the
+                # ansatz, before transpilation
+                zero_op = SparsePauliOp.from_list(
+                    [("I" * len(self.ansatz.layout.final_index_layout()), 0)]
+                )
+            else:
+                zero_op = SparsePauliOp.from_list([("I" * self.ansatz.num_qubits, 0)])
 
             # Convert the None and zero values when aux_operators is a list.
             # Drop None and convert zero values when aux_operators is a dict.
             key_op_iterator: Iterable[tuple[str | int, BaseOperator]]
             if isinstance(aux_operators, list):
+                aux_operators = [op if op is not None else zero_op for op in aux_operators]
                 key_op_iterator = enumerate(aux_operators)
                 converted: ListOrDict[BaseOperator] = [zero_op] * len(aux_operators)
             else:
@@ -228,6 +262,9 @@ def compute_eigenvalues(
                 if op is not None:
                     converted[key] = zero_op if op == 0 else op  # type: ignore[index]
 
+                    if self.ansatz.layout is not None:
+                        converted[key] = converted[key].apply_layout(self.ansatz.layout)
+
             aux_operators = converted
 
         else:
@@ -363,9 +400,9 @@ def compute_eigenvalues(
 
                     raise AlgorithmError(
                         f"Convergence threshold is set to {self.convergence_threshold} but an "
-                        f"average fidelity of {average_fidelity:.5f} with the previous eigenstates"
-                        f"has been observed during the evaluation of the {step}{suffix} lowest"
-                        f"eigenvalue."
+                        f"average (weighted by the betas) fidelity of {average_fidelity:.5f} with "
+                        f"the previous eigenstates has been observed during the evaluation of the "
+                        f"{step}{suffix} lowest eigenvalue."
                     )
                 logger.info(
                     (
@@ -425,17 +462,13 @@ def _get_evaluate_energy(  # pylint: disable=too-many-positional-arguments
                 f"Passed array has length {str(len(prev_states))}"
             )
 
-        self._check_operator_ansatz(operator)
-
         def evaluate_energy(parameters: np.ndarray) -> float | np.ndarray:
             # handle broadcasting: ensure parameters is of shape [array, array, ...]
             if len(parameters.shape) == 1:
                 parameters = np.reshape(parameters, (-1, num_parameters))
             batch_size = len(parameters)
 
-            estimator_job = self.estimator.run(
-                batch_size * [self.ansatz], batch_size * [operator], parameters
-            )
+            estimator_job = self.estimator.run([(self.ansatz, operator, parameters)])
 
             total_cost = np.zeros(batch_size)
 
@@ -454,18 +487,17 @@ def evaluate_energy(parameters: np.ndarray) -> float | np.ndarray:
                     total_cost += np.real(betas[state] * cost)
 
             try:
-                estimator_result = estimator_job.result()
+                estimator_result = estimator_job.result()[0]
 
             except Exception as exc:
                 raise AlgorithmError("The primitive job to evaluate the energy failed!") from exc
 
-            values = estimator_result.values + total_cost
+            values = estimator_result.data.evs + total_cost
 
             if self.callback is not None:
-                metadata = estimator_result.metadata
-                for params, value, meta in zip(parameters, values, metadata):
+                for params, value in zip(parameters, values):
                     self._eval_count += 1
-                    self.callback(self._eval_count, params, value, meta, step)
+                    self.callback(self._eval_count, params, value, estimator_result.metadata, step)
             else:
                 self._eval_count += len(values)
 
diff --git a/qiskit_algorithms/gradients/base/base_estimator_gradient.py b/qiskit_algorithms/gradients/base/base_estimator_gradient.py
index f7ea927b..f4461f8c 100644
--- a/qiskit_algorithms/gradients/base/base_estimator_gradient.py
+++ b/qiskit_algorithms/gradients/base/base_estimator_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023
+# (C) Copyright IBM 2022, 2025
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -18,14 +18,11 @@
 
 from abc import ABC, abstractmethod
 from collections.abc import Sequence
-from copy import copy
+from typing import Any
 
 import numpy as np
-
 from qiskit.circuit import Parameter, ParameterExpression, QuantumCircuit
-from qiskit.primitives import BaseEstimator
-from qiskit.primitives.utils import _circuit_key
-from qiskit.providers import Options
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 from qiskit.transpiler.passes import TranslateParameterizedGates
 
@@ -37,8 +34,9 @@
     _make_gradient_parameters,
     _make_gradient_parameter_values,
 )
-
 from ...algorithm_job import AlgorithmJob
+from ...custom_types import Transpiler
+from ...utils.circuit_key import _circuit_key
 
 
 class BaseEstimatorGradient(ABC):
@@ -46,17 +44,19 @@ class BaseEstimatorGradient(ABC):
 
     def __init__(
         self,
-        estimator: BaseEstimator,
-        options: Options | None = None,
+        estimator: BaseEstimatorV2,
+        precision: float | None = None,
         derivative_type: DerivativeType = DerivativeType.REAL,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ):
         r"""
         Args:
             estimator: The estimator used to compute the gradients.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-                Higher priority setting overrides lower priority setting
+            precision: Precision to be used by the underlying Estimator. If provided, this number
+                takes precedence over the default precision of the primitive. If None, the default
+                precision of the primitive is used.
             derivative_type: The type of derivative. Can be either ``DerivativeType.REAL``
                 ``DerivativeType.IMAG``, or ``DerivativeType.COMPLEX``.
 
@@ -67,13 +67,19 @@ def __init__(
                 Defaults to ``DerivativeType.REAL``, as this yields e.g. the commonly-used energy
                 gradient and this type is the only supported type for function-level schemes like
                 finite difference.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are run when using this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
-        self._estimator: BaseEstimator = estimator
-        self._default_options = Options()
-        if options is not None:
-            self._default_options.update_options(**options)
+        self._estimator: BaseEstimatorV2 = estimator
+        self._precision = precision
         self._derivative_type = derivative_type
 
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
+
         self._gradient_circuit_cache: dict[
             tuple,
             GradientCircuit,
@@ -94,7 +100,8 @@ def run(
         observables: Sequence[BaseOperator],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter] | None] | None = None,
-        **options,
+        *,
+        precision: float | Sequence[float] | None = None,
     ) -> AlgorithmJob:
         """Run the job of the estimator gradient on the given circuits.
 
@@ -107,10 +114,11 @@ def run(
                 ``circuits``. Defaults to None, which means that the gradients of all parameters in
                 each circuit are calculated. None in the sequence means that the gradients of all
                 parameters in the corresponding circuit are calculated.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-                Higher priority setting overrides lower priority setting
+            precision: Precision to be used by the underlying Estimator. If a single float is
+                provided, this number will be used for all circuits. If a sequence of floats is
+                provided, they will be used on a per-circuit basis. If not set, the gradient's default
+                precision will be used for all circuits, and if that is None (not set) then the
+                underlying primitive's (default) precision will be used for all circuits.
 
         Returns:
             The job object of the gradients of the expectation values. The i-th result corresponds to
@@ -124,7 +132,7 @@ def run(
         if isinstance(circuits, QuantumCircuit):
             # Allow a single circuit to be passed in.
             circuits = (circuits,)
-        if isinstance(observables, (BaseOperator)):
+        if isinstance(observables, BaseOperator):
             # Allow a single observable to be passed in.
             observables = (observables,)
 
@@ -141,15 +149,21 @@ def run(
             ]
         # Validate the arguments.
         self._validate_arguments(circuits, observables, parameter_values, parameters)
-        # The priority of run option is as follows:
-        # options in ``run`` method > gradient's default options > primitive's default setting.
-        opts = copy(self._default_options)
-        opts.update_options(**options)
+
+        if precision is None:
+            precision = self.precision  # May still be None
+
+        if self._transpiler is not None:
+            circuits = self._transpiler.run(circuits, **self._transpiler_options)
+            observables = [
+                obs.apply_layout(circuit.layout) for (circuit, obs) in zip(circuits, observables)
+            ]
+
         # Run the job.
         job = AlgorithmJob(
-            self._run, circuits, observables, parameter_values, parameters, **opts.__dict__
+            self._run, circuits, observables, parameter_values, parameters, precision=precision
         )
-        job.submit()
+        job._submit()
         return job
 
     @abstractmethod
@@ -159,7 +173,8 @@ def _run(
         observables: Sequence[BaseOperator],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | Sequence[float] | None,
     ) -> EstimatorGradientResult:
         """Compute the estimator gradients on the given circuits."""
         raise NotImplementedError()
@@ -262,7 +277,7 @@ def _postprocess(
             gradients.append(gradient)
             metadata.append({"parameters": parameters_})
         return EstimatorGradientResult(
-            gradients=gradients, metadata=metadata, options=results.options
+            gradients=gradients, metadata=metadata, precision=results.precision
         )
 
     @staticmethod
@@ -327,37 +342,21 @@ def _validate_arguments(
                 )
 
     @property
-    def options(self) -> Options:
-        """Return the union of estimator options setting and gradient default options,
-        where, if the same field is set in both, the gradient's default options override
-        the primitive's default setting.
+    def precision(self) -> float | None:
+        """Return the precision used by the `run` method of the Estimator primitive. If None,
+        the default precision of the primitive is used.
 
         Returns:
-            The gradient default + estimator options.
+            The default precision.
         """
-        return self._get_local_options(self._default_options.__dict__)
+        return self._precision
 
-    def update_default_options(self, **options):
-        """Update the gradient's default options setting.
+    @precision.setter
+    def precision(self, precision: float | None):
+        """Update the gradient's default precision setting.
 
         Args:
-            **options: The fields to update the default options.
+            precision: The new default precision.
         """
 
-        self._default_options.update_options(**options)
-
-    def _get_local_options(self, options: Options) -> Options:
-        """Return the union of the primitive's default setting,
-        the gradient default options, and the options in the ``run`` method.
-        The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-
-        Args:
-            options: The fields to update the options
-
-        Returns:
-            The gradient default + estimator + run options.
-        """
-        opts = copy(self._estimator.options)
-        opts.update_options(**options)
-        return opts
+        self._precision = precision
diff --git a/qiskit_algorithms/gradients/base/base_qgt.py b/qiskit_algorithms/gradients/base/base_qgt.py
index 2e254a8f..09f5d773 100644
--- a/qiskit_algorithms/gradients/base/base_qgt.py
+++ b/qiskit_algorithms/gradients/base/base_qgt.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -18,14 +18,12 @@
 
 from abc import ABC, abstractmethod
 from collections.abc import Sequence
-from copy import copy
+from typing import Any
 
 import numpy as np
 
 from qiskit.circuit import Parameter, ParameterExpression, QuantumCircuit
-from qiskit.primitives import BaseEstimator
-from qiskit.primitives.utils import _circuit_key
-from qiskit.providers import Options
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.transpiler.passes import TranslateParameterizedGates
 
 from .qgt_result import QGTResult
@@ -38,6 +36,8 @@
 )
 
 from ...algorithm_job import AlgorithmJob
+from ...custom_types import Transpiler
+from ...utils.circuit_key import _circuit_key
 
 
 class BaseQGT(ABC):
@@ -52,10 +52,13 @@ class BaseQGT(ABC):
 
     def __init__(
         self,
-        estimator: BaseEstimator,
+        estimator: BaseEstimatorV2,
         phase_fix: bool = True,
         derivative_type: DerivativeType = DerivativeType.COMPLEX,
-        options: Options | None = None,
+        precision: float | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ):
         r"""
         Args:
@@ -87,20 +90,25 @@ def __init__(
 
                     \mathrm{QGT}_{ij}= [\langle \partial_i \psi | \partial_j \psi \rangle
                         - \langle\partial_i \psi | \psi \rangle \langle\psi | \partial_j \psi \rangle].
-
-            options: Backend runtime options used for circuit execution. The order of priority is:
-                options in ``run`` method > QGT's default options > primitive's default
-                setting. Higher priority setting overrides lower priority setting.
+            precision: Precision to be used by the underlying Estimator. If provided, this number
+                takes precedence over the default precision of the primitive. If None, the default
+                precision of the primitive is used.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are run when using this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
-        self._estimator: BaseEstimator = estimator
+        self._estimator: BaseEstimatorV2 = estimator
+        self._precision = precision
         self._phase_fix: bool = phase_fix
         self._derivative_type: DerivativeType = derivative_type
-        self._default_options = Options()
-        if options is not None:
-            self._default_options.update_options(**options)
         self._qgt_circuit_cache: dict[tuple, GradientCircuit] = {}
         self._gradient_circuit_cache: dict[tuple, GradientCircuit] = {}
 
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
+
     @property
     def derivative_type(self) -> DerivativeType:
         """The derivative type."""
@@ -116,7 +124,8 @@ def run(
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter] | None] | None = None,
-        **options,
+        *,
+        precision: float | Sequence[float] | None = None,
     ) -> AlgorithmJob:
         """Run the job of the QGTs on the given circuits.
 
@@ -127,10 +136,11 @@ def run(
                 the specified parameters. Each sequence of parameters corresponds to a circuit in
                 ``circuits``. Defaults to None, which means that the QGTs of all parameters in
                 each circuit are calculated.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > QGT's
-                default options > primitive's default setting.
-                Higher priority setting overrides lower priority setting.
+            precision: Precision to be used by the underlying Estimator. If a single float is
+                provided, this number will be used for all circuits. If a sequence of floats is
+                provided, they will be used on a per-circuit basis. If not set, the gradient's default
+                precision will be used for all circuits, and if that is None (not set) then the
+                underlying primitive's (default) precision will be used for all circuits.
 
         Returns:
             The job object of the QGTs of the expectation values. The i-th result corresponds to
@@ -156,12 +166,15 @@ def run(
             ]
         # Validate the arguments.
         self._validate_arguments(circuits, parameter_values, parameters)
-        # The priority of run option is as follows:
-        # options in ``run`` method > QGT's default options > primitive's default setting.
-        opts = copy(self._default_options)
-        opts.update_options(**options)
-        job = AlgorithmJob(self._run, circuits, parameter_values, parameters, **opts.__dict__)
-        job.submit()
+
+        if precision is None:
+            precision = self.precision  # May still be None
+
+        if self._transpiler is not None:
+            circuits = self._transpiler.run(circuits, **self._transpiler_options)
+
+        job = AlgorithmJob(self._run, circuits, parameter_values, parameters, precision=precision)
+        job._submit()
         return job
 
     @abstractmethod
@@ -170,7 +183,8 @@ def _run(
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | Sequence[float] | None,
     ) -> QGTResult:
         """Compute the QGTs on the given circuits."""
         raise NotImplementedError()
@@ -297,7 +311,7 @@ def _postprocess(
             qgts=qgts,
             derivative_type=self.derivative_type,
             metadata=metadata,
-            options=results.options,
+            precision=results.precision,
         )
 
     @staticmethod
@@ -352,37 +366,21 @@ def _validate_arguments(
                 )
 
     @property
-    def options(self) -> Options:
-        """Return the union of estimator options setting and QGT default options,
-        where, if the same field is set in both, the QGT's default options override
-        the primitive's default setting.
+    def precision(self) -> float | None:
+        """Return the precision used by the `run` method of the Estimator primitive. If None,
+        the default precision of the primitive is used.
 
         Returns:
-            The QGT default + estimator options.
+            The default precision.
         """
-        return self._get_local_options(self._default_options.__dict__)
+        return self._precision
 
-    def update_default_options(self, **options):
-        """Update the gradient's default options setting.
+    @precision.setter
+    def precision(self, precision: float | None):
+        """Update the gradient's default precision setting.
 
         Args:
-            **options: The fields to update the default options.
+            precision: The new default precision.
         """
 
-        self._default_options.update_options(**options)
-
-    def _get_local_options(self, options: Options) -> Options:
-        """Return the union of the primitive's default setting,
-        the QGT default options, and the options in the ``run`` method.
-        The order of priority is: options in ``run`` method > QGT's default options > primitive's
-        default setting.
-
-        Args:
-            options: The fields to update the options
-
-        Returns:
-            The QGT default + estimator + run options.
-        """
-        opts = copy(self._estimator.options)
-        opts.update_options(**options)
-        return opts
+        self._precision = precision
diff --git a/qiskit_algorithms/gradients/base/base_sampler_gradient.py b/qiskit_algorithms/gradients/base/base_sampler_gradient.py
index 1114b5f0..7557ad95 100644
--- a/qiskit_algorithms/gradients/base/base_sampler_gradient.py
+++ b/qiskit_algorithms/gradients/base/base_sampler_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023
+# (C) Copyright IBM 2022, 2025
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -19,12 +19,10 @@
 from abc import ABC, abstractmethod
 from collections import defaultdict
 from collections.abc import Sequence
-from copy import copy
+from typing import Any
 
 from qiskit.circuit import Parameter, ParameterExpression, QuantumCircuit
-from qiskit.primitives import BaseSampler
-from qiskit.primitives.utils import _circuit_key
-from qiskit.providers import Options
+from qiskit.primitives import BaseSamplerV2
 from qiskit.transpiler.passes import TranslateParameterizedGates
 
 from .sampler_gradient_result import SamplerGradientResult
@@ -36,32 +34,47 @@
 )
 
 from ...algorithm_job import AlgorithmJob
+from ...custom_types import Transpiler
+from ...utils.circuit_key import _circuit_key
 
 
 class BaseSamplerGradient(ABC):
     """Base class for a ``SamplerGradient`` to compute the gradients of the sampling probability."""
 
-    def __init__(self, sampler: BaseSampler, options: Options | None = None):
+    def __init__(
+        self,
+        sampler: BaseSamplerV2,
+        shots: int | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
+    ):
         """
         Args:
             sampler: The sampler used to compute the gradients.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-                Higher priority setting overrides lower priority setting
+            shots: Number of shots to be used by the underlying Sampler. If provided, this number
+                takes precedence over the default number of shots of the primitive. Otherwise, the
+                default number of shots of the primitive is used.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are run when using this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
-        self._sampler: BaseSampler = sampler
-        self._default_options = Options()
-        if options is not None:
-            self._default_options.update_options(**options)
+        self._sampler: BaseSamplerV2 = sampler
+        self._shots = shots
         self._gradient_circuit_cache: dict[tuple, GradientCircuit] = {}
 
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
+
     def run(
         self,
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter] | None] | None = None,
-        **options,
+        *,
+        shots: int | Sequence[int] | None = None,
     ) -> AlgorithmJob:
         """Run the job of the sampler gradient on the given circuits.
 
@@ -73,10 +86,11 @@ def run(
                 ``circuits``. Defaults to None, which means that the gradients of all parameters in
                 each circuit are calculated. None in the sequence means that the gradients of all
                 parameters in the corresponding circuit are calculated.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-                Higher priority setting overrides lower priority setting
+            shots: Number of shots to be used by the underlying Sampler. If a single integer is
+                provided, this number will be used for all circuits. If a sequence of integers is
+                provided, they will be used on a per-circuit basis. If not set, the gradient's default
+                number of shots will be used for all circuits, and if that is None (not set) then the
+                underlying primitive's default number of shots will be used for all circuits.
         Returns:
             The job object of the gradients of the sampling probability. The i-th result
             corresponds to ``circuits[i]`` evaluated with parameters bound as ``parameter_values[i]``.
@@ -102,12 +116,15 @@ def run(
             ]
         # Validate the arguments.
         self._validate_arguments(circuits, parameter_values, parameters)
-        # The priority of run option is as follows:
-        # options in `run` method > gradient's default options > primitive's default options.
-        opts = copy(self._default_options)
-        opts.update_options(**options)
-        job = AlgorithmJob(self._run, circuits, parameter_values, parameters, **opts.__dict__)
-        job.submit()
+
+        if shots is None:
+            shots = self.shots
+
+        if self._transpiler is not None:
+            circuits = self._transpiler.run(circuits, **self._transpiler_options)
+
+        job = AlgorithmJob(self._run, circuits, parameter_values, parameters, shots=shots)
+        job._submit()
         return job
 
     @abstractmethod
@@ -116,7 +133,8 @@ def _run(
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        shots: int | Sequence[int] | None,
     ) -> SamplerGradientResult:
         """Compute the sampler gradients on the given circuits."""
         raise NotImplementedError()
@@ -213,9 +231,7 @@ def _postprocess(
                 gradient.append(dict(grad_dist))
             gradients.append(gradient)
             metadata.append([{"parameters": parameters_}])
-        return SamplerGradientResult(
-            gradients=gradients, metadata=metadata, options=results.options
-        )
+        return SamplerGradientResult(gradients=gradients, metadata=metadata, shots=results.shots)
 
     @staticmethod
     def _validate_arguments(
@@ -264,37 +280,21 @@ def _validate_arguments(
                 )
 
     @property
-    def options(self) -> Options:
-        """Return the union of sampler options setting and gradient default options,
-        where, if the same field is set in both, the gradient's default options override
-        the primitive's default setting.
+    def shots(self) -> int | None:
+        """Return the number of shots used by the `run` method of the Sampler primitive. If None,
+        the default number of shots of the primitive is used.
 
         Returns:
-            The gradient default + sampler options.
+            The default number of shots.
         """
-        return self._get_local_options(self._default_options.__dict__)
+        return self._shots
 
-    def update_default_options(self, **options):
-        """Update the gradient's default options setting.
+    @shots.setter
+    def shots(self, shots: int | None):
+        """Update the gradient's default number of shots setting.
 
         Args:
-            **options: The fields to update the default options.
+            shots: The new default number of shots.
         """
 
-        self._default_options.update_options(**options)
-
-    def _get_local_options(self, options: Options) -> Options:
-        """Return the union of the primitive's default setting,
-        the gradient default options, and the options in the ``run`` method.
-        The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-
-        Args:
-            options: The fields to update the options
-
-        Returns:
-            The gradient default + sampler + run options.
-        """
-        opts = copy(self._sampler.options)
-        opts.update_options(**options)
-        return opts
+        self._shots = shots
diff --git a/qiskit_algorithms/gradients/base/estimator_gradient_result.py b/qiskit_algorithms/gradients/base/estimator_gradient_result.py
index a01759f0..17b742ba 100644
--- a/qiskit_algorithms/gradients/base/estimator_gradient_result.py
+++ b/qiskit_algorithms/gradients/base/estimator_gradient_result.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,12 +16,10 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Any
+from typing import Any, Sequence
 
 import numpy as np
 
-from qiskit.providers import Options
-
 
 @dataclass(frozen=True)
 class EstimatorGradientResult:
@@ -31,5 +29,5 @@ class EstimatorGradientResult:
     """The gradients of the expectation values."""
     metadata: list[dict[str, Any]]
     """Additional information about the job."""
-    options: Options
-    """Primitive runtime options for the execution of the job."""
+    precision: float | Sequence[float]
+    """Precision for the execution of the job."""
diff --git a/qiskit_algorithms/gradients/base/qgt_result.py b/qiskit_algorithms/gradients/base/qgt_result.py
index f7a9d80b..543ec378 100644
--- a/qiskit_algorithms/gradients/base/qgt_result.py
+++ b/qiskit_algorithms/gradients/base/qgt_result.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,12 +16,10 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Any
+from typing import Any, Sequence
 
 import numpy as np
 
-from qiskit.providers import Options
-
 from ..utils import DerivativeType
 
 
@@ -35,5 +33,5 @@ class QGTResult:
     """The type of derivative."""
     metadata: list[dict[str, Any]] | list[list[dict[str, Any]]]
     """Additional information about the job."""
-    options: Options
-    """Primitive runtime options for the execution of the job."""
+    precision: float | Sequence[float]
+    """Precision for the execution of the job."""
diff --git a/qiskit_algorithms/gradients/base/sampler_gradient_result.py b/qiskit_algorithms/gradients/base/sampler_gradient_result.py
index 393319ab..0bf09d3d 100644
--- a/qiskit_algorithms/gradients/base/sampler_gradient_result.py
+++ b/qiskit_algorithms/gradients/base/sampler_gradient_result.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -15,11 +15,9 @@
 
 from __future__ import annotations
 
-from typing import Any
+from typing import Any, Sequence
 from dataclasses import dataclass
 
-from qiskit.providers import Options
-
 
 @dataclass(frozen=True)
 class SamplerGradientResult:
@@ -29,5 +27,5 @@ class SamplerGradientResult:
     """The gradients of the sample probabilities."""
     metadata: list[dict[str, Any]] | list[list[dict[str, Any]]]
     """Additional information about the job."""
-    options: Options
-    """Primitive runtime options for the execution of the job."""
+    shots: int | Sequence[int]
+    """Primitive number of shots for the execution of the job."""
diff --git a/qiskit_algorithms/gradients/finite_diff/finite_diff_estimator_gradient.py b/qiskit_algorithms/gradients/finite_diff/finite_diff_estimator_gradient.py
index a2480ee2..40b5d991 100644
--- a/qiskit_algorithms/gradients/finite_diff/finite_diff_estimator_gradient.py
+++ b/qiskit_algorithms/gradients/finite_diff/finite_diff_estimator_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -15,17 +15,17 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
-from typing import Literal
+from typing import Literal, Any
 
 import numpy as np
 
 from qiskit.circuit import Parameter, QuantumCircuit
-from qiskit.primitives import BaseEstimator
-from qiskit.providers import Options
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from ..base.base_estimator_gradient import BaseEstimatorGradient
 from ..base.estimator_gradient_result import EstimatorGradientResult
+from ...custom_types import Transpiler
 
 from ...exceptions import AlgorithmError
 
@@ -40,20 +40,21 @@ class FiniteDiffEstimatorGradient(BaseEstimatorGradient):
 
     def __init__(
         self,
-        estimator: BaseEstimator,
+        estimator: BaseEstimatorV2,
         epsilon: float,
-        options: Options | None = None,
+        precision: float | None = None,
         *,
         method: Literal["central", "forward", "backward"] = "central",
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ):
         r"""
         Args:
             estimator: The estimator used to compute the gradients.
             epsilon: The offset size for the finite difference gradients.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-                Higher priority setting overrides lower priority setting
+            precision: Precision to be used by the underlying Estimator. If provided, this number
+                takes precedence over the default precision of the primitive. If None, the default
+                precision of the primitive is used.
             method: The computation method of the gradients.
 
                     - ``central`` computes :math:`\frac{f(x+e)-f(x-e)}{2e}`,
@@ -61,6 +62,11 @@ def __init__(
                     - ``backward`` computes :math:`\frac{f(x)-f(x-e)}{e}`
 
                 where :math:`e` is epsilon.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are run when using this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
 
         Raises:
             ValueError: If ``epsilon`` is not positive.
@@ -74,7 +80,9 @@ def __init__(
                 f"The argument method should be central, forward, or backward: {method} is given."
             )
         self._method = method
-        super().__init__(estimator, options)
+        super().__init__(
+            estimator, precision, transpiler=transpiler, transpiler_options=transpiler_options
+        )
 
     def _run(
         self,
@@ -82,14 +90,22 @@ def _run(
         observables: Sequence[BaseOperator],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | Sequence[float] | None,
     ) -> EstimatorGradientResult:
         """Compute the estimator gradients on the given circuits."""
-        job_circuits, job_observables, job_param_values, metadata = [], [], [], []
+        metadata = []
         all_n = []
+        has_transformed_precision = False
+
+        if isinstance(precision, float) or precision is None:
+            precision = [precision] * len(circuits)
+            has_transformed_precision = True
+
+        pubs = []
 
-        for circuit, observable, parameter_values_, parameters_ in zip(
-            circuits, observables, parameter_values, parameters
+        for circuit, observable, parameter_values_, parameters_, precision_ in zip(
+            circuits, observables, parameter_values, parameters, precision
         ):
             # Indices of parameters to be differentiated
             indices = [circuit.parameters.data.index(p) for p in parameters_]
@@ -101,27 +117,21 @@ def _run(
                 plus = parameter_values_ + self._epsilon * offset
                 minus = parameter_values_ - self._epsilon * offset
                 n = 2 * len(indices)
-                job_circuits.extend([circuit] * n)
-                job_observables.extend([observable] * n)
-                job_param_values.extend(plus.tolist() + minus.tolist())
                 all_n.append(n)
+                pubs.append((circuit, observable, plus.tolist() + minus.tolist(), precision_))
             elif self._method == "forward":
                 plus = parameter_values_ + self._epsilon * offset
                 n = len(indices) + 1
-                job_circuits.extend([circuit] * n)
-                job_observables.extend([observable] * n)
-                job_param_values.extend([parameter_values_] + plus.tolist())
                 all_n.append(n)
+                pubs.append((circuit, observable, [parameter_values_] + plus.tolist(), precision_))
             elif self._method == "backward":
                 minus = parameter_values_ - self._epsilon * offset
                 n = len(indices) + 1
-                job_circuits.extend([circuit] * n)
-                job_observables.extend([observable] * n)
-                job_param_values.extend([parameter_values_] + minus.tolist())
                 all_n.append(n)
+                pubs.append((circuit, observable, [parameter_values_] + minus.tolist(), precision_))
 
         # Run the single job with all circuits.
-        job = self._estimator.run(job_circuits, job_observables, job_param_values, **options)
+        job = self._estimator.run(pubs)
         try:
             results = job.result()
         except Exception as exc:
@@ -130,23 +140,30 @@ def _run(
         # Compute the gradients
         gradients = []
         partial_sum_n = 0
-        for n in all_n:
+        for n, result_n in zip(all_n, results):
             # Ensure gradient is always defined for the append below after the if block
             # otherwise lint errors out. I left the if block as it has been coded though
             # as the values are checked in the constructor I could have made the last elif
             # a simple else instead of defining this here.
             gradient = None
+            result = result_n.data.evs
             if self._method == "central":
-                result = results.values[partial_sum_n : partial_sum_n + n]
                 gradient = (result[: n // 2] - result[n // 2 :]) / (2 * self._epsilon)
             elif self._method == "forward":
-                result = results.values[partial_sum_n : partial_sum_n + n]
                 gradient = (result[1:] - result[0]) / self._epsilon
             elif self._method == "backward":
-                result = results.values[partial_sum_n : partial_sum_n + n]
                 gradient = (result[0] - result[1:]) / self._epsilon
             partial_sum_n += n
             gradients.append(gradient)
 
-        opt = self._get_local_options(options)
-        return EstimatorGradientResult(gradients=gradients, metadata=metadata, options=opt)
+        if has_transformed_precision:
+            precision = precision[0]
+
+            if precision is None:
+                precision = results[0].metadata["target_precision"]
+        else:
+            for i, (precision_, result) in enumerate(zip(precision, results)):
+                if precision_ is None:
+                    precision[i] = results[i].metadata["target_precision"]
+
+        return EstimatorGradientResult(gradients=gradients, metadata=metadata, precision=precision)
diff --git a/qiskit_algorithms/gradients/finite_diff/finite_diff_sampler_gradient.py b/qiskit_algorithms/gradients/finite_diff/finite_diff_sampler_gradient.py
index 7203c68f..811a48bd 100644
--- a/qiskit_algorithms/gradients/finite_diff/finite_diff_sampler_gradient.py
+++ b/qiskit_algorithms/gradients/finite_diff/finite_diff_sampler_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -15,16 +15,16 @@
 from __future__ import annotations
 
 from collections import defaultdict
-from typing import Literal, Sequence
+from typing import Literal, Sequence, Any
 
 import numpy as np
 
 from qiskit.circuit import Parameter, QuantumCircuit
-from qiskit.primitives import BaseSampler
-from qiskit.providers import Options
+from qiskit.primitives import BaseSamplerV2
 
 from ..base.base_sampler_gradient import BaseSamplerGradient
 from ..base.sampler_gradient_result import SamplerGradientResult
+from ...custom_types import Transpiler
 
 from ...exceptions import AlgorithmError
 
@@ -39,20 +39,21 @@ class FiniteDiffSamplerGradient(BaseSamplerGradient):
 
     def __init__(
         self,
-        sampler: BaseSampler,
+        sampler: BaseSamplerV2,
         epsilon: float,
-        options: Options | None = None,
+        shots: int | None = None,
         *,
         method: Literal["central", "forward", "backward"] = "central",
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ):
         r"""
         Args:
             sampler: The sampler used to compute the gradients.
             epsilon: The offset size for the finite difference gradients.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-                Higher priority setting overrides lower priority setting
+            shots: Number of shots to be used by the underlying Sampler. If provided, this number
+                takes precedence over the default precision of the primitive. If None, the default
+                number of shots of the primitive is used.
             method: The computation method of the gradients.
 
                     - ``central`` computes :math:`\frac{f(x+e)-f(x-e)}{2e}`,
@@ -60,6 +61,11 @@ def __init__(
                     - ``backward`` computes :math:`\frac{f(x)-f(x-e)}{e}`
 
                 where :math:`e` is epsilon.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are run when using this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
 
         Raises:
             ValueError: If ``epsilon`` is not positive.
@@ -73,19 +79,31 @@ def __init__(
                 f"The argument method should be central, forward, or backward: {method} is given."
             )
         self._method = method
-        super().__init__(sampler, options)
+        super().__init__(
+            sampler, shots, transpiler=transpiler, transpiler_options=transpiler_options
+        )
 
     def _run(
         self,
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
-        parameters: Sequence[Sequence[Parameter]],
-        **options,
+        parameters: Sequence[Sequence[Parameter] | None] | None,
+        *,
+        shots: int | Sequence[int] | None,
     ) -> SamplerGradientResult:
         """Compute the sampler gradients on the given circuits."""
-        job_circuits, job_param_values, metadata = [], [], []
+        metadata = []
         all_n = []
-        for circuit, parameter_values_, parameters_ in zip(circuits, parameter_values, parameters):
+        has_transformed_shots = False
+
+        if isinstance(shots, int) or shots is None:
+            shots = [shots] * len(circuits)
+            has_transformed_shots = True
+
+        pubs = []
+        for circuit, parameter_values_, parameters_, shots_ in zip(
+            circuits, parameter_values, parameters, shots
+        ):
             # Indices of parameters to be differentiated
             indices = [circuit.parameters.data.index(p) for p in parameters_]
             metadata.append({"parameters": parameters_})
@@ -95,24 +113,21 @@ def _run(
                 plus = parameter_values_ + self._epsilon * offset
                 minus = parameter_values_ - self._epsilon * offset
                 n = 2 * len(indices)
-                job_circuits.extend([circuit] * n)
-                job_param_values.extend(plus.tolist() + minus.tolist())
                 all_n.append(n)
+                pubs.append((circuit, plus.tolist() + minus.tolist(), shots_))
             elif self._method == "forward":
                 plus = parameter_values_ + self._epsilon * offset
                 n = len(indices) + 1
-                job_circuits.extend([circuit] * n)
-                job_param_values.extend([parameter_values_] + plus.tolist())
+                pubs.append((circuit, [parameter_values_] + plus.tolist(), shots_))
                 all_n.append(n)
             elif self._method == "backward":
                 minus = parameter_values_ - self._epsilon * offset
                 n = len(indices) + 1
-                job_circuits.extend([circuit] * n)
-                job_param_values.extend([parameter_values_] + minus.tolist())
+                pubs.append((circuit, [parameter_values_] + minus.tolist(), shots_))
                 all_n.append(n)
 
         # Run the single job with all circuits.
-        job = self._sampler.run(job_circuits, job_param_values, **options)
+        job = self._sampler.run(pubs)
         try:
             results = job.result()
         except Exception as exc:
@@ -120,11 +135,14 @@ def _run(
 
         # Compute the gradients.
         gradients = []
-        partial_sum_n = 0
-        for n in all_n:
+
+        for n, result_n in zip(all_n, results):
             gradient = []
+            result = [
+                {label: value / res.num_shots for label, value in res.get_int_counts().items()}
+                for res in getattr(result_n.data, next(iter(result_n.data)))
+            ]
             if self._method == "central":
-                result = results.quasi_dists[partial_sum_n : partial_sum_n + n]
                 for dist_plus, dist_minus in zip(result[: n // 2], result[n // 2 :]):
                     grad_dist: dict[int, float] = defaultdict(float)
                     for key, value in dist_plus.items():
@@ -133,7 +151,6 @@ def _run(
                         grad_dist[key] -= value / (2 * self._epsilon)
                     gradient.append(dict(grad_dist))
             elif self._method == "forward":
-                result = results.quasi_dists[partial_sum_n : partial_sum_n + n]
                 dist_zero = result[0]
                 for dist_plus in result[1:]:
                     grad_dist = defaultdict(float)
@@ -142,9 +159,7 @@ def _run(
                     for key, value in dist_zero.items():
                         grad_dist[key] -= value / self._epsilon
                     gradient.append(dict(grad_dist))
-
             elif self._method == "backward":
-                result = results.quasi_dists[partial_sum_n : partial_sum_n + n]
                 dist_zero = result[0]
                 for dist_minus in result[1:]:
                     grad_dist = defaultdict(float)
@@ -154,8 +169,16 @@ def _run(
                         grad_dist[key] -= value / self._epsilon
                     gradient.append(dict(grad_dist))
 
-            partial_sum_n += n
             gradients.append(gradient)
 
-        opt = self._get_local_options(options)
-        return SamplerGradientResult(gradients=gradients, metadata=metadata, options=opt)
+        if has_transformed_shots:
+            shots = shots[0]
+
+            if shots is None:
+                shots = results[0].metadata["shots"]
+        else:
+            for i, (shots_, result) in enumerate(zip(shots, results)):
+                if shots_ is None:
+                    shots[i] = result.metadata["shots"]
+
+        return SamplerGradientResult(gradients=gradients, metadata=metadata, shots=shots)
diff --git a/qiskit_algorithms/gradients/lin_comb/lin_comb_estimator_gradient.py b/qiskit_algorithms/gradients/lin_comb/lin_comb_estimator_gradient.py
index 1be05900..37bfe781 100644
--- a/qiskit_algorithms/gradients/lin_comb/lin_comb_estimator_gradient.py
+++ b/qiskit_algorithms/gradients/lin_comb/lin_comb_estimator_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,20 +16,20 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
+from typing import Any
 
 import numpy as np
-
 from qiskit.circuit import Parameter, QuantumCircuit
-from qiskit.primitives import BaseEstimator
-from qiskit.primitives.utils import init_observable, _circuit_key
-from qiskit.providers import Options
+from qiskit.primitives import BaseEstimatorV2
+from qiskit.quantum_info import SparsePauliOp
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from ..base.base_estimator_gradient import BaseEstimatorGradient
 from ..base.estimator_gradient_result import EstimatorGradientResult
 from ..utils import DerivativeType, _make_lin_comb_gradient_circuit, _make_lin_comb_observables
-
+from ...custom_types import Transpiler
 from ...exceptions import AlgorithmError
+from ...utils.circuit_key import _circuit_key
 
 
 class LinCombEstimatorGradient(BaseEstimatorGradient):
@@ -66,9 +66,12 @@ class LinCombEstimatorGradient(BaseEstimatorGradient):
 
     def __init__(
         self,
-        estimator: BaseEstimator,
+        estimator: BaseEstimatorV2,
+        precision: float | None = None,
         derivative_type: DerivativeType = DerivativeType.REAL,
-        options: Options | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ):
         r"""
         Args:
@@ -80,14 +83,23 @@ def __init__(
                     - ``DerivativeType.REAL`` computes :math:`2 \mathrm{Re}[⟨ψ(ω)|O(θ)|dω ψ(ω)〉]`.
                     - ``DerivativeType.IMAG`` computes :math:`2 \mathrm{Im}[⟨ψ(ω)|O(θ)|dω ψ(ω)〉]`.
                     - ``DerivativeType.COMPLEX`` computes :math:`2 ⟨ψ(ω)|O(θ)|dω ψ(ω)〉`.
-
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-                Higher priority setting overrides lower priority setting.
+            precision: Precision to be used by the underlying Estimator. If provided, this number
+                takes precedence over the default precision of the primitive. If None, the default
+                precision of the primitive is used.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
         self._lin_comb_cache: dict[tuple, dict[Parameter, QuantumCircuit]] = {}
-        super().__init__(estimator, options, derivative_type=derivative_type)
+        super().__init__(
+            estimator,
+            precision,
+            derivative_type=derivative_type,
+            transpiler=transpiler,
+            transpiler_options=transpiler_options,
+        )
 
     @BaseEstimatorGradient.derivative_type.setter  # type: ignore[attr-defined]
     def derivative_type(self, derivative_type: DerivativeType) -> None:
@@ -100,14 +112,15 @@ def _run(
         observables: Sequence[BaseOperator],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | Sequence[float] | None,
     ) -> EstimatorGradientResult:
         """Compute the estimator gradients on the given circuits."""
         g_circuits, g_parameter_values, g_parameters = self._preprocess(
             circuits, parameter_values, parameters, self.SUPPORTED_GATES
         )
         results = self._run_unique(
-            g_circuits, observables, g_parameter_values, g_parameters, **options
+            g_circuits, observables, g_parameter_values, g_parameters, precision=precision
         )
         return self._postprocess(results, circuits, parameter_values, parameters)
 
@@ -117,13 +130,29 @@ def _run_unique(
         observables: Sequence[BaseOperator],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | Sequence[float] | None,
     ) -> EstimatorGradientResult:
         """Compute the estimator gradients on the given circuits."""
-        job_circuits, job_observables, job_param_values, metadata = [], [], [], []
+        has_transformed_precision = False
+
+        if isinstance(precision, float) or precision is None:
+            precision = [precision] * len(circuits)
+            has_transformed_precision = True
+
+        metadata = []
         all_n = []
-        for circuit, observable, parameter_values_, parameters_ in zip(
-            circuits, observables, parameter_values, parameters
+        pubs = []
+
+        if not (len(circuits) == len(observables) == len(parameters) == len(parameter_values)):
+            raise ValueError(
+                f"circuits, observables, parameters, and parameter_values must have the same length, "
+                f"but have respective lengths {len(circuits)},  {len(observables)}, {len(parameters)} "
+                f"and {len(parameter_values)}."
+            )
+
+        for circuit, observable, parameter_values_, parameters_, precision_ in zip(
+            circuits, observables, parameter_values, parameters, precision
         ):
             # Prepare circuits for the gradient of the specified parameters.
             meta = {"parameters": parameters_}
@@ -132,16 +161,18 @@ def _run_unique(
                 # Cache the circuits for the linear combination of unitaries.
                 # We only cache the circuits for the specified parameters in the future.
                 self._lin_comb_cache[circuit_key] = _make_lin_comb_gradient_circuit(
-                    circuit, add_measurement=False
+                    circuit, self._transpiler, self._transpiler_options, add_measurement=False
                 )
             lin_comb_circuits = self._lin_comb_cache[circuit_key]
             gradient_circuits = []
+
             for param in parameters_:
                 gradient_circuits.append(lin_comb_circuits[param])
+
             n = len(gradient_circuits)
             # Make the observable as :class:`~qiskit.quantum_info.SparsePauliOp` and
             # add an ancillary operator to compute the gradient.
-            observable = init_observable(observable)
+            observable = SparsePauliOp(observable)
             observable_1, observable_2 = _make_lin_comb_observables(
                 observable, self._derivative_type
             )
@@ -151,23 +182,30 @@ def _run_unique(
             metadata.append(meta)
             # Combine inputs into a single job to reduce overhead.
             if self._derivative_type == DerivativeType.COMPLEX:
-                job_circuits.extend(gradient_circuits * 2)
-                job_observables.extend([observable_1] * n + [observable_2] * n)
-                job_param_values.extend([parameter_values_] * 2 * n)
                 all_n.append(2 * n)
+                pubs.extend(
+                    [
+                        (gradient_circuit, observable_1, parameter_values_, precision_)
+                        for gradient_circuit in gradient_circuits
+                    ]
+                )
+                pubs.extend(
+                    [
+                        (gradient_circuit, observable_2, parameter_values_, precision_)
+                        for gradient_circuit in gradient_circuits
+                    ]
+                )
             else:
-                job_circuits.extend(gradient_circuits)
-                job_observables.extend([observable_1] * n)
-                job_param_values.extend([parameter_values_] * n)
                 all_n.append(n)
+                pubs.extend(
+                    [
+                        (gradient_circuit, observable_1, parameter_values_, precision_)
+                        for gradient_circuit in gradient_circuits
+                    ]
+                )
 
         # Run the single job with all circuits.
-        job = self._estimator.run(
-            job_circuits,
-            job_observables,
-            job_param_values,
-            **options,
-        )
+        job = self._estimator.run(pubs)
         try:
             results = job.result()
         except AlgorithmError as exc:
@@ -176,19 +214,39 @@ def _run_unique(
         # Compute the gradients.
         gradients = []
         partial_sum_n = 0
+
         for n in all_n:
             # this disable is needed as Pylint does not understand derivative_type is a property if
             # it is only defined in the base class and the getter is in the child
             # pylint: disable=comparison-with-callable
             if self.derivative_type == DerivativeType.COMPLEX:
                 gradient = np.zeros(n // 2, dtype="complex")
-                gradient.real = results.values[partial_sum_n : partial_sum_n + n // 2]
-                gradient.imag = results.values[partial_sum_n + n // 2 : partial_sum_n + n]
+                gradient.real = np.array(
+                    [result.data.evs for result in results[partial_sum_n : partial_sum_n + n // 2]]
+                )
+                gradient.imag = np.array(
+                    [
+                        result.data.evs
+                        for result in results[partial_sum_n + n // 2 : partial_sum_n + n]
+                    ]
+                )
 
             else:
-                gradient = np.real(results.values[partial_sum_n : partial_sum_n + n])
+                gradient = np.real(
+                    [result.data.evs for result in results[partial_sum_n : partial_sum_n + n]]
+                )
+
             partial_sum_n += n
             gradients.append(gradient)
 
-        opt = self._get_local_options(options)
-        return EstimatorGradientResult(gradients=gradients, metadata=metadata, options=opt)
+        if has_transformed_precision:
+            precision = precision[0]
+
+            if precision is None:
+                precision = results[0].metadata["target_precision"]
+        else:
+            for i, (precision_, result) in enumerate(zip(precision, results)):
+                if precision_ is None:
+                    precision[i] = results[i].metadata["target_precision"]
+
+        return EstimatorGradientResult(gradients=gradients, metadata=metadata, precision=precision)
diff --git a/qiskit_algorithms/gradients/lin_comb/lin_comb_qgt.py b/qiskit_algorithms/gradients/lin_comb/lin_comb_qgt.py
index a00c7b67..b26ff413 100644
--- a/qiskit_algorithms/gradients/lin_comb/lin_comb_qgt.py
+++ b/qiskit_algorithms/gradients/lin_comb/lin_comb_qgt.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,21 +16,22 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
+from typing import Any
 
 import numpy as np
 
 from qiskit.circuit import Parameter, QuantumCircuit
-from qiskit.primitives import BaseEstimator
-from qiskit.primitives.utils import _circuit_key
-from qiskit.providers import Options
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.quantum_info import SparsePauliOp
 
 from ..base.base_qgt import BaseQGT
 from .lin_comb_estimator_gradient import LinCombEstimatorGradient
 from ..base.qgt_result import QGTResult
 from ..utils import DerivativeType, _make_lin_comb_qgt_circuit, _make_lin_comb_observables
+from ...custom_types import Transpiler
 
 from ...exceptions import AlgorithmError
+from ...utils.circuit_key import _circuit_key
 
 
 class LinCombQGT(BaseQGT):
@@ -69,10 +70,13 @@ class LinCombQGT(BaseQGT):
 
     def __init__(
         self,
-        estimator: BaseEstimator,
+        estimator: BaseEstimatorV2,
         phase_fix: bool = True,
         derivative_type: DerivativeType = DerivativeType.COMPLEX,
-        options: Options | None = None,
+        precision: float | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ):
         r"""
         Args:
@@ -104,14 +108,30 @@ def __init__(
 
                     \mathrm{QGT}_{ij}= [\langle \partial_i \psi | \partial_j \psi \rangle
                         - \langle\partial_i \psi | \psi \rangle \langle\psi | \partial_j \psi \rangle].
-
-            options: Backend runtime options used for circuit execution. The order of priority is:
-                options in ``run`` method > QGT's default options > primitive's default
-                setting. Higher priority setting overrides lower priority setting.
+            precision: Precision to be used by the underlying Estimator. If provided, this number
+                takes precedence over the default precision of the primitive. If None, the default
+                precision of the primitive is used. It will also be used to instantiate the internal
+                gradient.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced by the internal gradient of this algorithm. If set to `None`,
+                these won't be transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
-        super().__init__(estimator, phase_fix, derivative_type, options=options)
+        super().__init__(
+            estimator,
+            phase_fix,
+            derivative_type,
+            precision,
+            transpiler=transpiler,
+            transpiler_options=transpiler_options,
+        )
         self._gradient = LinCombEstimatorGradient(
-            estimator, derivative_type=DerivativeType.COMPLEX, options=options
+            estimator,
+            derivative_type=DerivativeType.COMPLEX,
+            precision=precision,
+            transpiler=transpiler,
+            transpiler_options=transpiler_options,
         )
         self._lin_comb_qgt_circuit_cache: dict[
             tuple, dict[tuple[Parameter, Parameter], QuantumCircuit]
@@ -122,13 +142,16 @@ def _run(
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | Sequence[float] | None,
     ) -> QGTResult:
         """Compute the QGT on the given circuits."""
         g_circuits, g_parameter_values, g_parameters = self._preprocess(
             circuits, parameter_values, parameters, self.SUPPORTED_GATES
         )
-        results = self._run_unique(g_circuits, g_parameter_values, g_parameters, **options)
+        results = self._run_unique(
+            g_circuits, g_parameter_values, g_parameters, precision=precision
+        )
         return self._postprocess(results, circuits, parameter_values, parameters)
 
     def _run_unique(
@@ -136,14 +159,32 @@ def _run_unique(
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | Sequence[float] | None,
     ) -> QGTResult:
         """Compute the QGTs on the given circuits."""
-        job_circuits, job_observables, job_param_values, metadata = [], [], [], []
+        metadata = []
         all_n, all_m = [], []
         phase_fixes: list[int | np.ndarray] = []
 
-        for circuit, parameter_values_, parameters_ in zip(circuits, parameter_values, parameters):
+        has_transformed_precision = False
+
+        if isinstance(precision, float) or precision is None:
+            precision = [precision] * len(circuits)
+            has_transformed_precision = True
+
+        pubs = []
+
+        if not (len(circuits) == len(parameters) == len(parameter_values) == len(precision)):
+            raise ValueError(
+                f"circuits, parameters, parameter_values and precision must have the same length, but "
+                f"have respective lengths {len(circuits)},  {len(parameters)}, {len(parameter_values)} "
+                f"and {len(precision)}."
+            )
+
+        for circuit, parameter_values_, parameters_, precision_ in zip(
+            circuits, parameter_values, parameters, precision
+        ):
             # Prepare circuits for the gradient of the specified parameters.
             parameters_ = [p for p in circuit.parameters if p in parameters_]
             meta = {"parameters": parameters_}
@@ -172,25 +213,32 @@ def _run_unique(
 
             n = len(qgt_circuits)
             if self._derivative_type == DerivativeType.COMPLEX:
-                job_circuits.extend(qgt_circuits * 2)
-                job_observables.extend([observable_1] * n + [observable_2] * n)
-                job_param_values.extend([parameter_values_] * 2 * n)
                 all_m.append(len(parameters_))
                 all_n.append(2 * n)
+                pubs.extend(
+                    [
+                        (qgt_circuit, observable_1, parameter_values_, precision_)
+                        for qgt_circuit in qgt_circuits
+                    ]
+                )
+                pubs.extend(
+                    [
+                        (qgt_circuit, observable_2, parameter_values_, precision_)
+                        for qgt_circuit in qgt_circuits
+                    ]
+                )
             else:
-                job_circuits.extend(qgt_circuits)
-                job_observables.extend([observable_1] * n)
-                job_param_values.extend([parameter_values_] * n)
                 all_m.append(len(parameters_))
                 all_n.append(n)
+                pubs.extend(
+                    [
+                        (qgt_circuit, observable_1, parameter_values_, precision_)
+                        for qgt_circuit in qgt_circuits
+                    ]
+                )
 
         # Run the single job with all circuits.
-        job = self._estimator.run(
-            job_circuits,
-            job_observables,
-            job_param_values,
-            **options,
-        )
+        job = self._estimator.run(pubs)
 
         if self._phase_fix:
             # Compute the second term in the QGT if phase fix is enabled.
@@ -202,7 +250,7 @@ def _run_unique(
                 observables=phase_fix_obs,
                 parameter_values=parameter_values,
                 parameters=parameters,
-                **options,
+                precision=precision,
             )
 
         try:
@@ -223,22 +271,31 @@ def _run_unique(
                     phase_fix = np.imag(phase_fix)
                 phase_fixes.append(phase_fix)
         else:
-            phase_fixes = [0 for i in range(len(circuits))]
+            phase_fixes = [0 for _ in range(len(circuits))]
         # Compute the QGT
         qgts = []
         partial_sum_n = 0
-        for i, (n, m) in enumerate(zip(all_n, all_m)):
+        for phase_fix, n, m in zip(phase_fixes, all_n, all_m):
             qgt = np.zeros((m, m), dtype="complex")
             # Compute the first term in the QGT
             if self.derivative_type == DerivativeType.COMPLEX:
-                qgt[np.triu_indices(m)] = results.values[partial_sum_n : partial_sum_n + n // 2]
-                qgt[np.triu_indices(m)] += (
-                    1j * results.values[partial_sum_n + n // 2 : partial_sum_n + n]
+                qgt[np.triu_indices(m)] = np.array(
+                    [result.data.evs for result in results[partial_sum_n : partial_sum_n + n // 2]]
+                )
+                qgt[np.triu_indices(m)] += 1j * np.array(
+                    [
+                        result.data.evs
+                        for result in results[partial_sum_n + n // 2 : partial_sum_n + n]
+                    ]
                 )
             elif self.derivative_type == DerivativeType.REAL:
-                qgt[np.triu_indices(m)] = results.values[partial_sum_n : partial_sum_n + n]
+                qgt[np.triu_indices(m)] = np.real(
+                    [result.data.evs for result in results[partial_sum_n : partial_sum_n + n]]
+                )
             elif self.derivative_type == DerivativeType.IMAG:
-                qgt[np.triu_indices(m)] = 1j * results.values[partial_sum_n : partial_sum_n + n]
+                qgt[np.triu_indices(m)] = 1j * np.real(
+                    [result.data.evs for result in results[partial_sum_n : partial_sum_n + n]]
+                )
 
             # Add the conjugate of the upper triangle to the lower triangle
             qgt += np.triu(qgt, k=1).conjugate().T
@@ -248,11 +305,20 @@ def _run_unique(
                 qgt = np.imag(qgt)
 
             # Subtract the phase fix from the QGT
-            qgt = qgt - phase_fixes[i]
+            qgt = qgt - phase_fix
             partial_sum_n += n
             qgts.append(qgt / 4)
 
-        opt = self._get_local_options(options)
+        if has_transformed_precision:
+            precision = precision[0]
+
+            if precision is None:
+                precision = results[0].metadata["target_precision"]
+        else:
+            for i, (precision_, result) in enumerate(zip(precision, results)):
+                if precision_ is None:
+                    precision[i] = results[i].metadata["target_precision"]
+
         return QGTResult(
-            qgts=qgts, derivative_type=self.derivative_type, metadata=metadata, options=opt
+            qgts=qgts, derivative_type=self.derivative_type, metadata=metadata, precision=precision
         )
diff --git a/qiskit_algorithms/gradients/lin_comb/lin_comb_sampler_gradient.py b/qiskit_algorithms/gradients/lin_comb/lin_comb_sampler_gradient.py
index 30083d53..673f1a26 100644
--- a/qiskit_algorithms/gradients/lin_comb/lin_comb_sampler_gradient.py
+++ b/qiskit_algorithms/gradients/lin_comb/lin_comb_sampler_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -17,17 +17,17 @@
 
 from collections import defaultdict
 from collections.abc import Sequence
+from typing import Any
 
 from qiskit.circuit import Parameter, QuantumCircuit
-from qiskit.primitives import BaseSampler
-from qiskit.primitives.utils import _circuit_key
-from qiskit.providers import Options
+from qiskit.primitives import BaseSamplerV2
 
 from ..base.base_sampler_gradient import BaseSamplerGradient
 from ..base.sampler_gradient_result import SamplerGradientResult
 from ..utils import _make_lin_comb_gradient_circuit
-
+from ...custom_types import Transpiler
 from ...exceptions import AlgorithmError
+from ...utils.circuit_key import _circuit_key
 
 
 class LinCombSamplerGradient(BaseSamplerGradient):
@@ -62,30 +62,44 @@ class LinCombSamplerGradient(BaseSamplerGradient):
         "z",
     ]
 
-    def __init__(self, sampler: BaseSampler, options: Options | None = None):
+    def __init__(
+        self,
+        sampler: BaseSamplerV2,
+        shots: int | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
+    ):
         """
         Args:
             sampler: The sampler used to compute the gradients.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-                Higher priority setting overrides lower priority setting
+            shots: Number of shots to be used by the underlying Sampler. If provided, this number
+                takes precedence over the default precision of the primitive. If None, the default
+                number of shots of the primitive is used.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
         self._lin_comb_cache: dict[tuple, dict[Parameter, QuantumCircuit]] = {}
-        super().__init__(sampler, options)
+        super().__init__(
+            sampler, shots, transpiler=transpiler, transpiler_options=transpiler_options
+        )
 
     def _run(
         self,
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        shots: int | None = None,
     ) -> SamplerGradientResult:
         """Compute the estimator gradients on the given circuits."""
         g_circuits, g_parameter_values, g_parameters = self._preprocess(
             circuits, parameter_values, parameters, self.SUPPORTED_GATES
         )
-        results = self._run_unique(g_circuits, g_parameter_values, g_parameters, **options)
+        results = self._run_unique(g_circuits, g_parameter_values, g_parameters, shots=shots)
         return self._postprocess(results, circuits, parameter_values, parameters)
 
     def _run_unique(
@@ -93,12 +107,30 @@ def _run_unique(
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        shots: int | None = None,
     ) -> SamplerGradientResult:
         """Compute the sampler gradients on the given circuits."""
-        job_circuits, job_param_values, metadata = [], [], []
+        metadata = []
         all_n = []
-        for circuit, parameter_values_, parameters_ in zip(circuits, parameter_values, parameters):
+        has_transformed_shots = False
+
+        if isinstance(shots, int) or shots is None:
+            shots = [shots] * len(circuits)
+            has_transformed_shots = True
+
+        pubs = []
+
+        if not (len(circuits) == len(parameters) == len(parameter_values) == len(shots)):
+            raise ValueError(
+                f"circuits, parameters, parameter_values and shots must have the same length, but "
+                f"have respective lengths {len(circuits)},  {len(parameters)}, {len(parameter_values)} "
+                f"and {len(shots)}."
+            )
+
+        for circuit, parameter_values_, parameters_, shots_ in zip(
+            circuits, parameter_values, parameters, shots
+        ):
             # Prepare circuits for the gradient of the specified parameters.
             # TODO: why is this not wrapped into another list level like it is done elsewhere?
             metadata.append({"parameters": parameters_})
@@ -107,7 +139,7 @@ def _run_unique(
                 # Cache the circuits for the linear combination of unitaries.
                 # We only cache the circuits for the specified parameters in the future.
                 self._lin_comb_cache[circuit_key] = _make_lin_comb_gradient_circuit(
-                    circuit, add_measurement=True
+                    circuit, self._transpiler, self._transpiler_options, add_measurement=True
                 )
             lin_comb_circuits = self._lin_comb_cache[circuit_key]
             gradient_circuits = []
@@ -115,12 +147,11 @@ def _run_unique(
                 gradient_circuits.append(lin_comb_circuits[param])
             # Combine inputs into a single job to reduce overhead.
             n = len(gradient_circuits)
-            job_circuits.extend(gradient_circuits)
-            job_param_values.extend([parameter_values_] * n)
+            pubs.extend([(circ, parameter_values_, shots_) for circ in gradient_circuits])
             all_n.append(n)
 
         # Run the single job with all circuits.
-        job = self._sampler.run(job_circuits, job_param_values, **options)
+        job = self._sampler.run(pubs)
         try:
             results = job.result()
         except Exception as exc:
@@ -131,7 +162,14 @@ def _run_unique(
         partial_sum_n = 0
         for i, n in enumerate(all_n):
             gradient = []
-            result = results.quasi_dists[partial_sum_n : partial_sum_n + n]
+            result = []
+
+            for result_n in results[partial_sum_n : partial_sum_n + n]:
+                res = result_n.data.meas
+                result.append(
+                    {label: value / res.num_shots for label, value in res.get_int_counts().items()}
+                )
+
             m = 2 ** circuits[i].num_qubits
             for dist in result:
                 grad_dist: dict[int, float] = defaultdict(float)
@@ -144,5 +182,14 @@ def _run_unique(
             gradients.append(gradient)
             partial_sum_n += n
 
-        opt = self._get_local_options(options)
-        return SamplerGradientResult(gradients=gradients, metadata=metadata, options=opt)
+        if has_transformed_shots:
+            shots = shots[0]
+
+            if shots is None:
+                shots = results[0].metadata["shots"]
+        else:
+            for i, (shots_, result) in enumerate(zip(shots, results)):
+                if shots_ is None:
+                    shots[i] = result.metadata["shots"]
+
+        return SamplerGradientResult(gradients=gradients, metadata=metadata, shots=shots)
diff --git a/qiskit_algorithms/gradients/param_shift/param_shift_estimator_gradient.py b/qiskit_algorithms/gradients/param_shift/param_shift_estimator_gradient.py
index cb3fcf90..ba1b2aaf 100644
--- a/qiskit_algorithms/gradients/param_shift/param_shift_estimator_gradient.py
+++ b/qiskit_algorithms/gradients/param_shift/param_shift_estimator_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -60,14 +60,15 @@ def _run(
         observables: Sequence[BaseOperator],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | Sequence[float] | None,
     ) -> EstimatorGradientResult:
         """Compute the gradients of the expectation values by the parameter shift rule."""
         g_circuits, g_parameter_values, g_parameters = self._preprocess(
             circuits, parameter_values, parameters, self.SUPPORTED_GATES
         )
         results = self._run_unique(
-            g_circuits, observables, g_parameter_values, g_parameters, **options
+            g_circuits, observables, g_parameter_values, g_parameters, precision=precision
         )
         return self._postprocess(results, circuits, parameter_values, parameters)
 
@@ -77,13 +78,34 @@ def _run_unique(
         observables: Sequence[BaseOperator],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | Sequence[float] | None,
     ) -> EstimatorGradientResult:
         """Compute the estimator gradients on the given circuits."""
-        job_circuits, job_observables, job_param_values, metadata = [], [], [], []
-        all_n = []
-        for circuit, observable, parameter_values_, parameters_ in zip(
-            circuits, observables, parameter_values, parameters
+        has_transformed_precision = False
+
+        if isinstance(precision, float) or precision is None:
+            precision = [precision] * len(circuits)
+            has_transformed_precision = True
+
+        metadata = []
+        pubs = []
+
+        if not (
+            len(circuits)
+            == len(observables)
+            == len(parameters)
+            == len(parameter_values)
+            == len(precision)
+        ):
+            raise ValueError(
+                f"circuits, observables, parameters, parameter_values and precision must have the same"
+                f"length, but have respective lengths {len(circuits)},  {len(observables)}, "
+                f"{len(parameters)}, {len(parameter_values)} and {len(precision)}."
+            )
+
+        for circuit, observable, parameter_values_, parameters_, precision_ in zip(
+            circuits, observables, parameter_values, parameters, precision
         ):
             metadata.append({"parameters": parameters_})
             # Make parameter values for the parameter shift rule.
@@ -91,19 +113,10 @@ def _run_unique(
                 circuit, parameter_values_, parameters_
             )
             # Combine inputs into a single job to reduce overhead.
-            n = len(param_shift_parameter_values)
-            job_circuits.extend([circuit] * n)
-            job_observables.extend([observable] * n)
-            job_param_values.extend(param_shift_parameter_values)
-            all_n.append(n)
+            pubs.append((circuit, observable, param_shift_parameter_values, precision_))
 
         # Run the single job with all circuits.
-        job = self._estimator.run(
-            job_circuits,
-            job_observables,
-            job_param_values,
-            **options,
-        )
+        job = self._estimator.run(pubs)
         try:
             results = job.result()
         except Exception as exc:
@@ -111,12 +124,21 @@ def _run_unique(
 
         # Compute the gradients.
         gradients = []
-        partial_sum_n = 0
-        for n in all_n:
-            result = results.values[partial_sum_n : partial_sum_n + n]
-            gradient_ = (result[: n // 2] - result[n // 2 :]) / 2
+
+        for result in results:
+            evs = result.data.evs
+            n = evs.shape[0]
+            gradient_ = (evs[: n // 2] - evs[n // 2 :]) / 2
             gradients.append(gradient_)
-            partial_sum_n += n
 
-        opt = self._get_local_options(options)
-        return EstimatorGradientResult(gradients=gradients, metadata=metadata, options=opt)
+        if has_transformed_precision:
+            precision = precision[0]
+
+            if precision is None:
+                precision = results[0].metadata["target_precision"]
+        else:
+            for i, (precision_, result) in enumerate(zip(precision, results)):
+                if precision_ is None:
+                    precision[i] = results[i].metadata["target_precision"]
+
+        return EstimatorGradientResult(gradients=gradients, metadata=metadata, precision=precision)
diff --git a/qiskit_algorithms/gradients/param_shift/param_shift_sampler_gradient.py b/qiskit_algorithms/gradients/param_shift/param_shift_sampler_gradient.py
index b27d6487..bb346c88 100644
--- a/qiskit_algorithms/gradients/param_shift/param_shift_sampler_gradient.py
+++ b/qiskit_algorithms/gradients/param_shift/param_shift_sampler_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -59,13 +59,14 @@ def _run(
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        shots: int | None,
     ) -> SamplerGradientResult:
         """Compute the estimator gradients on the given circuits."""
         g_circuits, g_parameter_values, g_parameters = self._preprocess(
             circuits, parameter_values, parameters, self.SUPPORTED_GATES
         )
-        results = self._run_unique(g_circuits, g_parameter_values, g_parameters, **options)
+        results = self._run_unique(g_circuits, g_parameter_values, g_parameters, shots)
         return self._postprocess(results, circuits, parameter_values, parameters)
 
     def _run_unique(
@@ -73,12 +74,22 @@ def _run_unique(
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        shots: int | None,
     ) -> SamplerGradientResult:
         """Compute the sampler gradients on the given circuits."""
-        job_circuits, job_param_values, metadata = [], [], []
+        metadata = []
         all_n = []
-        for circuit, parameter_values_, parameters_ in zip(circuits, parameter_values, parameters):
+        has_transformed_shots = False
+
+        if isinstance(shots, int) or shots is None:
+            shots = [shots] * len(circuits)
+            has_transformed_shots = True
+
+        pubs = []
+
+        for circuit, parameter_values_, parameters_, shots_ in zip(
+            circuits, parameter_values, parameters, shots
+        ):
             metadata.append({"parameters": parameters_})
             # Make parameter values for the parameter shift rule.
             param_shift_parameter_values = _make_param_shift_parameter_values(
@@ -86,12 +97,11 @@ def _run_unique(
             )
             # Combine inputs into a single job to reduce overhead.
             n = len(param_shift_parameter_values)
-            job_circuits.extend([circuit] * n)
-            job_param_values.extend(param_shift_parameter_values)
+            pubs.append((circuit, param_shift_parameter_values, shots_))
             all_n.append(n)
 
         # Run the single job with all circuits.
-        job = self._sampler.run(job_circuits, job_param_values, **options)
+        job = self._sampler.run(pubs)
         try:
             results = job.result()
         except Exception as exc:
@@ -99,10 +109,12 @@ def _run_unique(
 
         # Compute the gradients.
         gradients = []
-        partial_sum_n = 0
-        for n in all_n:
+        for n, result_n in zip(all_n, results):
             gradient = []
-            result = results.quasi_dists[partial_sum_n : partial_sum_n + n]
+            result = [
+                {label: value / res.num_shots for label, value in res.get_int_counts().items()}
+                for res in getattr(result_n.data, next(iter(result_n.data)))
+            ]
             for dist_plus, dist_minus in zip(result[: n // 2], result[n // 2 :]):
                 grad_dist: dict[int, float] = defaultdict(float)
                 for key, val in dist_plus.items():
@@ -110,8 +122,17 @@ def _run_unique(
                 for key, val in dist_minus.items():
                     grad_dist[key] -= val / 2
                 gradient.append(dict(grad_dist))
+
             gradients.append(gradient)
-            partial_sum_n += n
 
-        opt = self._get_local_options(options)
-        return SamplerGradientResult(gradients=gradients, metadata=metadata, options=opt)
+        if has_transformed_shots:
+            shots = shots[0]
+
+            if shots is None:
+                shots = results[0].metadata["shots"]
+        else:
+            for i, (shots_, result) in enumerate(zip(shots, results)):
+                if shots_ is None:
+                    shots[i] = result.metadata["shots"]
+
+        return SamplerGradientResult(gradients=gradients, metadata=metadata, shots=shots)
diff --git a/qiskit_algorithms/gradients/qfi.py b/qiskit_algorithms/gradients/qfi.py
index 4a53b20d..08aa2e93 100644
--- a/qiskit_algorithms/gradients/qfi.py
+++ b/qiskit_algorithms/gradients/qfi.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -17,16 +17,15 @@
 
 from abc import ABC
 from collections.abc import Sequence
-from copy import copy
+from typing import Any
 
 from qiskit.circuit import Parameter, QuantumCircuit
-from qiskit.providers import Options
 
 from .base.base_qgt import BaseQGT
 from .lin_comb.lin_comb_estimator_gradient import DerivativeType
 from .qfi_result import QFIResult
-
 from ..algorithm_job import AlgorithmJob
+from ..custom_types import Transpiler
 from ..exceptions import AlgorithmError
 
 
@@ -43,26 +42,35 @@ class QFI(ABC):
     def __init__(
         self,
         qgt: BaseQGT,
-        options: Options | None = None,
+        precision: float | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ):
         r"""
         Args:
             qgt: The quantum geometric tensor used to compute the QFI.
-            options: Backend runtime options used for circuit execution. The order of priority is:
-                options in ``run`` method > QFI's default options > primitive's default
-                setting. Higher priority setting overrides lower priority setting.
+            precision: Precision to override the BaseQGT's. If None, the BaseQGT's precision will
+                be used.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are run when using this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
         self._qgt: BaseQGT = qgt
-        self._default_options = Options()
-        if options is not None:
-            self._default_options.update_options(**options)
+        self._precision = precision
+
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
 
     def run(
         self,
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter] | None] | None = None,
-        **options,
+        *,
+        precision: float | Sequence[float] | None = None,
     ) -> AlgorithmJob:
         """Run the job of the QFIs on the given circuits.
 
@@ -73,10 +81,11 @@ def run(
                 the specified parameters. Each sequence of parameters corresponds to a circuit in
                 ``circuits``. Defaults to None, which means that the QFIs of all parameters in
                 each circuit are calculated.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > QFI's
-                default options > QGT's default setting.
-                Higher priority setting overrides lower priority setting.
+            precision: Precision to be used by the underlying Estimator. If a single float is
+                provided, this number will be used for all circuits. If a sequence of floats is
+                provided, they will be used on a per-circuit basis. If not set, the gradient's default
+                precision will be used for all circuits, and if that is None (not set) then the
+                underlying primitive's (default) precision will be used for all circuits.
 
         Returns:
             The job object of the QFIs of the expectation values. The i-th result corresponds to
@@ -98,12 +107,15 @@ def run(
                 params if params is not None else circuits[i].parameters
                 for i, params in enumerate(parameters)
             ]
-        # The priority of run option is as follows:
-        # options in ``run`` method > QFI's default options > QGT's default setting.
-        opts = copy(self._default_options)
-        opts.update_options(**options)
-        job = AlgorithmJob(self._run, circuits, parameter_values, parameters, **opts.__dict__)
-        job.submit()
+
+        if precision is None:
+            precision = self.precision  # May still be None
+
+        if self._transpiler is not None:
+            circuits = self._transpiler.run(circuits, **self._transpiler_options)
+
+        job = AlgorithmJob(self._run, circuits, parameter_values, parameters, precision=precision)
+        job._submit()
         return job
 
     def _run(
@@ -111,7 +123,8 @@ def _run(
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | Sequence[float] | None,
     ) -> QFIResult:
         """Compute the QFI on the given circuits."""
         # Set the derivative type to real
@@ -119,7 +132,8 @@ def _run(
             self._qgt.derivative_type,
             DerivativeType.REAL,
         )
-        job = self._qgt.run(circuits, parameter_values, parameters, **options)
+
+        job = self._qgt.run(circuits, parameter_values, parameters, precision=precision)
 
         try:
             result = job.result()
@@ -131,41 +145,25 @@ def _run(
         return QFIResult(
             qfis=[4 * qgt.real for qgt in result.qgts],
             metadata=result.metadata,
-            options=result.options,
+            precision=result.precision,
         )
 
     @property
-    def options(self) -> Options:
-        """Return the union of QGT's options setting and QFI's default options,
-        where, if the same field is set in both, the QFI's default options override
-        the QGT's default setting.
+    def precision(self) -> float | None:
+        """Return the precision used by the `run` method of the BaseQGT's Estimator primitive. If
+        None, the default precision of the primitive is used.
 
         Returns:
-            The QFI default + QGT options.
+            The default precision.
         """
-        return self._get_local_options(self._default_options.__dict__)
+        return self._precision
 
-    def update_default_options(self, **options):
-        """Update the gradient's default options setting.
+    @precision.setter
+    def precision(self, precision: float | None):
+        """Update the QFI's default precision setting.
 
         Args:
-            **options: The fields to update the default options.
+            precision: The new default precision.
         """
 
-        self._default_options.update_options(**options)
-
-    def _get_local_options(self, options: Options) -> Options:
-        """Return the union of the QFI default setting,
-        the QGT default options, and the options in the ``run`` method.
-        The order of priority is: options in ``run`` method > QFI's default options > QGT's
-        default setting.
-
-        Args:
-            options: The fields to update the options
-
-        Returns:
-            The QFI default + QGT default + run options.
-        """
-        opts = copy(self._qgt.options)
-        opts.update_options(**options)
-        return opts
+        self._precision = precision
diff --git a/qiskit_algorithms/gradients/qfi_result.py b/qiskit_algorithms/gradients/qfi_result.py
index 9486c990..77a39ad2 100644
--- a/qiskit_algorithms/gradients/qfi_result.py
+++ b/qiskit_algorithms/gradients/qfi_result.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,12 +16,10 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Any
+from typing import Any, Sequence
 
 import numpy as np
 
-from qiskit.providers import Options
-
 
 @dataclass(frozen=True)
 class QFIResult:
@@ -29,7 +27,7 @@ class QFIResult:
 
     qfis: list[np.ndarray]
     """The QFI."""
-    metadata: list[dict[str, Any]]
+    metadata: dict[str, Any]
     """Additional information about the job."""
-    options: Options
-    """Primitive runtime options for the execution of the job."""
+    precision: float | Sequence[float]
+    """Precision for the execution of the job."""
diff --git a/qiskit_algorithms/gradients/reverse/reverse_gradient.py b/qiskit_algorithms/gradients/reverse/reverse_gradient.py
index 82654a59..cadc89e6 100644
--- a/qiskit_algorithms/gradients/reverse/reverse_gradient.py
+++ b/qiskit_algorithms/gradients/reverse/reverse_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -22,7 +22,7 @@
 from qiskit.circuit import QuantumCircuit, Parameter
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 from qiskit.quantum_info import Statevector
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 
 from .bind import bind
 from .derive_circuit import derive_circuit
@@ -64,7 +64,7 @@ def __init__(self, derivative_type: DerivativeType = DerivativeType.REAL):
             derivative_type: Defines whether the real, imaginary or real plus imaginary part
                 of the gradient is returned.
         """
-        dummy_estimator = Estimator()  # this is required by the base class, but not used
+        dummy_estimator = StatevectorEstimator()  # this is required by the base class, but not used
         super().__init__(dummy_estimator, derivative_type=derivative_type)
 
     @BaseEstimatorGradient.derivative_type.setter  # type: ignore[attr-defined]
@@ -78,15 +78,14 @@ def _run(
         observables: Sequence[BaseOperator],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | None = None,
     ) -> EstimatorGradientResult:
         """Compute the gradients of the expectation values by the parameter shift rule."""
         g_circuits, g_parameter_values, g_parameters = self._preprocess(
             circuits, parameter_values, parameters, self.SUPPORTED_GATES
         )
-        results = self._run_unique(
-            g_circuits, observables, g_parameter_values, g_parameters, **options
-        )
+        results = self._run_unique(g_circuits, observables, g_parameter_values, g_parameters)
         return self._postprocess(results, circuits, parameter_values, parameters)
 
     def _run_unique(
@@ -95,7 +94,6 @@ def _run_unique(
         observables: Sequence[BaseOperator],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,  # pylint: disable=unused-argument
     ) -> EstimatorGradientResult:
         num_gradients = len(circuits)
         gradients = []
@@ -168,7 +166,7 @@ def _run_unique(
             gradient = np.array(list(grads.values()))
             gradients.append(self._to_derivtype(gradient))
 
-        result = EstimatorGradientResult(gradients, metadata=metadata, options={})
+        result = EstimatorGradientResult(gradients, metadata=metadata, precision=0.0)
         return result
 
     def _to_derivtype(self, gradient):
diff --git a/qiskit_algorithms/gradients/reverse/reverse_qgt.py b/qiskit_algorithms/gradients/reverse/reverse_qgt.py
index 53708b37..6cf01907 100644
--- a/qiskit_algorithms/gradients/reverse/reverse_qgt.py
+++ b/qiskit_algorithms/gradients/reverse/reverse_qgt.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023, 2024.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,8 +21,7 @@
 
 from qiskit.circuit import QuantumCircuit, Parameter
 from qiskit.quantum_info import Statevector
-from qiskit.providers import Options
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 
 from ..base.base_qgt import BaseQGT
 from ..base.qgt_result import QGTResult
@@ -66,30 +65,24 @@ def __init__(
             derivative_type: Determines whether the complex QGT or only the real or imaginary
                 parts are calculated.
         """
-        dummy_estimator = Estimator()  # this method does not need an estimator
+        dummy_estimator = StatevectorEstimator()  # this method does not need an estimator
         super().__init__(dummy_estimator, phase_fix, derivative_type)
 
-    @property
-    def options(self) -> Options:
-        """There are no options for the reverse QGT, returns an empty options dict.
-
-        Returns:
-            Empty options.
-        """
-        return Options()
-
     def _run(  # pylint: disable=arguments-renamed
         self,
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | None = None,
     ) -> QGTResult:
         """Compute the QGT on the given circuits."""
         g_circuits, g_parameter_values, g_parameter_sets = self._preprocess(
             circuits, parameter_values, parameters, self.SUPPORTED_GATES
         )
-        results = self._run_unique(g_circuits, g_parameter_values, g_parameter_sets, **options)
+        results = self._run_unique(
+            g_circuits, g_parameter_values, g_parameter_sets, precision=precision
+        )
         return self._postprocess(results, circuits, parameter_values, parameters)
 
     def _run_unique(
@@ -97,7 +90,8 @@ def _run_unique(
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameter_sets: Sequence[Sequence[Parameter]],
-        **options,  # pylint: disable=unused-argument
+        *,
+        precision: float | None = None,
     ) -> QGTResult:
         num_qgts = len(circuits)
         qgts = []
@@ -108,7 +102,6 @@ def _run_unique(
             circuit = circuits[k]
             parameters = list(parameter_sets[k])
 
-            num_parameters = len(parameters)
             original_parameter_order = [p for p in circuit.parameters if p in parameters]
             metadata.append({"parameters": original_parameter_order})
 
@@ -216,7 +209,8 @@ def _run_unique(
             # append and cast to real/imag if required
             qgts.append(self._to_derivtype(qgt))
 
-        result = QGTResult(qgts, self.derivative_type, metadata, options=None)
+        # Doesn't really make sense to pass the precision since it's not used
+        result = QGTResult(qgts, self.derivative_type, metadata, precision=precision)
         return result
 
     def _to_derivtype(self, qgt):
diff --git a/qiskit_algorithms/gradients/spsa/spsa_estimator_gradient.py b/qiskit_algorithms/gradients/spsa/spsa_estimator_gradient.py
index c0387a20..9c4ec4e4 100644
--- a/qiskit_algorithms/gradients/spsa/spsa_estimator_gradient.py
+++ b/qiskit_algorithms/gradients/spsa/spsa_estimator_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -15,16 +15,17 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
+from typing import Any
 
 import numpy as np
 
 from qiskit.circuit import Parameter, QuantumCircuit
-from qiskit.primitives import BaseEstimator
-from qiskit.providers import Options
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from ..base.base_estimator_gradient import BaseEstimatorGradient
 from ..base.estimator_gradient_result import EstimatorGradientResult
+from ...custom_types import Transpiler
 
 from ...exceptions import AlgorithmError
 
@@ -43,11 +44,14 @@ class SPSAEstimatorGradient(BaseEstimatorGradient):
     # pylint: disable=too-many-positional-arguments
     def __init__(
         self,
-        estimator: BaseEstimator,
+        estimator: BaseEstimatorV2,
         epsilon: float,
         batch_size: int = 1,
         seed: int | None = None,
-        options: Options | None = None,
+        precision: float | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ):
         """
         Args:
@@ -55,11 +59,14 @@ def __init__(
             epsilon: The offset size for the SPSA gradients.
             batch_size: The number of gradients to average.
             seed: The seed for a random perturbation vector.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-                Higher priority setting overrides lower priority setting
-
+            precision: Precision to be used by the underlying Estimator. If provided, this number
+                takes precedence over the default precision of the primitive. If None, the default
+                precision of the primitive is used.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are run when using this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         Raises:
             ValueError: If ``epsilon`` is not positive.
         """
@@ -69,7 +76,9 @@ def __init__(
         self._batch_size = batch_size
         self._seed = np.random.default_rng(seed)
 
-        super().__init__(estimator, options)
+        super().__init__(
+            estimator, precision, transpiler=transpiler, transpiler_options=transpiler_options
+        )
 
     def _run(
         self,
@@ -77,16 +86,36 @@ def _run(
         observables: Sequence[BaseOperator],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        precision: float | Sequence[float] | None,
     ) -> EstimatorGradientResult:
         """Compute the estimator gradients on the given circuits."""
-        job_circuits, job_observables, job_param_values, metadata, offsets = [], [], [], [], []
-        all_n = []
-        for circuit, observable, parameter_values_, parameters_ in zip(
-            circuits, observables, parameter_values, parameters
+        metadata = []
+        offsets = []
+        has_transformed_precision = False
+
+        if isinstance(precision, float) or precision is None:
+            precision = [precision] * len(circuits)
+            has_transformed_precision = True
+
+        pubs = []
+
+        if not (
+            len(circuits)
+            == len(observables)
+            == len(parameters)
+            == len(parameter_values)
+            == len(precision)
+        ):
+            raise ValueError(
+                f"circuits, observables, parameters, parameter_values and precision must have the same "
+                f"length, but have respective lengths {len(circuits)},  {len(observables)}, "
+                f"{len(parameters)}, {len(parameter_values)} and {len(precision)}."
+            )
+
+        for circuit, observable, parameter_values_, parameters_, precision_ in zip(
+            circuits, observables, parameter_values, parameters, precision
         ):
-            # Indices of parameters to be differentiated.
-            indices = [circuit.parameters.data.index(p) for p in parameters_]
             metadata.append({"parameters": parameters_})
             # Make random perturbation vectors.
             offset = [
@@ -98,18 +127,10 @@ def _run(
             offsets.append(offset)
 
             # Combine inputs into a single job to reduce overhead.
-            job_circuits.extend([circuit] * 2 * self._batch_size)
-            job_observables.extend([observable] * 2 * self._batch_size)
-            job_param_values.extend(plus + minus)
-            all_n.append(2 * self._batch_size)
+            pubs.append((circuit, observable, plus + minus, precision_))
 
         # Run the single job with all circuits.
-        job = self._estimator.run(
-            job_circuits,
-            job_observables,
-            job_param_values,
-            **options,
-        )
+        job = self._estimator.run(pubs)
         try:
             results = job.result()
         except Exception as exc:
@@ -117,12 +138,10 @@ def _run(
 
         # Compute the gradients.
         gradients = []
-        partial_sum_n = 0
-        for i, n in enumerate(all_n):
-            result = results.values[partial_sum_n : partial_sum_n + n]
-            partial_sum_n += n
-            n = len(result) // 2
-            diffs = (result[:n] - result[n:]) / (2 * self._epsilon)
+        for i, result in enumerate(results):
+            evs = result.data.evs
+            n = evs.shape[0] // 2
+            diffs = (evs[:n] - evs[n:]) / (2 * self._epsilon)
             # Calculate the gradient for each batch. Note that (``diff`` / ``offset``) is the gradient
             # since ``offset`` is a perturbation vector of 1s and -1s.
             batch_gradients = np.array([diff / offset for diff, offset in zip(diffs, offsets[i])])
@@ -131,5 +150,14 @@ def _run(
             indices = [circuits[i].parameters.data.index(p) for p in metadata[i]["parameters"]]
             gradients.append(gradient[indices])
 
-        opt = self._get_local_options(options)
-        return EstimatorGradientResult(gradients=gradients, metadata=metadata, options=opt)
+        if has_transformed_precision:
+            precision = precision[0]
+
+            if precision is None:
+                precision = results[0].metadata["target_precision"]
+        else:
+            for i, (precision_, result) in enumerate(zip(precision, results)):
+                if precision_ is None:
+                    precision[i] = results[i].metadata["target_precision"]
+
+        return EstimatorGradientResult(gradients=gradients, metadata=metadata, precision=precision)
diff --git a/qiskit_algorithms/gradients/spsa/spsa_sampler_gradient.py b/qiskit_algorithms/gradients/spsa/spsa_sampler_gradient.py
index 1c25b8aa..298a0dc9 100644
--- a/qiskit_algorithms/gradients/spsa/spsa_sampler_gradient.py
+++ b/qiskit_algorithms/gradients/spsa/spsa_sampler_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,15 +16,16 @@
 
 from collections import defaultdict
 from collections.abc import Sequence
+from typing import Any
 
 import numpy as np
 
 from qiskit.circuit import Parameter, QuantumCircuit
-from qiskit.primitives import BaseSampler
-from qiskit.providers import Options
+from qiskit.primitives import BaseSamplerV2
 
 from ..base.base_sampler_gradient import BaseSamplerGradient
 from ..base.sampler_gradient_result import SamplerGradientResult
+from ...custom_types import Transpiler
 
 from ...exceptions import AlgorithmError
 
@@ -43,11 +44,14 @@ class SPSASamplerGradient(BaseSamplerGradient):
     # pylint: disable=too-many-positional-arguments
     def __init__(
         self,
-        sampler: BaseSampler,
+        sampler: BaseSamplerV2,
         epsilon: float,
         batch_size: int = 1,
         seed: int | None = None,
-        options: Options | None = None,
+        shots: int | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ):
         """
         Args:
@@ -55,10 +59,15 @@ def __init__(
             epsilon: The offset size for the SPSA gradients.
             batch_size: number of gradients to average.
             seed: The seed for a random perturbation vector.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > gradient's
-                default options > primitive's default setting.
-                Higher priority setting overrides lower priority setting
+            shots: Number of shots to be used by the underlying sampler.
+                The order of priority is: number of shots in ``run`` method > fidelity's
+                number of shots > primitive's default number of shots.
+                Higher priority setting overrides lower priority setting.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are run when using this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
 
         Raises:
             ValueError: If ``epsilon`` is not positive.
@@ -69,19 +78,32 @@ def __init__(
         self._epsilon = epsilon
         self._seed = np.random.default_rng(seed)
 
-        super().__init__(sampler, options)
+        super().__init__(
+            sampler, shots, transpiler=transpiler, transpiler_options=transpiler_options
+        )
 
     def _run(
         self,
         circuits: Sequence[QuantumCircuit],
         parameter_values: Sequence[Sequence[float]],
         parameters: Sequence[Sequence[Parameter]],
-        **options,
+        *,
+        shots: int | Sequence[int] | None = None,
     ) -> SamplerGradientResult:
         """Compute the sampler gradients on the given circuits."""
-        job_circuits, job_param_values, metadata, offsets = [], [], [], []
+        metadata, offsets = [], []
         all_n = []
-        for circuit, parameter_values_, parameters_ in zip(circuits, parameter_values, parameters):
+        has_transformed_shots = False
+
+        if isinstance(shots, int) or shots is None:
+            shots = [shots] * len(circuits)
+            has_transformed_shots = True
+
+        pubs = []
+
+        for circuit, parameter_values_, parameters_, shots_ in zip(
+            circuits, parameter_values, parameters, shots
+        ):
             # Indices of parameters to be differentiated.
             indices = [circuit.parameters.data.index(p) for p in parameters_]
             metadata.append({"parameters": parameters_})
@@ -97,12 +119,11 @@ def _run(
 
             # Combine inputs into a single job to reduce overhead.
             n = 2 * self._batch_size
-            job_circuits.extend([circuit] * n)
-            job_param_values.extend(plus + minus)
             all_n.append(n)
+            pubs.append((circuit, plus + minus, shots_))
 
         # Run the single job with all circuits.
-        job = self._sampler.run(job_circuits, job_param_values, **options)
+        job = self._sampler.run(pubs)
         try:
             results = job.result()
         except Exception as exc:
@@ -111,9 +132,12 @@ def _run(
         # Compute the gradients.
         gradients = []
         partial_sum_n = 0
-        for i, n in enumerate(all_n):
+        for i, (n, result_n) in enumerate(zip(all_n, results)):
             dist_diffs = {}
-            result = results.quasi_dists[partial_sum_n : partial_sum_n + n]
+            result = [
+                {label: value / res.num_shots for label, value in res.get_int_counts().items()}
+                for res in getattr(result_n.data, next(iter(result_n.data)))
+            ]
             for j, (dist_plus, dist_minus) in enumerate(zip(result[: n // 2], result[n // 2 :])):
                 dist_diff: dict[int, float] = defaultdict(float)
                 for key, value in dist_plus.items():
@@ -133,5 +157,14 @@ def _run(
             gradients.append(gradient)
             partial_sum_n += n
 
-        opt = self._get_local_options(options)
-        return SamplerGradientResult(gradients=gradients, metadata=metadata, options=opt)
+        if has_transformed_shots:
+            shots = shots[0]
+
+            if shots is None:
+                shots = results[0].metadata["shots"]
+        else:
+            for i, (shots_, result) in enumerate(zip(shots, results)):
+                if shots_ is None:
+                    shots[i] = result.metadata["shots"]
+
+        return SamplerGradientResult(gradients=gradients, metadata=metadata, shots=shots)
diff --git a/qiskit_algorithms/gradients/utils.py b/qiskit_algorithms/gradients/utils.py
index 53ef7fcc..5c04e9f8 100644
--- a/qiskit_algorithms/gradients/utils.py
+++ b/qiskit_algorithms/gradients/utils.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -20,6 +20,7 @@
 from collections.abc import Sequence
 from dataclasses import dataclass
 from enum import Enum
+from typing import Any
 
 import numpy as np
 
@@ -47,6 +48,8 @@
 )
 from qiskit.quantum_info import SparsePauliOp
 
+from qiskit_algorithms.custom_types import Transpiler
+
 
 ################################################################################
 ## Gradient circuits and Enum
@@ -112,7 +115,10 @@ def _make_param_shift_parameter_values(  # pylint: disable=invalid-name
 ## Linear combination gradient and Linear combination QGT
 ################################################################################
 def _make_lin_comb_gradient_circuit(
-    circuit: QuantumCircuit, add_measurement: bool = False
+    circuit: QuantumCircuit,
+    transpiler: Transpiler | None,
+    transpiler_options: dict[str, Any],
+    add_measurement: bool = False,
 ) -> dict[Parameter, QuantumCircuit]:
     """Makes a circuit that computes the linear combination of the gradient circuits."""
     circuit_temp = circuit.copy()
@@ -136,7 +142,14 @@ def _make_lin_comb_gradient_circuit(
                 lin_comb_circuit.data.insert(i, lin_comb_circuit.data.pop())
                 lin_comb_circuit.h(qr_aux)
                 if add_measurement:
+                    # Measure so that cr_aux is removed by the following line
                     lin_comb_circuit.measure(qr_aux, cr_aux)
+                    # Merge classical registers
+                    lin_comb_circuit.remove_final_measurements()
+                    lin_comb_circuit.measure_all()
+
+                if transpiler is not None:
+                    lin_comb_circuit = transpiler.run(lin_comb_circuit, **transpiler_options)
                 lin_comb_circuits[p] = lin_comb_circuit
 
     return lin_comb_circuits
diff --git a/qiskit_algorithms/minimum_eigensolvers/adapt_vqe.py b/qiskit_algorithms/minimum_eigensolvers/adapt_vqe.py
index 895a8fae..a1ffe5d9 100644
--- a/qiskit_algorithms/minimum_eigensolvers/adapt_vqe.py
+++ b/qiskit_algorithms/minimum_eigensolvers/adapt_vqe.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,27 +13,24 @@
 """An implementation of the AdaptVQE algorithm."""
 from __future__ import annotations
 
-from enum import Enum
-
-import re
 import logging
+import re
+from enum import Enum
+from typing import Iterable
 
 import numpy as np
-
-from qiskit.quantum_info.operators.base_operator import BaseOperator
 from qiskit.circuit.library import EvolvedOperatorAnsatz
+from qiskit.quantum_info import SparsePauliOp
+from qiskit.quantum_info.operators.base_operator import BaseOperator
 
-from qiskit_algorithms.utils.validation import validate_min
 from qiskit_algorithms.exceptions import AlgorithmError
-
 from qiskit_algorithms.list_or_dict import ListOrDict
-
+from qiskit_algorithms.utils.validation import validate_min
 from .minimum_eigensolver import MinimumEigensolver
 from .vqe import VQE, VQEResult
 from ..observables_evaluator import estimate_observables
 from ..variational_algorithm import VariationalAlgorithm
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -62,7 +59,7 @@ class AdaptVQE(VariationalAlgorithm, MinimumEigensolver):
 
       from qiskit_algorithms.minimum_eigensolvers import AdaptVQE, VQE
       from qiskit_algorithms.optimizers import SLSQP
-      from qiskit.primitives import Estimator
+      from qiskit.primitives import StatevectorEstimator
       from qiskit.circuit.library import EvolvedOperatorAnsatz
 
       # get your Hamiltonian
@@ -71,7 +68,7 @@ class AdaptVQE(VariationalAlgorithm, MinimumEigensolver):
       # construct your ansatz
       ansatz = EvolvedOperatorAnsatz(...)
 
-      vqe = VQE(Estimator(), ansatz, SLSQP())
+      vqe = VQE(StatevectorEstimator(), ansatz, SLSQP())
 
       adapt_vqe = AdaptVQE(vqe)
 
@@ -117,6 +114,11 @@ def __init__(
                 are not considered.
             max_iterations: the maximum number of iterations for the adaptive loop. If ``None``, the
                 algorithm is not bound in its number of iterations.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are run when using this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
         validate_min("gradient_threshold", gradient_threshold, 1e-15)
         validate_min("eigenvalue_threshold", eigenvalue_threshold, 1e-15)
@@ -146,7 +148,7 @@ def supports_aux_operators(cls) -> bool:
     def _compute_gradients(
         self,
         theta: list[float],
-        operator: BaseOperator,
+        operator: SparsePauliOp,
     ) -> ListOrDict[tuple[float, dict[str, BaseOperator]]]:
         """
         Computes the gradients for all available excitation operators.
@@ -160,6 +162,17 @@ def _compute_gradients(
         # The excitations operators are applied later as exp(i*theta*excitation).
         # For this commutator, we need to explicitly pull in the imaginary phase.
         commutators = [1j * (operator @ exc - exc @ operator) for exc in self._excitation_pool]
+        # We have to call simplify on it since Qiskit doesn't do so for now, see
+        # Qiskit/qiskit/issues/14567
+        # TODO: Remove the below line once the aforementioned issue is fixed to avoid unnecessary
+        #  overhead. The issue will be present in Qiskit 2.1 however
+        commutators = [obs.simplify() for obs in commutators]
+
+        # If the ansatz has been transpiled
+        if self.solver.ansatz.layout:
+            commutators = [
+                commutator.apply_layout(self.solver.ansatz.layout) for commutator in commutators
+            ]
         res = estimate_observables(self.solver.estimator, self.solver.ansatz, commutators, theta)
         return res
 
@@ -217,10 +230,10 @@ def compute_minimum_eigenvalue(
         # Overwrite the solver's ansatz with the initial state
         self._tmp_ansatz = self.solver.ansatz
         self._excitation_pool = self._tmp_ansatz.operators
+        # This will transpile the initial state if the solver has a transpiler that is set
         self.solver.ansatz = self._tmp_ansatz.initial_state
 
         prev_op_indices: list[int] = []
-        prev_raw_vqe_result: VQEResult | None = None
         raw_vqe_result: VQEResult | None = None
         theta: list[float] = []
         max_grad: tuple[float, dict[str, BaseOperator] | None] = (0.0, None)
@@ -321,6 +334,22 @@ def compute_minimum_eigenvalue(
 
         # once finished evaluate auxiliary operators if any
         if aux_operators is not None:
+            if self.solver.ansatz.layout is not None:
+                key_op_iterator: Iterable[tuple[str | int, BaseOperator]]
+                if isinstance(aux_operators, list):
+                    key_op_iterator = enumerate(aux_operators)
+                    # Dummy placeholder
+                    converted: ListOrDict[BaseOperator] = [
+                        SparsePauliOp.from_list([("I", 0)])
+                    ] * len(aux_operators)
+                else:
+                    key_op_iterator = aux_operators.items()
+                    converted = {}
+                for key, op in key_op_iterator:
+                    if op is not None:
+                        converted[key] = op.apply_layout(self.solver.ansatz.layout)
+
+                aux_operators = converted
             aux_values = estimate_observables(
                 self.solver.estimator,
                 self.solver.ansatz,
diff --git a/qiskit_algorithms/minimum_eigensolvers/diagonal_estimator.py b/qiskit_algorithms/minimum_eigensolvers/diagonal_estimator.py
index aaabf3b8..9a4cf863 100644
--- a/qiskit_algorithms/minimum_eigensolvers/diagonal_estimator.py
+++ b/qiskit_algorithms/minimum_eigensolvers/diagonal_estimator.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -14,40 +14,49 @@
 
 from __future__ import annotations
 
-from collections.abc import Callable, Sequence, Mapping, Iterable, MappingView
+from collections.abc import Callable, Iterable
 from typing import Any
 
-from dataclasses import dataclass
-
 import numpy as np
-from qiskit.circuit import QuantumCircuit
-from qiskit.primitives import BaseSampler, BaseEstimator, EstimatorResult
-from qiskit.primitives.utils import init_observable, _circuit_key
+from qiskit.primitives import (
+    BaseSamplerV2,
+    BaseEstimatorV2,
+    PubResult,
+    EstimatorPubLike,
+    DataBin,
+    SamplerPubLike,
+    PrimitiveResult,
+)
+from qiskit.primitives.containers.estimator_pub import EstimatorPub
 from qiskit.quantum_info import SparsePauliOp
-from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from qiskit_algorithms.algorithm_job import AlgorithmJob
 
 
-@dataclass(frozen=True)
-class _DiagonalEstimatorResult(EstimatorResult):
+class _DiagonalEstimatorResult(PubResult):
     """A result from an expectation of a diagonal observable."""
 
-    # TODO make each measurement a dataclass rather than a dict
-    best_measurements: Sequence[Mapping[str, Any]] | None = None
+    def __init__(
+        self,
+        data: DataBin,
+        metadata: dict[str, Any] | None = None,
+        best_measurements: list[dict[str, Any]] | None = None,
+    ):
+        super().__init__(data, metadata)
+        # TODO make each measurement a dataclass rather than a dict
+        self.best_measurements: list[dict[str, Any]] | None = best_measurements
 
 
-class _DiagonalEstimator(BaseEstimator):
+class _DiagonalEstimator(BaseEstimatorV2):
     """An estimator for diagonal observables."""
 
     def __init__(
         self,
-        sampler: BaseSampler,
+        sampler: BaseSamplerV2,
         aggregation: float | Callable[[Iterable[tuple[float, float]]], float] | None = None,
-        callback: Callable[[Sequence[Mapping[str, Any]]], None] | None = None,
-        **options,
+        callback: Callable[[list[dict[str, Any]]], None] | None = None,
     ) -> None:
-        r"""Evaluate the expectation of quantum state with respect to a diagonal operator.
+        r"""Evaluate the expectation of quantum state with respect to diagonal operators.
 
         Args:
             sampler: The sampler used to evaluate the circuits.
@@ -55,93 +64,82 @@ def __init__(
                 this specified the CVaR :math:`\alpha` parameter.
             callback: A callback which is given the best measurements of all circuits in each
                 evaluation.
-            run_options: Options for the sampler.
-
         """
-        super().__init__(options=options)
-        self._circuits: list[QuantumCircuit] = []  # See Qiskit pull request 11051
-        self._parameters: list[MappingView] = []
-        self._observables: list[SparsePauliOp] = []
-
         self.sampler = sampler
+
         if not callable(aggregation):
             aggregation = _get_cvar_aggregation(aggregation)
 
         self.aggregation = aggregation
         self.callback = callback
-        self._circuit_ids: dict[int, QuantumCircuit] = {}
-        self._observable_ids: dict[int, BaseOperator] = {}
 
-    def _run(
-        self,
-        circuits: Sequence[QuantumCircuit],
-        observables: Sequence[BaseOperator],
-        parameter_values: Sequence[Sequence[float]],
-        **run_options,
-    ) -> AlgorithmJob:
-        circuit_indices = []
-        for circuit in circuits:
-            key = _circuit_key(circuit)
-            index = self._circuit_ids.get(key)
-            if index is not None:
-                circuit_indices.append(index)
-            else:
-                circuit_indices.append(len(self._circuits))
-                self._circuit_ids[key] = len(self._circuits)
-                self._circuits.append(circuit)
-                self._parameters.append(circuit.parameters)
-        observable_indices = []
-        for observable in observables:
-            index = self._observable_ids.get(id(observable))
-            if index is not None:
-                observable_indices.append(index)
-            else:
-                observable_indices.append(len(self._observables))
-                self._observable_ids[id(observable)] = len(self._observables)
-                converted_observable = init_observable(observable)
-                _check_observable_is_diagonal(converted_observable)  # check it's diagonal
-                self._observables.append(converted_observable)
-        job = AlgorithmJob(
-            self._call, circuit_indices, observable_indices, parameter_values, **run_options
-        )
-        job.submit()
+    # If precision is set to None, the default number of shots of the Sampler will be used. It will
+    # otherwise be computed as a function of the observable and the precision
+    def run(
+        self, pubs: Iterable[EstimatorPubLike], *, precision: float | None = None
+    ) -> AlgorithmJob[PrimitiveResult[_DiagonalEstimatorResult]]:
+        # Since we will convert the standalone observables to a list, this `observables` list will
+        # remember the shape of the original observables, either standalone or in a list.
+        coerced_pubs = [EstimatorPub.coerce(pub, precision) for pub in pubs]
+
+        job = AlgorithmJob(self._run, coerced_pubs)
+        job._submit()
         return job
 
-    def _call(
-        self,
-        circuits: Sequence[int],
-        observables: Sequence[int],
-        parameter_values: Sequence[Sequence[float]],
-        **run_options,
-    ) -> _DiagonalEstimatorResult:
-        job = self.sampler.run(
-            [self._circuits[i] for i in circuits],
-            parameter_values,
-            **run_options,
-        )
-        sampler_result = job.result()
-        samples = sampler_result.quasi_dists
+    def _run(self, pubs: list[EstimatorPub]) -> PrimitiveResult[_DiagonalEstimatorResult]:
+        return PrimitiveResult([self._run_pub(pub) for pub in pubs])
+
+    # Adapted from StatevectorEstimator, OK with the license?
+    def _run_pub(self, pub: EstimatorPub) -> _DiagonalEstimatorResult:
+        circuit = pub.circuit
+        observables = pub.observables
+        parameter_values = pub.parameter_values
+        bound_circuits = parameter_values.bind_all(circuit)
+        bc_circuits, bc_obs = np.broadcast_arrays(bound_circuits, observables)
+        sampler_pubs: list[SamplerPubLike] = []
+        evs = np.zeros_like(bc_circuits, dtype=np.float64)
+        best_measurements = []
 
-        # a list of dictionaries containing: {state: (measurement probability, value)}
-        evaluations: list[dict[int, tuple[float, float]]] = [
-            {
-                state: (probability, _evaluate_sparsepauli(state, self._observables[i]))
+        for index in np.ndindex(*bc_circuits.shape):
+            bound_circuit = bc_circuits[index]
+            observable = bc_obs[index]
+            paulis, coeffs = zip(*observable.items())
+            obs = SparsePauliOp(paulis, coeffs)
+            _check_observable_is_diagonal(obs)
+
+            if pub.precision is None:
+                sampler_pubs.append((bound_circuit.measure_all(inplace=False),))
+            else:
+                sampler_pubs.append(
+                    (
+                        bound_circuit.measure_all(inplace=False),
+                        None,
+                        # Ensures a standard deviation of at most pub.precision
+                        round((sum(obs.coeffs) / pub.precision) ** 2),
+                    )
+                )
+
+        job = self.sampler.run(sampler_pubs)
+        sampler_pubs_results = job.result()
+
+        for sampler_pub_result, index in zip(sampler_pubs_results, np.ndindex(*bc_circuits.shape)):
+            observable = bc_obs[index]
+            paulis, coeffs = zip(*observable.items())
+            obs = SparsePauliOp(paulis, coeffs)
+            sampled = {
+                label: value / sampler_pub_result.data.meas.num_shots
+                for label, value in sampler_pub_result.data.meas.get_int_counts().items()
+            }
+            evaluated = {
+                state: (probability, _evaluate_sparsepauli(state, obs))
                 for state, probability in sampled.items()
             }
-            for i, sampled in zip(observables, samples)
-        ]
-
-        results = np.array([self.aggregation(evaluated.values()) for evaluated in evaluations])
-
-        # get the best measurements
-        best_measurements = []
-        num_qubits = self._circuits[0].num_qubits
-        for evaluated in evaluations:
+            evs[index] = np.real_if_close(self.aggregation(evaluated.values()))
             best_result = min(evaluated.items(), key=lambda x: x[1][1])
             best_measurements.append(
                 {
                     "state": best_result[0],
-                    "bitstring": bin(best_result[0])[2:].zfill(num_qubits),
+                    "bitstring": bin(best_result[0])[2:].zfill(pub.circuit.num_qubits),
                     "value": best_result[1][1],
                     "probability": best_result[1][0],
                 }
@@ -150,8 +148,15 @@ def _call(
         if self.callback is not None:
             self.callback(best_measurements)
 
+        data = DataBin(evs=evs, shape=evs.shape)
+
         return _DiagonalEstimatorResult(
-            values=results, metadata=sampler_result.metadata, best_measurements=best_measurements
+            data,
+            metadata={
+                "circuit_metadata": pub.circuit.metadata,
+                "target_precision": pub.precision,
+            },
+            best_measurements=best_measurements,
         )
 
 
diff --git a/qiskit_algorithms/minimum_eigensolvers/qaoa.py b/qiskit_algorithms/minimum_eigensolvers/qaoa.py
index 8953b795..bdc8b7dc 100644
--- a/qiskit_algorithms/minimum_eigensolvers/qaoa.py
+++ b/qiskit_algorithms/minimum_eigensolvers/qaoa.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -20,12 +20,13 @@
 from qiskit.circuit import QuantumCircuit
 from qiskit.circuit.library.n_local.qaoa_ansatz import QAOAAnsatz
 from qiskit.quantum_info.operators.base_operator import BaseOperator
-from qiskit.primitives import BaseSampler
+from qiskit.primitives import BaseSamplerV2
 
 from qiskit_algorithms.utils.validation import validate_min
 from qiskit_algorithms.optimizers import Minimizer, Optimizer
 
 from .sampling_vqe import SamplingVQE
+from ..custom_types import Transpiler
 
 
 class QAOA(SamplingVQE):
@@ -54,7 +55,7 @@ class QAOA(SamplingVQE):
     the QAOA object has been constructed.
 
     Attributes:
-        sampler (BaseSampler): The sampler primitive to sample the circuits.
+        sampler (BaseSamplerV2): The sampler primitive to sample the circuits.
         optimizer (Optimizer | Minimizer): A classical optimizer to find the minimum energy. This
             can either be an :class:`.Optimizer` or a callable implementing the
             :class:`.Minimizer` protocol.
@@ -72,6 +73,7 @@ class QAOA(SamplingVQE):
             evaluation count, the optimizer parameters for the ansatz, the evaluated value, and
             the metadata dictionary.
 
+
     References:
         [1]: Farhi, E., Goldstone, J., Gutmann, S., "A Quantum Approximate Optimization Algorithm"
             `arXiv:1411.4028 <https://arxiv.org/abs/1411.4028>`__
@@ -85,7 +87,7 @@ class QAOA(SamplingVQE):
 
     def __init__(
         self,
-        sampler: BaseSampler,
+        sampler: BaseSamplerV2,
         optimizer: Optimizer | Minimizer,
         *,
         reps: int = 1,
@@ -94,6 +96,8 @@ def __init__(
         initial_point: np.ndarray | None = None,
         aggregation: float | Callable[[list[float]], float] | None = None,
         callback: Callable[[int, np.ndarray, float, dict[str, Any]], None] | None = None,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         r"""
         Args:
@@ -117,6 +121,10 @@ def __init__(
             callback: A callback that can access the intermediate data at each optimization step.
                 These data are: the evaluation count, the optimizer parameters for the ansatz, the
                 evaluated value, the metadata dictionary.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
         validate_min("reps", reps, 1)
 
@@ -124,6 +132,8 @@ def __init__(
         self.mixer = mixer
         self.initial_state = initial_state
         self._cost_operator = None
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
 
         super().__init__(
             sampler=sampler,
@@ -136,6 +146,9 @@ def __init__(
 
     def _check_operator_ansatz(self, operator: BaseOperator):
         # Recreates a circuit based on operator parameter.
-        self.ansatz = QAOAAnsatz(
+        ansatz = QAOAAnsatz(
             operator, self.reps, initial_state=self.initial_state, mixer_operator=self.mixer
-        ).decompose()  # TODO remove decompose once #6674 is fixed
+        )
+        if self._transpiler is not None:
+            ansatz = self._transpiler.run(ansatz, **self._transpiler_options)
+        self.ansatz = ansatz
diff --git a/qiskit_algorithms/minimum_eigensolvers/sampling_vqe.py b/qiskit_algorithms/minimum_eigensolvers/sampling_vqe.py
index 0ebd7e05..9b7b4663 100644
--- a/qiskit_algorithms/minimum_eigensolvers/sampling_vqe.py
+++ b/qiskit_algorithms/minimum_eigensolvers/sampling_vqe.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -22,7 +22,7 @@
 import numpy as np
 
 from qiskit.circuit import QuantumCircuit
-from qiskit.primitives import BaseSampler
+from qiskit.primitives import BaseSamplerV2
 from qiskit.result import QuasiDistribution
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
@@ -92,7 +92,7 @@ def my_minimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
     the ``SamplingVQE`` object has been constructed.
 
     Attributes:
-        sampler (BaseSampler): The sampler primitive to sample the circuits.
+        sampler (BaseSamplerV2): The sampler primitive to sample the circuits.
         ansatz (QuantumCircuit): A parameterized quantum circuit to prepare the trial state.
         optimizer (Optimizer | Minimizer): A classical optimizer to find the minimum energy. This
             can either be an :class:`.Optimizer` or a callable implementing the
@@ -116,7 +116,7 @@ def my_minimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
 
     def __init__(
         self,
-        sampler: BaseSampler,
+        sampler: BaseSamplerV2,
         ansatz: QuantumCircuit,
         optimizer: Optimizer | Minimizer,
         *,
@@ -240,7 +240,12 @@ def compute_minimum_eigenvalue(
             optimizer_result.x,
         )
 
-        final_state = self.sampler.run([self.ansatz], [optimizer_result.x]).result().quasi_dists[0]
+        final_res = self.sampler.run([(self.ansatz, optimizer_result.x)]).result()
+        final_state = getattr(final_res[0].data, self.ansatz.cregs[0].name)
+        final_state = {
+            label: value / final_state.num_shots
+            for label, value in final_state.get_counts().items()
+        }
 
         if aux_operators is not None:
             aux_operators_evaluated = estimate_observables(
@@ -314,18 +319,16 @@ def evaluate_energy(parameters: np.ndarray) -> np.ndarray | float:
             nonlocal eval_count
             # handle broadcasting: ensure parameters is of shape [array, array, ...]
             parameters = np.reshape(parameters, (-1, num_parameters)).tolist()
-            batch_size = len(parameters)
-
-            estimator_result = estimator.run(
-                batch_size * [ansatz], batch_size * [operator], parameters
-            ).result()
-            values = estimator_result.values
+            job = estimator.run([(ansatz, operator, parameters)])
+            estimator_result = job.result()[0]
+            values = estimator_result.data.evs
+            if not values.shape:
+                values = values.reshape(1)
 
             if self.callback is not None:
-                metadata = estimator_result.metadata
-                for params, value, meta in zip(parameters, values, metadata):
+                for params, value in zip(parameters, values):
                     eval_count += 1
-                    self.callback(eval_count, params, value, meta)
+                    self.callback(eval_count, params, value, estimator_result.metadata)
 
             result = values if len(values) > 1 else values[0]
             return np.real(result)
diff --git a/qiskit_algorithms/minimum_eigensolvers/vqe.py b/qiskit_algorithms/minimum_eigensolvers/vqe.py
index b0e85a67..20f637f9 100644
--- a/qiskit_algorithms/minimum_eigensolvers/vqe.py
+++ b/qiskit_algorithms/minimum_eigensolvers/vqe.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -17,15 +17,17 @@
 import logging
 from time import time
 from collections.abc import Callable
-from typing import Any
+from typing import Any, Iterable
 
 import numpy as np
 
 from qiskit.circuit import QuantumCircuit
-from qiskit.primitives import BaseEstimator
+from qiskit.primitives import BaseEstimatorV2
+from qiskit.quantum_info import SparsePauliOp
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from qiskit_algorithms.gradients import BaseEstimatorGradient
+from ..custom_types import Transpiler
 
 from ..exceptions import AlgorithmError
 from ..list_or_dict import ListOrDict
@@ -94,7 +96,7 @@ def my_minimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
     the VQE object has been constructed.
 
     Attributes:
-        estimator (BaseEstimator): The estimator primitive to compute the expectation value of the
+        estimator (BaseEstimatorV2): The estimator primitive to compute the expectation value of the
             Hamiltonian operator.
         ansatz (QuantumCircuit): A parameterized quantum circuit to prepare the trial state.
         optimizer (Optimizer | Minimizer): A classical optimizer to find the minimum energy. This
@@ -114,13 +116,15 @@ def my_minimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
 
     def __init__(
         self,
-        estimator: BaseEstimator,
+        estimator: BaseEstimatorV2,
         ansatz: QuantumCircuit,
         optimizer: Optimizer | Minimizer,
         *,
         gradient: BaseEstimatorGradient | None = None,
         initial_point: np.ndarray | None = None,
         callback: Callable[[int, np.ndarray, float, dict[str, Any]], None] | None = None,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         r"""
         Args:
@@ -139,17 +143,28 @@ def __init__(
             callback: A callback that can access the intermediate data at each optimization step.
                 These data are: the evaluation count, the optimizer parameters for the ansatz, the
                 estimated value, and the metadata dictionary.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are run when using this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
         super().__init__()
 
         self.estimator = estimator
-        self.ansatz = ansatz
+        self._ansatz = ansatz
         self.optimizer = optimizer
         self.gradient = gradient
         # this has to go via getters and setters due to the VariationalAlgorithm interface
         self.initial_point = initial_point
         self.callback = callback
 
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
+
+        if self._transpiler is not None:
+            self._ansatz = self._transpiler.run(self._ansatz, **self._transpiler_options)
+
     @property
     def initial_point(self) -> np.ndarray | None:
         return self._initial_point
@@ -158,11 +173,25 @@ def initial_point(self) -> np.ndarray | None:
     def initial_point(self, value: np.ndarray | None) -> None:
         self._initial_point = value
 
+    @property
+    def ansatz(self) -> QuantumCircuit:
+        return self._ansatz
+
+    @ansatz.setter
+    def ansatz(self, value: QuantumCircuit | None) -> None:
+        if self._transpiler is not None:
+            self._ansatz = self._transpiler.run(value, **self._transpiler_options)
+        else:
+            self._ansatz = value
+
     def compute_minimum_eigenvalue(
         self,
         operator: BaseOperator,
         aux_operators: ListOrDict[BaseOperator] | None = None,
     ) -> VQEResult:
+        if self.ansatz.layout is not None:
+            operator = operator.apply_layout(self.ansatz.layout)
+
         self._check_operator_ansatz(operator)
 
         initial_point = validate_initial_point(self.initial_point, self.ansatz)
@@ -211,6 +240,29 @@ def compute_minimum_eigenvalue(
         )
 
         if aux_operators is not None:
+            if self.ansatz.layout is not None:
+                # We need to handle the array entries being zero or Optional i.e. having value None
+                # len(self.ansatz.layout.final_index_layout()) is the original number of qubits in the
+                # ansatz, before transpilation
+                zero_op = SparsePauliOp.from_list(
+                    [("I" * len(self.ansatz.layout.final_index_layout()), 0)]
+                )
+                key_op_iterator: Iterable[tuple[str | int, BaseOperator]]
+                if isinstance(aux_operators, list):
+                    key_op_iterator = enumerate(aux_operators)
+                    converted: ListOrDict[BaseOperator] = [zero_op] * len(aux_operators)
+                else:
+                    key_op_iterator = aux_operators.items()
+                    converted = {}
+                for key, op in key_op_iterator:
+                    if op is not None:
+                        converted[key] = (
+                            zero_op.apply_layout(self.ansatz.layout)
+                            if op == 0
+                            else op.apply_layout(self.ansatz.layout)
+                        )
+
+                aux_operators = converted
             aux_operators_evaluated = estimate_observables(
                 self.estimator,
                 self.ansatz,
@@ -258,22 +310,23 @@ def evaluate_energy(parameters: np.ndarray) -> np.ndarray | float:
             nonlocal eval_count
 
             # handle broadcasting: ensure parameters is of shape [array, array, ...]
-            parameters = np.reshape(parameters, (-1, num_parameters)).tolist()
-            batch_size = len(parameters)
+            parameters = np.reshape(parameters, (-1, num_parameters))
 
             try:
-                job = self.estimator.run(batch_size * [ansatz], batch_size * [operator], parameters)
-                estimator_result = job.result()
+                job = self.estimator.run([(ansatz, operator, parameters)])
+                estimator_result = job.result()[0]
             except Exception as exc:
                 raise AlgorithmError("The primitive job to evaluate the energy failed!") from exc
 
-            values = estimator_result.values
+            values = estimator_result.data.evs
+
+            if not values.shape:
+                values = values.reshape(1)
 
             if self.callback is not None:
-                metadata = estimator_result.metadata
-                for params, value, meta in zip(parameters, values, metadata):
+                for params, value in zip(parameters.reshape(-1, 1), values):
                     eval_count += 1
-                    self.callback(eval_count, params, value, meta)
+                    self.callback(eval_count, params, value, estimator_result.metadata)
 
             energy = values[0] if len(values) == 1 else values
 
diff --git a/qiskit_algorithms/observables_evaluator.py b/qiskit_algorithms/observables_evaluator.py
index ae125bfb..da898982 100644
--- a/qiskit_algorithms/observables_evaluator.py
+++ b/qiskit_algorithms/observables_evaluator.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2023.
+# (C) Copyright IBM 2021, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -20,15 +20,16 @@
 
 from qiskit import QuantumCircuit
 from qiskit.quantum_info import SparsePauliOp
-from qiskit.primitives import BaseEstimator
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from .exceptions import AlgorithmError
 from .list_or_dict import ListOrDict
 
 
+# TODO: make estimate_observables accept EstimatorPubLike inputs
 def estimate_observables(
-    estimator: BaseEstimator,
+    estimator: BaseEstimatorV2,
     quantum_state: QuantumCircuit,
     observables: ListOrDict[BaseOperator],
     parameter_values: Sequence[float] | None = None,
@@ -42,7 +43,7 @@ def estimate_observables(
     Args:
         estimator: An estimator primitive used for calculations.
         quantum_state: A (parameterized) quantum circuit preparing a quantum state that expectation
-            values are computed against.
+            values are computed against. It is expected to be an ISA circuit.
         observables: A list or a dictionary of operators whose expectation values are to be
             calculated.
         parameter_values: Optional list of parameters values to evaluate the quantum circuit on.
@@ -63,21 +64,19 @@ def estimate_observables(
 
     if len(observables_list) > 0:
         observables_list = _handle_zero_ops(observables_list)
-        quantum_state = [quantum_state] * len(observables)
         parameter_values_: Sequence[float] | Sequence[Sequence[float]] | None = parameter_values
-        if parameter_values is not None:
-            parameter_values_ = [parameter_values] * len(observables)
         try:
-            estimator_job = estimator.run(quantum_state, observables_list, parameter_values_)
-            expectation_values = estimator_job.result().values
+            estimator_job = estimator.run([(quantum_state, observables_list, parameter_values_)])
+            estimator_result = estimator_job.result()[0]
+            expectation_values = estimator_result.data.evs
         except Exception as exc:
             raise AlgorithmError("The primitive job failed!") from exc
 
-        metadata = estimator_job.result().metadata
+        metadata = estimator_result.metadata
         # Discard values below threshold
         observables_means = expectation_values * (np.abs(expectation_values) > threshold)
         # zip means and metadata into tuples
-        observables_results = list(zip(observables_means, metadata))
+        observables_results = list(zip(observables_means, [metadata] * len(observables_means)))
     else:
         observables_results = []
 
diff --git a/qiskit_algorithms/optimizers/qnspsa.py b/qiskit_algorithms/optimizers/qnspsa.py
index 3a0ea8e0..86031d36 100644
--- a/qiskit_algorithms/optimizers/qnspsa.py
+++ b/qiskit_algorithms/optimizers/qnspsa.py
@@ -20,10 +20,11 @@
 import numpy as np
 from qiskit.circuit import QuantumCircuit
 
-from qiskit.primitives import BaseSampler
+from qiskit.primitives import BaseSamplerV2
 from qiskit_algorithms.state_fidelities import ComputeUncompute
 
 from .spsa import SPSA, CALLBACK, TERMINATIONCHECKER, _batch_evaluate
+from ..custom_types import Transpiler
 
 # the function to compute the fidelity
 FIDELITY = Callable[[np.ndarray, np.ndarray], float]
@@ -63,7 +64,7 @@ class QNSPSA(SPSA):
             import numpy as np
             from qiskit_algorithms.optimizers import QNSPSA
             from qiskit.circuit.library import PauliTwoDesign
-            from qiskit.primitives import Estimator, Sampler
+            from qiskit.primitives import StatevectorEstimator, StatevectorSampler
             from qiskit.quantum_info import Pauli
 
             # problem setup
@@ -72,14 +73,14 @@ class QNSPSA(SPSA):
             initial_point = np.random.random(ansatz.num_parameters)
 
             # loss function
-            estimator = Estimator()
+            estimator = StatevectorEstimator()
 
             def loss(x):
-                result = estimator.run([ansatz], [observable], [x]).result()
-                return np.real(result.values[0])
+                result = estimator.run([(ansatz, observable, x)]).result()[0]
+                return np.real(result.data.evs[0])
 
             # fidelity for estimation of the geometric tensor
-            sampler = Sampler()
+            sampler = StatevectorSampler()
             fidelity = QNSPSA.get_fidelity(ansatz, sampler)
 
             # run QN-SPSA
@@ -232,7 +233,10 @@ def settings(self) -> dict[str, Any]:
     @staticmethod
     def get_fidelity(
         circuit: QuantumCircuit,
-        sampler: BaseSampler,
+        sampler: BaseSamplerV2,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> Callable[[np.ndarray, np.ndarray], float]:
         r"""Get a function to compute the fidelity of ``circuit`` with itself.
 
@@ -250,12 +254,19 @@ def get_fidelity(
         Args:
             circuit: The circuit preparing the parameterized ansatz.
             sampler: A sampler primitive to sample from a quantum state.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced by the fidelity object. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
 
         Returns:
             A handle to the function :math:`F`.
 
         """
-        fid = ComputeUncompute(sampler)
+        fid = ComputeUncompute(
+            sampler, transpiler=transpiler, transpiler_options=transpiler_options
+        )
 
         num_parameters = circuit.num_parameters
 
diff --git a/qiskit_algorithms/optimizers/spsa.py b/qiskit_algorithms/optimizers/spsa.py
index 1a13fb97..b9059e2b 100644
--- a/qiskit_algorithms/optimizers/spsa.py
+++ b/qiskit_algorithms/optimizers/spsa.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2024.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -90,17 +90,17 @@ class SPSA(Optimizer):
             import numpy as np
             from qiskit_algorithms.optimizers import SPSA
             from qiskit.circuit.library import PauliTwoDesign
-            from qiskit.primitives import Estimator
+            from qiskit.primitives import StatevectorEstimator
             from qiskit.quantum_info import SparsePauliOp
 
             ansatz = PauliTwoDesign(2, reps=1, seed=2)
             observable = SparsePauliOp("ZZ")
             initial_point = np.random.random(ansatz.num_parameters)
-            estimator = Estimator()
+            estimator = StatevectorEstimator()
 
             def loss(x):
-                job = estimator.run([ansatz], [observable], [x])
-                return job.result().values[0]
+                job = estimator.run([(ansatz, observable, x)])
+                return job.result()[0].data.evs
 
             spsa = SPSA(maxiter=300)
             result = spsa.minimize(loss, x0=initial_point)
diff --git a/qiskit_algorithms/optimizers/umda.py b/qiskit_algorithms/optimizers/umda.py
index f420e82d..140524d6 100644
--- a/qiskit_algorithms/optimizers/umda.py
+++ b/qiskit_algorithms/optimizers/umda.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -74,7 +74,7 @@ class UMDA(Optimizer):
             from qiskit_algorithms.optimizers import UMDA
             from qiskit_algorithms import QAOA
             from qiskit.quantum_info import Pauli
-            from qiskit.primitives import Sampler
+            from qiskit.primitives import StatevectorSampler
 
             X = Pauli("X")
             I = Pauli("I")
@@ -89,7 +89,7 @@ class UMDA(Optimizer):
             p = 2  # Toy example: 2 layers with 2 parameters in each layer: 4 variables
 
             opt = UMDA(maxiter=100, size_gen=20)
-            qaoa = QAOA(Sampler(), opt,reps=p)
+            qaoa = QAOA(StatevectorSampler(), opt,reps=p)
             result = qaoa.compute_minimum_eigenvalue(operator=H2_op)
 
         If it is desired to modify the percentage of individuals considered to update the
@@ -99,7 +99,7 @@ class UMDA(Optimizer):
         .. code-block:: python
 
             opt = UMDA(maxiter=100, size_gen=20, alpha = 0.6)
-            qaoa = QAOA(Sampler(), opt,reps=p)
+            qaoa = QAOA(StatevectorSampler(), opt,reps=p)
             result = qaoa.compute_minimum_eigenvalue(operator=H2_op)
 
     .. note::
diff --git a/qiskit_algorithms/phase_estimators/hamiltonian_phase_estimation.py b/qiskit_algorithms/phase_estimators/hamiltonian_phase_estimation.py
index bfb36e9a..e909bc7d 100644
--- a/qiskit_algorithms/phase_estimators/hamiltonian_phase_estimation.py
+++ b/qiskit_algorithms/phase_estimators/hamiltonian_phase_estimation.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2020, 2023.
+# (C) Copyright IBM 2020, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -14,16 +14,18 @@
 
 from __future__ import annotations
 
+from typing import Any
 
 from qiskit import QuantumCircuit
 from qiskit.circuit.library import PauliEvolutionGate
-from qiskit.primitives import BaseSampler
+from qiskit.primitives import BaseSamplerV2
 from qiskit.quantum_info import SparsePauliOp, Statevector, Pauli
 from qiskit.synthesis import EvolutionSynthesis
 
-from .phase_estimation import PhaseEstimation
 from .hamiltonian_phase_estimation_result import HamiltonianPhaseEstimationResult
+from .phase_estimation import PhaseEstimation
 from .phase_estimation_scale import PhaseEstimationScale
+from ..custom_types import Transpiler
 
 
 class HamiltonianPhaseEstimation:
@@ -83,17 +85,27 @@ class HamiltonianPhaseEstimation:
     def __init__(
         self,
         num_evaluation_qubits: int,
-        sampler: BaseSampler | None = None,
+        sampler: BaseSamplerV2 | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         r"""
         Args:
             num_evaluation_qubits: The number of qubits used in estimating the phase. The phase will
                 be estimated as a binary string with this many bits.
             sampler: The sampler primitive on which the circuit will be sampled.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
         """
         self._phase_estimation = PhaseEstimation(
             num_evaluation_qubits=num_evaluation_qubits,
             sampler=sampler,
+            transpiler=transpiler,
+            transpiler_options=transpiler_options,
         )
 
     def _get_scale(self, hamiltonian, bound=None) -> PhaseEstimationScale:
diff --git a/qiskit_algorithms/phase_estimators/ipe.py b/qiskit_algorithms/phase_estimators/ipe.py
index bac41756..a7fa880b 100644
--- a/qiskit_algorithms/phase_estimators/ipe.py
+++ b/qiskit_algorithms/phase_estimators/ipe.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2024.
+# (C) Copyright IBM 2021, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -14,17 +14,18 @@
 """The Iterative Quantum Phase Estimation Algorithm."""
 
 from __future__ import annotations
+from typing import Any
 
 import numpy
 
-from qiskit.circuit import QuantumCircuit, QuantumRegister
-from qiskit.circuit.classicalregister import ClassicalRegister
-from qiskit.primitives import BaseSampler
+from qiskit.circuit import ClassicalRegister, QuantumCircuit, QuantumRegister
+from qiskit.primitives import BaseSamplerV2
 
 from qiskit_algorithms.exceptions import AlgorithmError
 
 from .phase_estimator import PhaseEstimator
 from .phase_estimator import PhaseEstimatorResult
+from ..custom_types import Transpiler
 
 
 class IterativePhaseEstimation(PhaseEstimator):
@@ -40,12 +41,20 @@ class IterativePhaseEstimation(PhaseEstimator):
     def __init__(
         self,
         num_iterations: int,
-        sampler: BaseSampler | None = None,
+        sampler: BaseSamplerV2 | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         r"""
         Args:
             num_iterations: The number of iterations (rounds) of the phase estimation to run.
             sampler: The sampler primitive on which the circuit will be sampled.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
 
         Raises:
             ValueError: if num_iterations is not greater than zero.
@@ -58,6 +67,8 @@ def __init__(
             raise ValueError("`num_iterations` must be greater than zero.")
         self._num_iterations = num_iterations
         self._sampler = sampler
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
 
     # pylint: disable=too-many-positional-arguments
     def construct_circuit(
@@ -126,9 +137,17 @@ def _estimate_phase_iteratively(self, unitary, state_preparation):
             qc = self.construct_circuit(
                 unitary, state_preparation, k, -2 * numpy.pi * omega_coef, True
             )
+
+            if self._transpiler is not None:
+                qc = self._transpiler.run(qc, **self._transpiler_options)
+
             try:
                 sampler_job = self._sampler.run([qc])
-                result = sampler_job.result().quasi_dists[0]
+                result = sampler_job.result()[0].data.c
+                result = {
+                    label: value / result.num_shots
+                    for label, value in result.get_int_counts().items()
+                }
             except Exception as exc:
                 raise AlgorithmError("The primitive job failed!") from exc
             x = 1 if result.get(1, 0) > result.get(0, 0) else 0
diff --git a/qiskit_algorithms/phase_estimators/phase_estimation.py b/qiskit_algorithms/phase_estimators/phase_estimation.py
index bf84b736..f3ebb2ac 100644
--- a/qiskit_algorithms/phase_estimators/phase_estimation.py
+++ b/qiskit_algorithms/phase_estimators/phase_estimation.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2020, 2023.
+# (C) Copyright IBM 2020, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -15,18 +15,20 @@
 
 from __future__ import annotations
 
+from typing import Any
+
 import numpy
 import qiskit
 from qiskit import circuit
-from qiskit.circuit import QuantumCircuit
-from qiskit.circuit.classicalregister import ClassicalRegister
-from qiskit.primitives import BaseSampler
+from qiskit.circuit import QuantumCircuit, ClassicalRegister
+from qiskit.primitives import BaseSamplerV2
 from qiskit.result import Result
 
 from qiskit_algorithms.exceptions import AlgorithmError
 
 from .phase_estimation_result import PhaseEstimationResult, _sort_phases
 from .phase_estimator import PhaseEstimator
+from ..custom_types import Transpiler
 
 
 class PhaseEstimation(PhaseEstimator):
@@ -82,13 +84,21 @@ class PhaseEstimation(PhaseEstimator):
     def __init__(
         self,
         num_evaluation_qubits: int,
-        sampler: BaseSampler | None = None,
+        sampler: BaseSamplerV2 | None = None,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         r"""
         Args:
             num_evaluation_qubits: The number of qubits used in estimating the phase. The phase will
                 be estimated as a binary string with this many bits.
             sampler: The sampler primitive on which the circuit will be sampled.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
 
         Raises:
             AlgorithmError: If a sampler is not provided
@@ -101,6 +111,8 @@ def __init__(
             self._num_evaluation_qubits = num_evaluation_qubits
 
         self._sampler = sampler
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
 
     def construct_circuit(
         self, unitary: QuantumCircuit, state_preparation: QuantumCircuit | None = None
@@ -189,6 +201,9 @@ def estimate_from_pe_circuit(self, pe_circuit: QuantumCircuit) -> PhaseEstimatio
             AlgorithmError: Primitive job failed.
         """
 
+        if self._transpiler is not None:
+            pe_circuit = self._transpiler.run(pe_circuit, **self._transpiler_options)
+
         self._add_measurement_if_required(pe_circuit)
 
         try:
@@ -196,11 +211,13 @@ def estimate_from_pe_circuit(self, pe_circuit: QuantumCircuit) -> PhaseEstimatio
             circuit_result = circuit_job.result()
         except Exception as exc:
             raise AlgorithmError("The primitive job failed!") from exc
-        phases = circuit_result.quasi_dists[0]
+        phases = circuit_result[0].data.meas.get_counts()
+        # Ensure we still return the measurement strings in sorted order, which SamplerV2 doesn't
+        # guarantee
+        measurement_labels = sorted(phases.keys())
         phases_bitstrings = {}
-        for key, phase in phases.items():
-            bitstring_key = self._get_reversed_bitstring(self._num_evaluation_qubits, key)
-            phases_bitstrings[bitstring_key] = phase
+        for key in measurement_labels:
+            phases_bitstrings[key[::-1]] = phases[key] / circuit_result[0].data.meas.num_shots
         phases = phases_bitstrings
 
         return PhaseEstimationResult(
diff --git a/qiskit_algorithms/phase_estimators/phase_estimation_result.py b/qiskit_algorithms/phase_estimators/phase_estimation_result.py
index bf57c800..e46b4cab 100644
--- a/qiskit_algorithms/phase_estimators/phase_estimation_result.py
+++ b/qiskit_algorithms/phase_estimators/phase_estimation_result.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2020, 2023.
+# (C) Copyright IBM 2020, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -23,7 +23,7 @@ class PhaseEstimationResult(PhaseEstimatorResult):
 
     This class is instantiated by the ``PhaseEstimation`` class, not via user code.
     The ``PhaseEstimation`` class generates a list of phases and corresponding weights. Upon
-    completion it returns the results as an instance of this class. The main method for
+    completion, it returns the results as an instance of this class. The main method for
     accessing the results is `filter_phases`.
 
     The canonical phase satisfying the ``PhaseEstimator`` interface, returned by the
diff --git a/qiskit_algorithms/phase_estimators/phase_estimator.py b/qiskit_algorithms/phase_estimators/phase_estimator.py
index 1f0f5002..2a78f7f8 100644
--- a/qiskit_algorithms/phase_estimators/phase_estimator.py
+++ b/qiskit_algorithms/phase_estimators/phase_estimator.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2020, 2023.
+# (C) Copyright IBM 2020, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -38,10 +38,6 @@ def estimate(
         """Estimate the phase."""
         raise NotImplementedError
 
-    @staticmethod
-    def _get_reversed_bitstring(length: int, number: int) -> str:
-        return f"{number:b}".zfill(length)[::-1]
-
 
 class PhaseEstimatorResult(AlgorithmResult):
     """Phase Estimator Result."""
diff --git a/qiskit_algorithms/state_fidelities/base_state_fidelity.py b/qiskit_algorithms/state_fidelities/base_state_fidelity.py
index 5c1199c4..b9f1b0b4 100644
--- a/qiskit_algorithms/state_fidelities/base_state_fidelity.py
+++ b/qiskit_algorithms/state_fidelities/base_state_fidelity.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,14 +16,15 @@
 from __future__ import annotations
 from abc import ABC, abstractmethod
 from collections.abc import MutableMapping
-from typing import cast, Sequence, List
+from typing import cast, Sequence, List, Any
 import numpy as np
 
 from qiskit import QuantumCircuit
 from qiskit.circuit import ParameterVector
-from qiskit.primitives.utils import _circuit_key
 
 from ..algorithm_job import AlgorithmJob
+from ..custom_types import Transpiler
+from ..utils.circuit_key import _circuit_key
 
 
 class BaseStateFidelity(ABC):
@@ -42,10 +43,24 @@ class BaseStateFidelity(ABC):
 
     """
 
-    def __init__(self) -> None:
-
+    def __init__(
+        self,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
+    ) -> None:
+        r"""
+        Args:
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
+        """
         # use cache for preventing unnecessary circuit compositions
         self._circuit_cache: MutableMapping[tuple[int, int], QuantumCircuit] = {}
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
 
     @staticmethod
     def _preprocess_values(
@@ -195,6 +210,9 @@ def _construct_circuits(
                 # update cache
                 self._circuit_cache[_circuit_key(circuit_1), _circuit_key(circuit_2)] = circuit
 
+        if self._transpiler is not None:
+            return self._transpiler.run(circuits, **self._transpiler_options)
+
         return circuits
 
     def _construct_value_list(
@@ -245,7 +263,8 @@ def _run(
         circuits_2: QuantumCircuit | Sequence[QuantumCircuit],
         values_1: Sequence[float] | Sequence[Sequence[float]] | None = None,
         values_2: Sequence[float] | Sequence[Sequence[float]] | None = None,
-        **options,
+        *,
+        shots: int | Sequence[int] | None = None,
     ) -> AlgorithmJob:
         r"""
         Computes the state overlap (fidelity) calculation between two
@@ -257,10 +276,11 @@ def _run(
             circuits_2: (Parametrized) quantum circuits preparing :math:`|\phi\rangle`.
             values_1: Numerical parameters to be bound to the first set of circuits
             values_2: Numerical parameters to be bound to the second set of circuits.
-            options: Primitive backend runtime options used for circuit execution. The order
-                of priority is\: options in ``run`` method > fidelity's default
-                options > primitive's default setting.
-                Higher priority setting overrides lower priority setting.
+            shots: Number of shots to be used by the underlying Sampler. If a single integer is
+                provided, this number will be used for all circuits. If a sequence of integers is
+                provided, they will be used on a per-circuit basis. If not set, the fidelity's default
+                number of shots will be used for all circuits, and if that is None (not set) then the
+                underlying primitive's default number of shots will be used for all circuits.
 
         Returns:
             A newly constructed algorithm job instance to get the fidelity result.
@@ -273,7 +293,8 @@ def run(
         circuits_2: QuantumCircuit | Sequence[QuantumCircuit],
         values_1: Sequence[float] | Sequence[Sequence[float]] | None = None,
         values_2: Sequence[float] | Sequence[Sequence[float]] | None = None,
-        **options,
+        *,
+        shots: int | Sequence[int] | None = None,
     ) -> AlgorithmJob:
         r"""
         Runs asynchronously the state overlap (fidelity) calculation between two
@@ -286,18 +307,20 @@ def run(
             circuits_2: (Parametrized) quantum circuits preparing :math:`|\phi\rangle`.
             values_1: Numerical parameters to be bound to the first set of circuits.
             values_2: Numerical parameters to be bound to the second set of circuits.
-            options: Primitive backend runtime options used for circuit execution. The order
-                of priority is\: options in ``run`` method > fidelity's default
-                options > primitive's default setting.
-                Higher priority setting overrides lower priority setting.
+            shots: Number of shots to be used by the underlying sampler. If a single integer is
+                provided, this number will be used for all circuits. If a sequence of integers is
+                provided, they will be used on a per-circuit basis. If none is provided, the
+                fidelity's default number of shots will be used for all circuits. If this number is
+                also set to None, the underlying primitive's default number of shots will be used
+                for all circuits.
 
         Returns:
             Primitive job for the fidelity calculation.
             The job's result is an instance of :class:`.StateFidelityResult`.
         """
-        job = self._run(circuits_1, circuits_2, values_1, values_2, **options)
+        job = self._run(circuits_1, circuits_2, values_1, values_2, shots=shots)
 
-        job.submit()
+        job._submit()
         return job
 
     @staticmethod
diff --git a/qiskit_algorithms/state_fidelities/compute_uncompute.py b/qiskit_algorithms/state_fidelities/compute_uncompute.py
index b16e4011..14198038 100644
--- a/qiskit_algorithms/state_fidelities/compute_uncompute.py
+++ b/qiskit_algorithms/state_fidelities/compute_uncompute.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -14,18 +14,20 @@
 """
 
 from __future__ import annotations
+
 from collections.abc import Sequence
-from copy import copy
+from typing import Any
 
 from qiskit import QuantumCircuit
-from qiskit.primitives import BaseSampler
+from qiskit.primitives import BaseSamplerV2
+from qiskit.primitives.containers.sampler_pub import SamplerPub
 from qiskit.primitives.primitive_job import PrimitiveJob
-from qiskit.providers import Options
 
-from ..exceptions import AlgorithmError
 from .base_state_fidelity import BaseStateFidelity
 from .state_fidelity_result import StateFidelityResult
 from ..algorithm_job import AlgorithmJob
+from ..custom_types import Transpiler
+from ..exceptions import AlgorithmError
 
 
 class ComputeUncompute(BaseStateFidelity):
@@ -53,16 +55,19 @@ class ComputeUncompute(BaseStateFidelity):
 
     def __init__(
         self,
-        sampler: BaseSampler,
-        options: Options | None = None,
+        sampler: BaseSamplerV2,
+        shots: int | None = None,
         local: bool = False,
+        *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
     ) -> None:
         r"""
         Args:
             sampler: Sampler primitive instance.
-            options: Primitive backend runtime options used for circuit execution.
-                The order of priority is: options in ``run`` method > fidelity's
-                default options > primitive's default setting.
+            shots: Number of shots to be used by the underlying sampler.
+                The order of priority is: number of shots in ``run`` method > fidelity's
+                number of shots > primitive's default number of shots.
                 Higher priority setting overrides lower priority setting.
             local: If set to ``True``, the fidelity is averaged over
                 single-qubit projectors
@@ -75,20 +80,23 @@ def __init__(
                 This coincides with the standard (global) fidelity in the limit of
                 the fidelity approaching 1. Might be used to increase the variance
                 to improve trainability in algorithms such as :class:`~.time_evolvers.PVQD`.
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
 
         Raises:
-            ValueError: If the sampler is not an instance of ``BaseSampler``.
+            ValueError: If the sampler is not an instance of ``BaseSamplerV2``.
         """
-        if not isinstance(sampler, BaseSampler):
+        if not isinstance(sampler, BaseSamplerV2):
             raise ValueError(
-                f"The sampler should be an instance of BaseSampler, " f"but got {type(sampler)}"
+                f"The sampler should be an instance of BaseSamplerV2, " f"but got {type(sampler)}"
             )
-        self._sampler: BaseSampler = sampler
+        self._sampler: BaseSamplerV2 = sampler
         self._local = local
-        self._default_options = Options()
-        if options is not None:
-            self._default_options.update_options(**options)
-        super().__init__()
+        self._shots = shots
+        super().__init__(transpiler=transpiler, transpiler_options=transpiler_options)
 
     def create_fidelity_circuit(
         self, circuit_1: QuantumCircuit, circuit_2: QuantumCircuit
@@ -119,7 +127,8 @@ def _run(
         circuits_2: QuantumCircuit | Sequence[QuantumCircuit],
         values_1: Sequence[float] | Sequence[Sequence[float]] | None = None,
         values_2: Sequence[float] | Sequence[Sequence[float]] | None = None,
-        **options,
+        *,
+        shots: int | Sequence[int] | None = None,
     ) -> AlgorithmJob:
         r"""
         Computes the state overlap (fidelity) calculation between two
@@ -131,10 +140,11 @@ def _run(
             circuits_2: (Parametrized) quantum circuits preparing :math:`|\phi\rangle`.
             values_1: Numerical parameters to be bound to the first circuits.
             values_2: Numerical parameters to be bound to the second circuits.
-            options: Primitive backend runtime options used for circuit execution.
-                    The order of priority is: options in ``run`` method > fidelity's
-                    default options > primitive's default setting.
-                    Higher priority setting overrides lower priority setting.
+            shots: Number of shots to be used by the underlying Sampler. If a single integer is
+                provided, this number will be used for all circuits. If a sequence of integers is
+                provided, they will be used on a per-circuit basis. If not set, the fidelity's default
+                number of shots will be used for all circuits, and if that is None (not set) then the
+                underlying primitive's default number of shots will be used for all circuits.
 
         Returns:
             An AlgorithmJob for the fidelity calculation.
@@ -143,7 +153,6 @@ def _run(
             ValueError: At least one pair of circuits must be defined.
             AlgorithmError: If the sampler job is not completed successfully.
         """
-
         circuits = self._construct_circuits(circuits_1, circuits_2)
         if len(circuits) == 0:
             raise ValueError(
@@ -151,79 +160,88 @@ def _run(
             )
         values = self._construct_value_list(circuits_1, circuits_2, values_1, values_2)
 
-        # The priority of run options is as follows:
-        # options in `evaluate` method > fidelity's default options >
-        # primitive's default options.
-        opts = copy(self._default_options)
-        opts.update_options(**options)
+        # The priority of number of shots options is as follows:
+        # number in `run` method > fidelity's default number of shots >
+        # primitive's default number of shots.
+        if not isinstance(shots, Sequence):
+            if shots is None:
+                shots = self.shots
+            coerced_pubs = [
+                SamplerPub.coerce((circuit, value), shots)
+                for circuit, value in zip(circuits, values)
+            ]
+        else:
+            coerced_pubs = [
+                SamplerPub.coerce((circuit, value), shots_number)
+                for circuit, value, shots_number in zip(circuits, values, shots)
+            ]
 
-        sampler_job = self._sampler.run(circuits=circuits, parameter_values=values, **opts.__dict__)
+        job = self._sampler.run(coerced_pubs)
 
-        local_opts = self._get_local_options(opts.__dict__)
-        return AlgorithmJob(ComputeUncompute._call, sampler_job, circuits, self._local, local_opts)
+        return AlgorithmJob(ComputeUncompute._call, job, circuits, self._local)
 
     @staticmethod
     def _call(
-        job: PrimitiveJob, circuits: Sequence[QuantumCircuit], local: bool, local_opts: Options
+        job: PrimitiveJob, circuits: Sequence[QuantumCircuit], local: bool
     ) -> StateFidelityResult:
         try:
             result = job.result()
         except Exception as exc:
             raise AlgorithmError("Sampler job failed!") from exc
 
+        pub_results_data = [
+            getattr(pub_result.data, circuit.cregs[0].name)
+            for pub_result, circuit in zip(result, circuits)
+        ]
+        quasi_dists = [
+            {
+                label: value / prob_dist.num_shots
+                for label, value in prob_dist.get_int_counts().items()
+            }
+            for prob_dist in pub_results_data
+        ]
+
         if local:
             raw_fidelities = [
                 ComputeUncompute._get_local_fidelity(prob_dist, circuit.num_qubits)
-                for prob_dist, circuit in zip(result.quasi_dists, circuits)
+                for prob_dist, circuit in zip(quasi_dists, circuits)
             ]
         else:
             raw_fidelities = [
-                ComputeUncompute._get_global_fidelity(prob_dist) for prob_dist in result.quasi_dists
+                ComputeUncompute._get_global_fidelity(prob_dist) for prob_dist in quasi_dists
             ]
         fidelities = ComputeUncompute._truncate_fidelities(raw_fidelities)
+        shots = [pub_result_data.num_shots for pub_result_data in pub_results_data]
+
+        if len(shots) == 1:
+            shots = shots[0]
 
         return StateFidelityResult(
             fidelities=fidelities,
             raw_fidelities=raw_fidelities,
             metadata=result.metadata,
-            options=local_opts,
+            shots=shots,
         )
 
     @property
-    def options(self) -> Options:
-        """Return the union of estimator options setting and fidelity default options,
-        where, if the same field is set in both, the fidelity's default options override
-        the primitive's default setting.
+    def shots(self) -> int | None:
+        """Return the number of shots used by the `run` method of the Sampler primitive. If None,
+        the default number of shots of the primitive is used.
 
         Returns:
-            The fidelity default + estimator options.
+            The default number of shots.
         """
-        return self._get_local_options(self._default_options.__dict__)
+        return self._shots
 
-    def update_default_options(self, **options):
-        """Update the fidelity's default options setting.
+    @shots.setter
+    def shots(self, shots: int | None):
+        """Update the fidelity's default number of shots setting.
 
         Args:
-            **options: The fields to update the default options.
+            shots: The new default number of shots.
         """
 
-        self._default_options.update_options(**options)
-
-    def _get_local_options(self, options: Options) -> Options:
-        """Return the union of the primitive's default setting,
-        the fidelity default options, and the options in the ``run`` method.
-        The order of priority is: options in ``run`` method > fidelity's
-                default options > primitive's default setting.
-
-        Args:
-            options: The fields to update the options
-
-        Returns:
-            The fidelity default + estimator + run options.
-        """
-        opts = copy(self._sampler.options)
-        opts.update_options(**options)
-        return opts
+        self._shots = shots
 
     @staticmethod
     def _get_global_fidelity(probability_distribution: dict[int, float]) -> float:
diff --git a/qiskit_algorithms/state_fidelities/state_fidelity_result.py b/qiskit_algorithms/state_fidelities/state_fidelity_result.py
index 6dc26dbf..2d9807b3 100644
--- a/qiskit_algorithms/state_fidelities/state_fidelity_result.py
+++ b/qiskit_algorithms/state_fidelities/state_fidelity_result.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,10 +16,8 @@
 from __future__ import annotations
 
 from collections.abc import Sequence, Mapping
-from typing import Any
 from dataclasses import dataclass
-
-from qiskit.providers import Options
+from typing import Any
 
 
 @dataclass(frozen=True)
@@ -33,5 +31,5 @@ class StateFidelityResult:
     depending on the error mitigation method used."""
     metadata: Sequence[Mapping[str, Any]]
     """Additional information about the fidelity calculation."""
-    options: Options
-    """Primitive runtime options for the execution of the fidelity job."""
+    shots: int | Sequence[int]
+    """Primitive number of shots options for the execution of the fidelity job."""
diff --git a/qiskit_algorithms/time_evolvers/pvqd/pvqd.py b/qiskit_algorithms/time_evolvers/pvqd/pvqd.py
index c04d0bc3..44a461f1 100644
--- a/qiskit_algorithms/time_evolvers/pvqd/pvqd.py
+++ b/qiskit_algorithms/time_evolvers/pvqd/pvqd.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2019, 2024.
+# (C) Copyright IBM 2019, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -20,7 +20,7 @@
 
 from qiskit.circuit import Parameter, ParameterVector, QuantumCircuit
 from qiskit.circuit.library import PauliEvolutionGate
-from qiskit.primitives import BaseEstimator
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 from qiskit.synthesis import EvolutionSynthesis, LieTrotter
 from qiskit_algorithms.utils import algorithm_globals
@@ -74,14 +74,14 @@ class PVQD(RealTimeEvolver):
 
             from qiskit_algorithms.state_fidelities import ComputeUncompute
             from qiskit_algorithms.time_evolvers import TimeEvolutionProblem, PVQD
-            from qiskit.primitives import Estimator, Sampler
+            from qiskit.primitives import StatevectorEstimator, StatevectorSampler
             from qiskit.circuit.library import EfficientSU2
             from qiskit.quantum_info import SparsePauliOp, Pauli
             from qiskit_algorithms.optimizers import L_BFGS_B
 
-            sampler = Sampler()
+            sampler = StatevectorSampler()
             fidelity = ComputeUncompute(sampler)
-            estimator = Estimator()
+            estimator = StatevectorEstimator()
             hamiltonian = 0.1 * SparsePauliOp(["ZZ", "IX", "XI"])
             observable = Pauli("ZZ")
             ansatz = EfficientSU2(2, reps=1)
@@ -121,7 +121,7 @@ def __init__(
         fidelity: BaseStateFidelity,
         ansatz: QuantumCircuit,
         initial_parameters: np.ndarray,
-        estimator: BaseEstimator | None = None,
+        estimator: BaseEstimatorV2 | None = None,
         optimizer: Optimizer | Minimizer | None = None,
         num_timesteps: int | None = None,
         evolution: EvolutionSynthesis | None = None,
diff --git a/qiskit_algorithms/time_evolvers/pvqd/utils.py b/qiskit_algorithms/time_evolvers/pvqd/utils.py
index b571a792..b992ab9b 100644
--- a/qiskit_algorithms/time_evolvers/pvqd/utils.py
+++ b/qiskit_algorithms/time_evolvers/pvqd/utils.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,7 +21,7 @@
 from qiskit.circuit import QuantumCircuit, Parameter, ParameterExpression
 from qiskit.compiler import transpile
 from qiskit.exceptions import QiskitError
-from qiskit.primitives import BaseEstimator
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from qiskit_algorithms.gradients import ParamShiftSamplerGradient as ParamShift
@@ -75,7 +75,7 @@ def _is_gradient_supported(ansatz: QuantumCircuit) -> bool:
 def _get_observable_evaluator(
     ansatz: QuantumCircuit,
     observables: BaseOperator | list[BaseOperator],
-    estimator: BaseEstimator,
+    estimator: BaseEstimatorV2,
 ) -> Callable[[np.ndarray], float | list[float]]:
     """Get a callable to evaluate a (list of) observable(s) for given circuit parameters."""
 
@@ -91,18 +91,10 @@ def evaluate_observables(theta: np.ndarray) -> float | list[float]:
         Raises:
             AlgorithmError: If a primitive job fails.
         """
-        if isinstance(observables, list):
-            num_observables = len(observables)
-            obs = observables
-        else:
-            num_observables = 1
-            obs = [observables]
-        states = [ansatz] * num_observables
-        parameter_values = [theta] * num_observables
 
         try:
-            estimator_job = estimator.run(states, obs, parameter_values=parameter_values)
-            results = estimator_job.result().values
+            estimator_job = estimator.run([(ansatz, observables, theta)])
+            results = estimator_job.result()[0].data.evs
         except Exception as exc:
             raise AlgorithmError("The primitive job failed!") from exc
 
diff --git a/qiskit_algorithms/time_evolvers/trotterization/trotter_qrte.py b/qiskit_algorithms/time_evolvers/trotterization/trotter_qrte.py
index 893cd985..256c6864 100644
--- a/qiskit_algorithms/time_evolvers/trotterization/trotter_qrte.py
+++ b/qiskit_algorithms/time_evolvers/trotterization/trotter_qrte.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2024.
+# (C) Copyright IBM 2021, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -14,14 +14,17 @@
 
 from __future__ import annotations
 
+from typing import Any
+
 from qiskit import QuantumCircuit
 
 from qiskit.circuit.library import PauliEvolutionGate
 from qiskit.circuit.parametertable import ParameterView
-from qiskit.primitives import BaseEstimator
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.quantum_info import Pauli, SparsePauliOp
 from qiskit.synthesis import ProductFormula, LieTrotter
 
+from qiskit_algorithms.custom_types import Transpiler
 from qiskit_algorithms.time_evolvers.time_evolution_problem import TimeEvolutionProblem
 from qiskit_algorithms.time_evolvers.time_evolution_result import TimeEvolutionResult
 from qiskit_algorithms.time_evolvers.real_time_evolver import RealTimeEvolver
@@ -41,14 +44,14 @@ class TrotterQRTE(RealTimeEvolver):
             from qiskit.quantum_info import Pauli, SparsePauliOp
             from qiskit import QuantumCircuit
             from qiskit_algorithms import TrotterQRTE, TimeEvolutionProblem
-            from qiskit.primitives import Estimator
+            from qiskit.primitives import StatevectorEstimator
 
             operator = SparsePauliOp([Pauli("X"), Pauli("Z")])
             initial_state = QuantumCircuit(1)
             time = 1
             evolution_problem = TimeEvolutionProblem(operator, time, initial_state)
             # LieTrotter with 1 rep
-            estimator = Estimator()
+            estimator = StatevectorEstimator()
             trotter_qrte = TrotterQRTE(estimator=estimator)
             evolved_state = trotter_qrte.evolve(evolution_problem).evolved_state
     """
@@ -56,9 +59,11 @@ class TrotterQRTE(RealTimeEvolver):
     def __init__(
         self,
         product_formula: ProductFormula | None = None,
-        estimator: BaseEstimator | None = None,
+        estimator: BaseEstimatorV2 | None = None,
         num_timesteps: int = 1,
         *,
+        transpiler: Transpiler | None = None,
+        transpiler_options: dict[str, Any] | None = None,
         insert_barriers: bool = False,
     ) -> None:
         """
@@ -73,6 +78,11 @@ def __init__(
                 ``TimeEvolutionProblem.aux_operators``.
             num_timesteps: The number of time-steps the full evolution time is divided into
                 (repetitions of ``product_formula``).
+            transpiler: An optional object with a `run` method allowing to transpile the circuits
+                that are produced within this algorithm. If set to `None`, these won't be
+                transpiled.
+            transpiler_options: A dictionary of options to be passed to the transpiler's `run`
+                method as keyword arguments.
             insert_barriers: If True, insert a barrier after the initial state and after each Trotter
                 step.
         """
@@ -81,6 +91,8 @@ def __init__(
         self.num_timesteps = num_timesteps
         self.estimator = estimator
         self._insert_barriers = insert_barriers
+        self._transpiler = transpiler
+        self._transpiler_options = transpiler_options if transpiler_options is not None else {}
 
     @property
     def product_formula(self) -> ProductFormula:
@@ -96,14 +108,14 @@ def product_formula(self, product_formula: ProductFormula | None):
         self._product_formula = product_formula
 
     @property
-    def estimator(self) -> BaseEstimator | None:
+    def estimator(self) -> BaseEstimatorV2 | None:
         """
         Returns an estimator.
         """
         return self._estimator
 
     @estimator.setter
-    def estimator(self, estimator: BaseEstimator) -> None:
+    def estimator(self, estimator: BaseEstimatorV2) -> None:
         """
         Sets an estimator.
         """
@@ -197,6 +209,10 @@ def evolve(self, evolution_problem: TimeEvolutionProblem) -> TimeEvolutionResult
 
         evolved_state = QuantumCircuit(initial_state.num_qubits)
         evolved_state.append(initial_state, evolved_state.qubits)
+
+        if self._transpiler is not None:
+            evolved_state = self._transpiler.run(evolved_state, **self._transpiler_options)
+
         if self._insert_barriers:
             evolved_state.barrier()
 
@@ -235,6 +251,10 @@ def evolve(self, evolution_problem: TimeEvolutionProblem) -> TimeEvolutionResult
                     synthesis=self.product_formula,
                 )
             evolved_state.append(single_step_evolution_gate, evolved_state.qubits)
+
+            if self._transpiler is not None:
+                evolved_state = self._transpiler.run(evolved_state, **self._transpiler_options)
+
             if self._insert_barriers:
                 evolved_state.barrier()
 
diff --git a/qiskit_algorithms/time_evolvers/variational/solvers/ode/forward_euler_solver.py b/qiskit_algorithms/time_evolvers/variational/solvers/ode/forward_euler_solver.py
index 4dc2f922..3aa09ed9 100644
--- a/qiskit_algorithms/time_evolvers/variational/solvers/ode/forward_euler_solver.py
+++ b/qiskit_algorithms/time_evolvers/variational/solvers/ode/forward_euler_solver.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023, 2024.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -66,6 +66,8 @@ def _step_impl(self):
             self.y = list(np.add(self.y, self._step_length * self.fun(self.t, self.y)))
             self.t += self._step_length
             return True, None
+        # TODO: why do we catch an Exception here? It makes debugging errors quite obscure, as the
+        #  evolve method continues without updating the parameters without signaling the problem
         except Exception as ex:  # pylint: disable=broad-except
             return False, f"Unknown ODE solver error: {str(ex)}."
 
diff --git a/qiskit_algorithms/time_evolvers/variational/var_qite.py b/qiskit_algorithms/time_evolvers/variational/var_qite.py
index a6f9d388..fd6fb932 100644
--- a/qiskit_algorithms/time_evolvers/variational/var_qite.py
+++ b/qiskit_algorithms/time_evolvers/variational/var_qite.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023, 2024.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,7 +21,7 @@
 
 from qiskit import QuantumCircuit
 from qiskit.circuit import Parameter
-from qiskit.primitives import BaseEstimator
+from qiskit.primitives import BaseEstimatorV2
 
 from .solvers.ode.forward_euler_solver import ForwardEulerSolver
 
@@ -42,7 +42,7 @@ class VarQITE(VarQTE, ImaginaryTimeEvolver):
         from qiskit_algorithms.time_evolvers.variational import ImaginaryMcLachlanPrinciple
         from qiskit.circuit.library import EfficientSU2
         from qiskit.quantum_info import SparsePauliOp, Pauli
-        from qiskit.primitives import Estimator
+        from qiskit.primitives import StatevectorEstimator
 
         observable = SparsePauliOp.from_list(
             [
@@ -68,7 +68,7 @@ class VarQITE(VarQTE, ImaginaryTimeEvolver):
         # evaluating auxiliary operators
         aux_ops = [Pauli("XX"), Pauli("YZ")]
         evolution_problem = TimeEvolutionProblem(observable, time, aux_operators=aux_ops)
-        var_qite = VarQITE(ansatz, init_param_values, var_principle, Estimator())
+        var_qite = VarQITE(ansatz, init_param_values, var_principle, StatevectorEstimator())
         evolution_result = var_qite.evolve(evolution_problem)
     """
 
@@ -78,7 +78,7 @@ def __init__(
         ansatz: QuantumCircuit,
         initial_parameters: Mapping[Parameter, float] | Sequence[float],
         variational_principle: ImaginaryVariationalPrinciple | None = None,
-        estimator: BaseEstimator | None = None,
+        estimator: BaseEstimatorV2 | None = None,
         ode_solver: Type[OdeSolver] | str = ForwardEulerSolver,
         lse_solver: Callable[[np.ndarray, np.ndarray], np.ndarray] | None = None,
         num_timesteps: int | None = None,
diff --git a/qiskit_algorithms/time_evolvers/variational/var_qrte.py b/qiskit_algorithms/time_evolvers/variational/var_qrte.py
index 12cda9a2..d2621e85 100644
--- a/qiskit_algorithms/time_evolvers/variational/var_qrte.py
+++ b/qiskit_algorithms/time_evolvers/variational/var_qrte.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023, 2024.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,7 +21,7 @@
 
 from qiskit import QuantumCircuit
 from qiskit.circuit import Parameter
-from qiskit.primitives import BaseEstimator
+from qiskit.primitives import BaseEstimatorV2
 
 from .solvers.ode.forward_euler_solver import ForwardEulerSolver
 
@@ -43,7 +43,7 @@ class VarQRTE(VarQTE, RealTimeEvolver):
         from qiskit_algorithms.time_evolvers.variational import RealMcLachlanPrinciple
         from qiskit.quantum_info import SparsePauliOp
         from qiskit.quantum_info import SparsePauliOp, Pauli
-        from qiskit.primitives import Estimator
+        from qiskit.primitives import StatevectorEstimator
 
         observable = SparsePauliOp.from_list(
             [
@@ -69,7 +69,7 @@ class VarQRTE(VarQTE, RealTimeEvolver):
         # evaluating auxiliary operators
         aux_ops = [Pauli("XX"), Pauli("YZ")]
         evolution_problem = TimeEvolutionProblem(observable, time, aux_operators=aux_ops)
-        var_qrte = VarQRTE(ansatz, init_param_values, var_principle, Estimator())
+        var_qrte = VarQRTE(ansatz, init_param_values, var_principle, StatevectorEstimator())
         evolution_result = var_qrte.evolve(evolution_problem)
     """
 
@@ -79,7 +79,7 @@ def __init__(
         ansatz: QuantumCircuit,
         initial_parameters: Mapping[Parameter, float] | Sequence[float],
         variational_principle: RealVariationalPrinciple | None = None,
-        estimator: BaseEstimator | None = None,
+        estimator: BaseEstimatorV2 | None = None,
         ode_solver: Type[OdeSolver] | str = ForwardEulerSolver,
         lse_solver: Callable[[np.ndarray, np.ndarray], np.ndarray] | None = None,
         num_timesteps: int | None = None,
diff --git a/qiskit_algorithms/time_evolvers/variational/var_qte.py b/qiskit_algorithms/time_evolvers/variational/var_qte.py
index bc1b2e36..88939233 100644
--- a/qiskit_algorithms/time_evolvers/variational/var_qte.py
+++ b/qiskit_algorithms/time_evolvers/variational/var_qte.py
@@ -22,7 +22,7 @@
 
 from qiskit import QuantumCircuit
 from qiskit.circuit import Parameter
-from qiskit.primitives import BaseEstimator
+from qiskit.primitives import BaseEstimatorV2
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from .solvers.ode.forward_euler_solver import ForwardEulerSolver
@@ -77,7 +77,7 @@ def __init__(
         ansatz: QuantumCircuit,
         initial_parameters: Mapping[Parameter, float] | Sequence[float],
         variational_principle: VariationalPrinciple,
-        estimator: BaseEstimator,
+        estimator: BaseEstimatorV2,
         ode_solver: Type[OdeSolver] | str = ForwardEulerSolver,
         lse_solver: Callable[[np.ndarray, np.ndarray], np.ndarray] | None = None,
         num_timesteps: int | None = None,
diff --git a/qiskit_algorithms/time_evolvers/variational/variational_principles/imaginary_mc_lachlan_principle.py b/qiskit_algorithms/time_evolvers/variational/variational_principles/imaginary_mc_lachlan_principle.py
index bfa29efb..d36e175f 100644
--- a/qiskit_algorithms/time_evolvers/variational/variational_principles/imaginary_mc_lachlan_principle.py
+++ b/qiskit_algorithms/time_evolvers/variational/variational_principles/imaginary_mc_lachlan_principle.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,7 +21,7 @@
 
 from qiskit import QuantumCircuit
 from qiskit.circuit import Parameter
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
 from .imaginary_variational_principle import ImaginaryVariationalPrinciple
@@ -69,7 +69,7 @@ def __init__(
                     "The provided gradient instance does not contain an estimator primitive."
                 ) from exc
         else:
-            estimator = Estimator()
+            estimator = StatevectorEstimator()
             gradient = LinCombEstimatorGradient(estimator)
 
         if qgt is None:
diff --git a/qiskit_algorithms/time_evolvers/variational/variational_principles/real_mc_lachlan_principle.py b/qiskit_algorithms/time_evolvers/variational/variational_principles/real_mc_lachlan_principle.py
index 822e6cc9..aa7a83ea 100644
--- a/qiskit_algorithms/time_evolvers/variational/variational_principles/real_mc_lachlan_principle.py
+++ b/qiskit_algorithms/time_evolvers/variational/variational_principles/real_mc_lachlan_principle.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -22,7 +22,7 @@
 
 from qiskit import QuantumCircuit
 from qiskit.circuit import Parameter
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 from qiskit.quantum_info import SparsePauliOp
 from qiskit.quantum_info.operators.base_operator import BaseOperator
 
@@ -70,7 +70,7 @@ def __init__(
                     "The provided gradient instance does not contain an estimator primitive."
                 ) from exc
         else:
-            estimator = Estimator()
+            estimator = StatevectorEstimator()
             gradient = LinCombEstimatorGradient(estimator, derivative_type=DerivativeType.IMAG)
 
         if qgt is None:
@@ -103,8 +103,8 @@ def evolution_gradient(
         """
 
         try:
-            estimator_job = self.gradient._estimator.run([ansatz], [hamiltonian], [param_values])
-            energy = estimator_job.result().values[0]
+            estimator_job = self.gradient._estimator.run([(ansatz, hamiltonian, param_values)])
+            energy = estimator_job.result()[0].data.evs
         except Exception as exc:
             raise AlgorithmError("The primitive job failed!") from exc
 
diff --git a/qiskit_algorithms/utils/circuit_key.py b/qiskit_algorithms/utils/circuit_key.py
new file mode 100644
index 00000000..3b0345c4
--- /dev/null
+++ b/qiskit_algorithms/utils/circuit_key.py
@@ -0,0 +1,78 @@
+# This code is part of a Qiskit project.
+#
+# (C) Copyright IBM 2025.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+"""
+_circuit_key function from Qiskit 1.4
+
+This file is to be deleted once all interfaces such as the BaseStateFidelity's and gradients' accept
+PUB-like inputs instead of separate arguments.
+"""
+from typing import Iterable
+
+import numpy as np
+from qiskit import QuantumCircuit
+from qiskit.circuit import Bit
+
+
+def _bits_key(bits: tuple[Bit, ...], circuit: QuantumCircuit) -> tuple:
+    return tuple(
+        (
+            circuit.find_bit(bit).index,
+            tuple((reg[0].size, reg[0].name, reg[1]) for reg in circuit.find_bit(bit).registers),
+        )
+        for bit in bits
+    )
+
+
+def _format_params(param):
+    if isinstance(param, np.ndarray):
+        return param.data.tobytes()
+    elif isinstance(param, QuantumCircuit):
+        return _circuit_key(param)
+    elif isinstance(param, Iterable):
+        return tuple(param)
+    return param
+
+
+def _circuit_key(circuit: QuantumCircuit, functional: bool = True) -> tuple:
+    """Private key function for QuantumCircuit.
+
+    This is the workaround until :meth:`QuantumCircuit.__hash__` will be introduced.
+    If key collision is found, please add elements to avoid it.
+
+    Args:
+        circuit: Input quantum circuit.
+        functional: If True, the returned key only includes functional data (i.e. execution related).
+
+    Returns:
+        Composite key for circuit.
+    """
+    functional_key: tuple = (
+        circuit.num_qubits,
+        circuit.num_clbits,
+        circuit.num_parameters,
+        tuple(  # circuit.data
+            (
+                _bits_key(data.qubits, circuit),  # qubits
+                _bits_key(data.clbits, circuit),  # classical bits
+                data.operation.name,  # operation.name
+                tuple(_format_params(param) for param in data.operation.params),  # operation.params
+            )
+            for data in circuit.data
+        ),
+        None if not hasattr(circuit, "op_start_times") else tuple(circuit.op_start_times),
+    )
+    if functional:
+        return functional_key
+    return (
+        circuit.name,
+        *functional_key,
+    )
diff --git a/qiskit_algorithms/utils/optionals.py b/qiskit_algorithms/utils/optionals.py
index 472157be..b0af7341 100644
--- a/qiskit_algorithms/utils/optionals.py
+++ b/qiskit_algorithms/utils/optionals.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -15,7 +15,7 @@
 """
 
 from qiskit.utils import LazyImportTester
-
+from qiskit.version import get_version_info
 
 HAS_NLOPT = LazyImportTester("nlopt", name="NLopt Optimizer", install="pip install nlopt")
 HAS_SKQUANT = LazyImportTester(
@@ -24,4 +24,7 @@
     install="pip install scikit-quant",
 )
 HAS_SQSNOBFIT = LazyImportTester("SQSnobFit", install="pip install SQSnobFit")
-HAS_TWEEDLEDUM = LazyImportTester("tweedledum", install="pip install tweedledum")
+# From Qiskit 2.0.0 onward, tweedledum isn't required anymore to use the phase oracle
+CAN_USE_PHASE_ORACLE = get_version_info() >= "2.0.0" or LazyImportTester(
+    "tweedledum", install="pip install tweedledum"
+)
diff --git a/releasenotes/notes/drop_python_3_8-dffbed172a8cccf1.yaml b/releasenotes/notes/drop_python_3_8-dffbed172a8cccf1.yaml
index 804f41de..7216bd0e 100644
--- a/releasenotes/notes/drop_python_3_8-dffbed172a8cccf1.yaml
+++ b/releasenotes/notes/drop_python_3_8-dffbed172a8cccf1.yaml
@@ -1,13 +1,6 @@
 ---
 prelude: >
     Support for Python 3.8 has been dropped.
-issues:
-  - |
-    Qiskit Algorithms does not work with Qiskit version 2.0 (or above).
-    Qiskit Algorithms is using Version 1 of the primitives and as they
-    were deprecated and then removed in Qiskit 2.0 it needs an earlier
-    version that still has them to work. As such Qiskit Algorithms pins
-    the qiskit dependency so that a version less than 2.0 will be installed.
 upgrade:
   - |
     Python 3.8 support was dropped as Python 3.8 reached End Of Life
diff --git a/releasenotes/notes/replaced-v1-primitives-by-v2-ones-897d9610351bd54b.yaml b/releasenotes/notes/replaced-v1-primitives-by-v2-ones-897d9610351bd54b.yaml
new file mode 100644
index 00000000..87286b17
--- /dev/null
+++ b/releasenotes/notes/replaced-v1-primitives-by-v2-ones-897d9610351bd54b.yaml
@@ -0,0 +1,132 @@
+---
+prelude: >
+    Following up on the deprecation of V1 primitives in Qiskit 1.X and their
+    subsequent removal in Qiskit 2.X, this release of ``qiskit-algorithms``
+    drops their support in favor of the V2 primitives. You can read about
+    how to modify your imports in your code in the
+    [Qiskit migration guide to the V2 primitives](https://quantum.cloud.ibm.com/docs/en/migration-guides/v2-primitives).
+    Since V2 primitives may require users to transpile their circuit, it is
+    now possible for the users to provide the classes that define their own
+    :class:`~qiskit.circuit.QuantumCircuit` with a ``Transpiler``
+    along with some options. A ``Transpiler`` is any object having a ``run``
+    method that can take as input a
+    :class:`~qiskit.circuit.QuantumCircuit` or a list thereof and
+    additional options and returns the transpiled version of its input(s)
+    according to the provided options.
+features:
+  - |
+    Some classes, notably those that create their own
+    :class:`~qiskit.circuit.QuantumCircuit`, now support being passed a
+    ``Transpiler`` along with some options. A ``Transpiler`` is any object
+    having a `run` method that can take as input
+    a :class:`~qiskit.circuit.QuantumCircuit` or a list thereof and additional options and returns
+    the transpiled version of its input(s) according to the provided options.
+    Passing a ``Transpiler`` to such a class is done via the `transpiler`
+    keyword-only argument at instantiation, and additional options passed to
+    its `run` method can be passed using the `transpiler_options` keyword-only
+    argument at instantiation. The classes that support this feature are
+      - :class:`.Grover`
+      - :class:`.AmplitudeEstimation`
+      - :class:`.FasterAmplitudeEstimation`
+      - :class:`.IterativeAmplitudeEstimation`
+      - :class:`.MaximumLikelihoodAmplitudeEstimation`
+      - :class:`.LinCombEstimatorGradient`
+      - :class:`.LinCombQGT`
+      - :class:`.LinCombSamplerGradient`
+      - :class:`.QAOA`
+      - :class:`.IterativePhaseEstimation`
+      - :class:`.PhaseEstimation`
+      - :class:`.ComputeUncompute`
+      - :class:`.TrotterQRTE`
+    Additionally, the following classes also support this feature. Contrarily
+    to those above, these classes don't create their own
+    :class:`qiskit.circuit.QuantumCircuit` but is instead provided by the user.
+    As such, transpiling the circuits prior to passing them to theses classes
+    has the same effect as providing the class with a `Transpiler`. These classes are
+      - :class:`.FiniteDiffEstimatorGradient`
+      - :class:`.FiniteDiffSamplerGradient`
+      - :class:`.ParamShiftEstimatorGradient`
+      - :class:`.ParamShiftSamplerGradient`
+      - :class:`.SPSAEstimatorGradient`
+      - :class:`.SPSASamplerGradient`
+      - :class:`.QFI`
+      - :class:`.VQD`
+      - :class:`.VQE`
+issues:
+  - |
+    Rare bugs might occur when using ``qiskit-algorithms`` in version ``0.4``
+    in conjunction with ``qiskit`` in version ``2.1.0``. If you encounter some
+    difficulties using this version of ``qiskit``, try to downgrade to ``2.0``.
+  - |
+    Passing to :class:`.VQE` or :class:`.VQD` a transpiler along with an ansatz
+    with a number of qubits that hasn't been set results in an error. Setting
+    explicitly the number of qubits fixes it.
+upgrade:
+  - |
+    The oldest supported version of Qiskit is now ``1.0``.
+  - |
+    The V1 primitives such as ``Sampler`` and ``Estimator`` from ``qiskit`` or
+    ``SamplerV1`` and ``EstimatorV1`` from ``qiskit-ibm-runtime`` are no longer
+    supported.
+  - |
+    Upgrading to ``qiskit-algorithms`` version ``0.4`` can be done in the
+    following manner:
+
+      1. Replace all usage of V1 primitives by their V2 counterpart.
+      2. If needed, provide the class you want to use with a ``Transpiler`` set
+         up for the backend you wish to use. Note that if you use the
+         Statevector simulation primitives from Qiskit you do not need to use
+         this feature. You do need to use it if you intend to run on an actual
+         quantum computer.
+
+    For instance, the following code for instantiating the :class:`.Grover`
+    class in the ``0.3`` version::
+
+        grover = Grover(
+            sampler=Sampler()
+        )
+
+    would be translated to, using a custom :class:`~qiskit.providers.fake_provider.GenericBackendV2` to
+    showcase the transpilation options::
+
+        from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
+        from qiskit.providers.fake_provider import GenericBackendV2
+
+        coupling_map = [(0, 1), (1, 2)]
+        backend = GenericBackendV2(num_qubits=3, coupling_map=coupling_map)
+
+        pm = generate_preset_pass_manager(optimization_level=2, backend=backend)
+
+        def callback(**kwargs):
+            if kwargs["count"] == 0:
+                print(f"Callback function has been called!")
+
+        grover = Grover(
+            sampler=StatevectorSampler(),
+            transpiler=pm,
+            transpiler_options={"callback": callback}
+        )
+
+    Note that the ``transpiler`` and ``transpiler_options`` arguments are
+    keyword-only, you must use the above syntax to use them. All options in the
+    ``transpiler_options`` dictionary will be passed to the ``run`` method of the
+    ``transpiler``. Similarly, if creating a :class:`.VQE` instance could be
+    done like so in the ``0.3`` version::
+
+        vqe = VQE(Estimator(), ansatz, optimizer)
+
+    You will now have to write the following in the ``0.4`` version::
+
+        vqe = VQE(
+            StatevectorEstimator(),
+            ansatz,
+            optimizer,
+            transpiler=pm,
+            transpiler_options={"callback": callback}
+        )
+
+    Note that since :class:`.VQE` doesn't create a
+    :class:`~qiskit.circuit.QuantumCircuit` internally but instead uses the
+    ansatz provided by the user, transpiling the ansatz before passing it to
+    :class:`.VQE` allows not to provide :class:`.VQE` with a ``Transpiler``.
+    If one is provided, the ansatz will be transpiled using it regardless.
diff --git a/requirements-dev.txt b/requirements-dev.txt
index de2bf625..bb1ed487 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -19,9 +19,12 @@ qiskit-aer>=0.12
 networkx>=2.2
 mypy>=0.991
 mypy-extensions>=0.4.3
+qiskit!=2.1.*
 
 # Tweedledum is unmaintained and its existing Mac wheels are unreliable. If you
 # manage to get a working install on a Mac the functionality should still work,
 # but as a convenience this file won't attempt the install itself.
+# Furthermore, tweedledum won't be required anymore when the oldest supported version
+# of Qiskit is 2.0.
 tweedledum; python_version<'3.11' and platform_system!="Darwin"
 
diff --git a/requirements.txt b/requirements.txt
index 38521bdd..b63bd765 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,3 @@
-qiskit>=0.44,<2.0
+qiskit>=1.0
 scipy>=1.4
 numpy>=1.17
diff --git a/test/eigensolvers/test_vqd.py b/test/eigensolvers/test_vqd.py
index a4d2fbe7..33f8fac0 100644
--- a/test/eigensolvers/test_vqd.py
+++ b/test/eigensolvers/test_vqd.py
@@ -13,24 +13,25 @@
 """Test VQD"""
 
 import unittest
+
+from qiskit.providers.fake_provider import GenericBackendV2
+
 from test import QiskitAlgorithmsTestCase
 
 import numpy as np
-import scipy
-from ddt import data, ddt
-from packaging.version import Version
-
-from qiskit import QuantumCircuit
+from ddt import data, ddt, idata, unpack
+from qiskit import QuantumCircuit, generate_preset_pass_manager
 from qiskit.circuit.library import TwoLocal, RealAmplitudes
-from qiskit.primitives import Sampler, Estimator
+from qiskit.primitives import StatevectorSampler, StatevectorEstimator
 from qiskit.quantum_info import SparsePauliOp
 
-from qiskit_algorithms.eigensolvers import VQD, VQDResult
 from qiskit_algorithms import AlgorithmError
+from qiskit_algorithms.eigensolvers import VQD, VQDResult
 from qiskit_algorithms.optimizers import COBYLA, L_BFGS_B, SLSQP, SPSA
 from qiskit_algorithms.state_fidelities import ComputeUncompute
 from qiskit_algorithms.utils import algorithm_globals
 
+
 H2_SPARSE_PAULI = SparsePauliOp.from_list(
     [
         ("II", -1.052373245772859),
@@ -41,6 +42,8 @@
     ]
 )
 
+THREE_QUBITS_BACKEND = GenericBackendV2(num_qubits=3, coupling_map=[[0, 1], [1, 2]], seed=54)
+
 
 @ddt
 class TestVQD(QiskitAlgorithmsTestCase):
@@ -59,9 +62,8 @@ def setUp(self):
         )
         self.ry_wavefunction = TwoLocal(rotation_blocks="ry", entanglement_blocks="cz")
 
-        self.estimator = Estimator()
-        self.estimator_shots = Estimator(options={"shots": 1024, "seed": self.seed})
-        self.fidelity = ComputeUncompute(Sampler(options={"shots": 100_000, "seed": self.seed}))
+        self.estimator = StatevectorEstimator(seed=self.seed)
+        self.fidelity = ComputeUncompute(StatevectorSampler(seed=self.seed, default_shots=10_000))
         self.betas = [3]
 
     @data(H2_SPARSE_PAULI)
@@ -94,11 +96,16 @@ def test_basic_operator(self, op):
 
         with self.subTest(msg="assert return ansatz is set"):
             job = self.estimator.run(
-                result.optimal_circuits,
-                [op] * len(result.optimal_points),
-                result.optimal_points,
+                [
+                    (circuits, op, optimal_points)
+                    for (circuits, optimal_points) in zip(
+                        result.optimal_circuits, result.optimal_points
+                    )
+                ]
             )
-            np.testing.assert_array_almost_equal(job.result().values, result.eigenvalues, 6)
+            job_result = job.result()
+            eigenvalues = np.array([job_result[i].data.evs for i in range(len(result.eigenvalues))])
+            np.testing.assert_array_almost_equal(eigenvalues, result.eigenvalues, 6)
 
         with self.subTest(msg="assert returned values are eigenvalues"):
             np.testing.assert_array_almost_equal(
@@ -125,9 +132,7 @@ def test_beta_autoeval(self, op):
         """Test beta auto-evaluation for different operator types."""
 
         with self.assertLogs(level="INFO") as logs:
-            vqd = VQD(
-                self.estimator_shots, self.fidelity, self.ryrz_wavefunction, optimizer=L_BFGS_B()
-            )
+            vqd = VQD(self.estimator, self.fidelity, self.ryrz_wavefunction, optimizer=L_BFGS_B())
             _ = vqd.compute_eigenvalues(op)
 
         # the first log message shows the value of beta[0]
@@ -177,11 +182,11 @@ def store_intermediate_result(eval_count, parameters, mean, metadata, step):
             history["metadata"].append(metadata)
             history["step"].append(step)
 
-        optimizer = COBYLA(maxiter=12)
+        optimizer = COBYLA(maxiter=10)
         wavefunction = self.ry_wavefunction
 
         vqd = VQD(
-            estimator=self.estimator_shots,
+            estimator=self.estimator,
             fidelity=self.fidelity,
             ansatz=wavefunction,
             optimizer=optimizer,
@@ -210,8 +215,6 @@ def store_intermediate_result(eval_count, parameters, mean, metadata, step):
             8,
             9,
             10,
-            11,
-            12,
             1,
             2,
             3,
@@ -222,75 +225,32 @@ def store_intermediate_result(eval_count, parameters, mean, metadata, step):
             8,
             9,
             10,
-            11,
-            12,
         ]
 
-        ref_mean_pre_1_16 = [
+        ref_mean = [
             -1.08,
             -1.08,
-            -1.0,
+            -1.01,
             -1.14,
             -1.17,
             -1.38,
-            -1.0,
+            -1.01,
             -1.63,
-            -1.45,
-            -1.55,
-            -1.63,
-            -1.75,
-            -1.04,
-            -1.07,
-            -0.72,
-            -0.46,
+            -1.46,
+            -1.56,
+            -0.99,
+            -1.03,
             -0.71,
-            -0.56,
-            -0.92,
-            -0.29,
-            -0.89,
-            -0.38,
-            -1.06,
-            -1.05,
+            -0.17,
+            -0.36,
+            -0.47,
+            -0.95,
+            -0.15,
+            -0.86,
+            -0.55,
         ]
-        ref_mean_1_16 = [
-            -1.08,
-            -1.08,
-            -1.0,
-            -1.14,
-            -1.17,
-            -1.38,
-            -1.0,
-            -1.63,
-            -1.45,
-            -1.55,
-            -1.63,
-            -1.75,
-            -1.04,
-            -1.07,
-            -0.72,
-            -0.46,
-            -0.71,
-            -0.56,
-            -0.92,
-            -0.29,
-            -0.89,
-            -0.38,
-            -0.97,
-            -1.16,
-        ]
-        # Unlike in other places where COYBLA is used in tests and differences arose between
-        # pre 1.16.0 versions and after, where in 1.16.0 scipy changed the COBYLA
-        # implementation, I was not able to find changes that would reproduce the outcome so
-        # tests passed no matter whether 1.16 or before was installed. Here the mean outcomes
-        # match all but the last 2 values so I thought about comparing a subset but in the
-        # end decided to go with different reference values based on scipy version
-        ref_mean = (
-            ref_mean_pre_1_16
-            if Version(scipy.version.version) < Version("1.16.0")
-            else ref_mean_1_16
-        )
 
-        ref_step = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
+        ref_step = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
 
         np.testing.assert_array_almost_equal(history["eval_count"], ref_eval_count, decimal=0)
         np.testing.assert_array_almost_equal(history["mean"], ref_mean, decimal=2)
@@ -372,13 +332,29 @@ def test_optimizer_list(self, op):
 
         result = vqd.compute_eigenvalues(operator=op)
         np.testing.assert_array_almost_equal(
-            result.eigenvalues.real, self.h2_energy_excited[:2], decimal=3
+            result.eigenvalues.real, self.h2_energy_excited[:2], decimal=2
         )
 
-    @data(H2_SPARSE_PAULI)
-    def test_aux_operators_list(self, op):
+    # Since we perform actions on the aux_operators when a transpiler is set, we have to check that it
+    # doesn't affect the final result
+    @idata(
+        [
+            [H2_SPARSE_PAULI, None],
+            [
+                H2_SPARSE_PAULI,
+                generate_preset_pass_manager(
+                    backend=THREE_QUBITS_BACKEND,
+                    optimization_level=1,
+                    seed_transpiler=42
+                )
+            ],
+        ]
+    )
+    @unpack
+    def test_aux_operators_list(self, op, transpiler):
         """Test list-based aux_operators."""
         wavefunction = self.ry_wavefunction
+        wavefunction.num_qubits = 2
         vqd = VQD(
             estimator=self.estimator,
             fidelity=self.fidelity,
@@ -386,6 +362,7 @@ def test_aux_operators_list(self, op):
             optimizer=COBYLA(),
             k=2,
             betas=self.betas,
+            transpiler=transpiler,
         )
 
         # Start with an empty list
@@ -428,16 +405,33 @@ def test_aux_operators_list(self, op):
         self.assertIsInstance(result.aux_operators_evaluated[0][1][1], dict)
         self.assertIsInstance(result.aux_operators_evaluated[0][3][1], dict)
 
-    @data(H2_SPARSE_PAULI)
-    def test_aux_operators_dict(self, op):
+    # Since we perform actions on the aux_operators when a transpiler is set, we have to check that it
+    # doesn't affect the final result
+    @idata(
+        [
+            [H2_SPARSE_PAULI, None],
+            [
+                H2_SPARSE_PAULI,
+                generate_preset_pass_manager(
+                    backend=THREE_QUBITS_BACKEND,
+                    optimization_level=1,
+                    seed_transpiler=42
+                )
+            ],
+        ]
+    )
+    @unpack
+    def test_aux_operators_dict(self, op, transpiler):
         """Test dictionary compatibility of aux_operators"""
         wavefunction = self.ry_wavefunction
+        wavefunction.num_qubits = 2
         vqd = VQD(
             estimator=self.estimator,
             fidelity=self.fidelity,
             ansatz=wavefunction,
             optimizer=COBYLA(),
             betas=self.betas,
+            transpiler=transpiler
         )
 
         # Start with an empty dictionary
@@ -482,10 +476,26 @@ def test_aux_operators_dict(self, op):
         self.assertIsInstance(result.aux_operators_evaluated[0]["aux_op2"][1], dict)
         self.assertIsInstance(result.aux_operators_evaluated[0]["zero_operator"][1], dict)
 
-    @data(H2_SPARSE_PAULI)
-    def test_aux_operator_std_dev(self, op):
+    # Since we perform actions on the aux_operators when a transpiler is set, we have to check that it
+    # doesn't affect the final result
+    @idata(
+        [
+            [H2_SPARSE_PAULI, None],
+            [
+                H2_SPARSE_PAULI,
+                generate_preset_pass_manager(
+                    backend=THREE_QUBITS_BACKEND,
+                    optimization_level=1,
+                    seed_transpiler=42
+                )
+            ],
+        ]
+    )
+    @unpack
+    def test_aux_operator_std_dev(self, op, transpiler):
         """Test non-zero standard deviations of aux operators."""
         wavefunction = self.ry_wavefunction
+        wavefunction.num_qubits = 2
         vqd = VQD(
             estimator=self.estimator,
             fidelity=self.fidelity,
@@ -502,6 +512,7 @@ def test_aux_operator_std_dev(self, op):
             ],
             optimizer=COBYLA(maxiter=10),
             betas=self.betas,
+            transpiler=transpiler
         )
 
         # Go again with two auxiliary operators
@@ -543,6 +554,18 @@ def test_convergence_threshold(self):
             SLSQP(),
             k=2,
             betas=self.betas,
+            initial_point=np.array(
+                [
+                    2.15707009,
+                    -2.6128808,
+                    1.40478697,
+                    -1.73909435,
+                    -2.89100903,
+                    1.75289926,
+                    -0.14760479,
+                    -2.00011645,
+                ]
+            ),
             convergence_threshold=1e-3,
         )
         with self.subTest("Failed convergence"):
@@ -553,9 +576,38 @@ def test_convergence_threshold(self):
             vqd.convergence_threshold = 1e-1
             result = vqd.compute_eigenvalues(operator=H2_SPARSE_PAULI)
             np.testing.assert_array_almost_equal(
-                result.eigenvalues.real, self.h2_energy_excited[:2], decimal=1
+                result.eigenvalues.real, self.h2_energy_excited[:2], decimal=2
             )
 
+    @data(None, THREE_QUBITS_BACKEND)
+    def test_transpiler(self, backend):
+        """Test that the transpiler is called"""
+        pass_manager = generate_preset_pass_manager(
+            backend=backend,
+            optimization_level=1,
+            seed_transpiler=42
+        )
+        counts = [0]
+
+        def callback(**kwargs):
+            counts[0] = kwargs["count"]
+
+        wavefunction = self.ryrz_wavefunction
+        wavefunction.num_qubits = 2
+        vqd = VQD(
+            estimator=self.estimator,
+            fidelity=self.fidelity,
+            ansatz=wavefunction,
+            optimizer=COBYLA(),
+            betas=self.betas,
+            transpiler=pass_manager,
+            transpiler_options={"callback": callback},
+        )
+
+        vqd.compute_eigenvalues(operator=H2_SPARSE_PAULI)
+
+        self.assertGreater(counts[0], 0)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/test/gradients/logging_primitives.py b/test/gradients/logging_primitives.py
index e9a9f21c..442c45fa 100644
--- a/test/gradients/logging_primitives.py
+++ b/test/gradients/logging_primitives.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -11,32 +11,38 @@
 # that they have been altered from the originals.
 
 """Test primitives that check what kind of operations are in the circuits they execute."""
+from __future__ import annotations
+from typing import Iterable
 
-from qiskit.primitives import Estimator, Sampler
+from qiskit.primitives import StatevectorEstimator, StatevectorSampler
+from qiskit.primitives.containers.estimator_pub import EstimatorPub
+from qiskit.primitives.containers.sampler_pub import SamplerPub
 
 
-class LoggingEstimator(Estimator):
+class LoggingEstimator(StatevectorEstimator):
     """An estimator checking what operations were in the circuits it executed."""
 
-    def __init__(self, options=None, operations_callback=None):
-        super().__init__(options=options)
+    def __init__(
+        self, default_precision: float = 0.0, seed: int | None = None, operations_callback=None
+    ):
+        super().__init__(default_precision=default_precision, seed=seed)
         self.operations_callback = operations_callback
 
-    def _run(self, circuits, observables, parameter_values, **run_options):
+    def _run(self, pubs: list[EstimatorPub]):
         if self.operations_callback is not None:
-            ops = [circuit.count_ops() for circuit in circuits]
+            ops = [pub.circuit.count_ops() for pub in pubs]
             self.operations_callback(ops)
-        return super()._run(circuits, observables, parameter_values, **run_options)
+        return super()._run(pubs)
 
 
-class LoggingSampler(Sampler):
+class LoggingSampler(StatevectorSampler):
     """A sampler checking what operations were in the circuits it executed."""
 
-    def __init__(self, operations_callback):
-        super().__init__()
+    def __init__(self, shots: int = 1024, seed: int | None = None, operations_callback=None):
+        super().__init__(default_shots=shots, seed=seed)
         self.operations_callback = operations_callback
 
-    def _run(self, circuits, parameter_values, **run_options):
-        ops = [circuit.count_ops() for circuit in circuits]
+    def _run(self, pubs: Iterable[SamplerPub]):
+        ops = [pub.circuit.count_ops() for pub in pubs]
         self.operations_callback(ops)
-        return super()._run(circuits, parameter_values, **run_options)
+        return super()._run(pubs)
diff --git a/test/gradients/test_estimator_gradient.py b/test/gradients/test_estimator_gradient.py
index 7719b650..7be74339 100644
--- a/test/gradients/test_estimator_gradient.py
+++ b/test/gradients/test_estimator_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2019, 2024.
+# (C) Copyright IBM 2019, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -23,9 +23,10 @@
 from qiskit.circuit import Parameter
 from qiskit.circuit.library import EfficientSU2, RealAmplitudes
 from qiskit.circuit.library.standard_gates import RXXGate, RYYGate, RZXGate, RZZGate
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 from qiskit.quantum_info import SparsePauliOp, Pauli
 from qiskit.quantum_info.random import random_pauli_list
+from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
 
 from qiskit_algorithms.gradients import (
     FiniteDiffEstimatorGradient,
@@ -55,7 +56,7 @@ class TestEstimatorGradient(QiskitAlgorithmsTestCase):
     @data(*gradient_factories)
     def test_gradient_operators(self, grad):
         """Test the estimator gradient for different operators"""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         a = Parameter("a")
         qc = QuantumCircuit(1)
         qc.h(0)
@@ -74,7 +75,7 @@ def test_gradient_operators(self, grad):
     @data(*gradient_factories)
     def test_single_circuit_observable(self, grad):
         """Test the estimator gradient for a single circuit and observable"""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         a = Parameter("a")
         qc = QuantumCircuit(1)
         qc.h(0)
@@ -90,7 +91,7 @@ def test_single_circuit_observable(self, grad):
     @data(*gradient_factories)
     def test_gradient_p(self, grad):
         """Test the estimator gradient for p"""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         a = Parameter("a")
         qc = QuantumCircuit(1)
         qc.h(0)
@@ -108,7 +109,7 @@ def test_gradient_p(self, grad):
     @data(*gradient_factories)
     def test_gradient_u(self, grad):
         """Test the estimator gradient for u"""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         a = Parameter("a")
         b = Parameter("b")
         c = Parameter("c")
@@ -129,7 +130,7 @@ def test_gradient_u(self, grad):
     @data(*gradient_factories)
     def test_gradient_efficient_su2(self, grad):
         """Test the estimator gradient for EfficientSU2"""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         qc = EfficientSU2(2, reps=1)
         op = SparsePauliOp.from_list([("ZI", 1)])
         gradient = grad(estimator)
@@ -157,7 +158,7 @@ def test_gradient_efficient_su2(self, grad):
     @data(*gradient_factories)
     def test_gradient_2qubit_gate(self, grad):
         """Test the estimator gradient for 2 qubit gates"""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         for gate in [RXXGate, RYYGate, RZZGate, RZXGate]:
             param_list = [[np.pi / 4], [np.pi / 2]]
             correct_results = [
@@ -182,7 +183,7 @@ def test_gradient_2qubit_gate(self, grad):
     @data(*gradient_factories)
     def test_gradient_parameter_coefficient(self, grad):
         """Test the estimator gradient for parameter variables with coefficients"""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         qc = RealAmplitudes(num_qubits=2, reps=1)
         qc.rz(qc.parameters[0].exp() + 2 * qc.parameters[1], 0)
         qc.rx(3.0 * qc.parameters[0] + qc.parameters[1].sin(), 1)
@@ -203,7 +204,7 @@ def test_gradient_parameter_coefficient(self, grad):
     @data(*gradient_factories)
     def test_gradient_parameters(self, grad):
         """Test the estimator gradient for parameters"""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         a = Parameter("a")
         b = Parameter("b")
         qc = QuantumCircuit(1)
@@ -246,7 +247,7 @@ def test_gradient_parameters(self, grad):
     @data(*gradient_factories)
     def test_gradient_multi_arguments(self, grad):
         """Test the estimator gradient for multiple arguments"""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         a = Parameter("a")
         b = Parameter("b")
         qc = QuantumCircuit(1)
@@ -285,7 +286,7 @@ def test_gradient_multi_arguments(self, grad):
     @data(*gradient_factories)
     def test_gradient_validation(self, grad):
         """Test estimator gradient's validation"""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         a = Parameter("a")
         qc = QuantumCircuit(1)
         qc.rx(a, 0)
@@ -303,7 +304,7 @@ def test_gradient_validation(self, grad):
 
     def test_spsa_gradient(self):
         """Test the SPSA estimator gradient"""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         with self.assertRaises(ValueError):
             _ = SPSAEstimatorGradient(estimator, epsilon=-0.1)
         a = Parameter("a")
@@ -391,7 +392,7 @@ def test_gradient_random_parameters(self, grad):
         op = SparsePauliOp(random_pauli_list(num_qubits=qc.num_qubits, size=size, seed=rng))
         op.coeffs = rng.normal(0, 10, size)
 
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         findiff = FiniteDiffEstimatorGradient(estimator, 1e-6)
         gradient = grad(estimator)
 
@@ -407,7 +408,7 @@ def test_gradient_random_parameters(self, grad):
     @unpack
     def test_complex_gradient(self, derivative_type, expected_gradient_value):
         """Tests if the ``LinCombEstimatorGradient`` has the correct value."""
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         lcu = LinCombEstimatorGradient(estimator, derivative_type=derivative_type)
         reverse = ReverseEstimatorGradient(derivative_type=derivative_type)
 
@@ -424,54 +425,54 @@ def test_complex_gradient(self, derivative_type, expected_gradient_value):
         LinCombEstimatorGradient,
         SPSAEstimatorGradient,
     )
-    def test_options(self, grad):
-        """Test estimator gradient's run options"""
+    def test_precision(self, grad):
+        """Test estimator gradient's precision"""
         a = Parameter("a")
         qc = QuantumCircuit(1)
         qc.rx(a, 0)
         op = SparsePauliOp.from_list([("Z", 1)])
-        estimator = Estimator(options={"shots": 100})
+        estimator = StatevectorEstimator(default_precision=0.2)
         with self.subTest("estimator"):
             if grad is FiniteDiffEstimatorGradient or grad is SPSAEstimatorGradient:
                 gradient = grad(estimator, epsilon=1e-6)
             else:
                 gradient = grad(estimator)
-            options = gradient.options
+            precision = gradient.precision
             result = gradient.run([qc], [op], [[1]]).result()
-            self.assertEqual(result.options.get("shots"), 100)
-            self.assertEqual(options.get("shots"), 100)
+            self.assertEqual(result.precision, 0.2)
+            self.assertEqual(precision, None)
 
         with self.subTest("gradient init"):
             if grad is FiniteDiffEstimatorGradient or grad is SPSAEstimatorGradient:
-                gradient = grad(estimator, epsilon=1e-6, options={"shots": 200})
+                gradient = grad(estimator, epsilon=1e-6, precision=0.3)
             else:
-                gradient = grad(estimator, options={"shots": 200})
-            options = gradient.options
+                gradient = grad(estimator, precision=0.3)
+            precision = gradient.precision
             result = gradient.run([qc], [op], [[1]]).result()
-            self.assertEqual(result.options.get("shots"), 200)
-            self.assertEqual(options.get("shots"), 200)
+            self.assertEqual(result.precision, 0.3)
+            self.assertEqual(precision, 0.3)
 
         with self.subTest("gradient update"):
             if grad is FiniteDiffEstimatorGradient or grad is SPSAEstimatorGradient:
-                gradient = grad(estimator, epsilon=1e-6, options={"shots": 200})
+                gradient = grad(estimator, epsilon=1e-6, precision=0.4)
             else:
-                gradient = grad(estimator, options={"shots": 200})
-            gradient.update_default_options(shots=100)
-            options = gradient.options
+                gradient = grad(estimator, precision=0.4)
+            gradient.precision = 0.5
+            precision = gradient.precision
             result = gradient.run([qc], [op], [[1]]).result()
-            self.assertEqual(result.options.get("shots"), 100)
-            self.assertEqual(options.get("shots"), 100)
+            self.assertEqual(result.precision, 0.5)
+            self.assertEqual(precision, 0.5)
 
         with self.subTest("gradient run"):
             if grad is FiniteDiffEstimatorGradient or grad is SPSAEstimatorGradient:
-                gradient = grad(estimator, epsilon=1e-6, options={"shots": 200})
+                gradient = grad(estimator, epsilon=1e-6, precision=0.6)
             else:
-                gradient = grad(estimator, options={"shots": 200})
-            options = gradient.options
-            result = gradient.run([qc], [op], [[1]], shots=300).result()
-            self.assertEqual(result.options.get("shots"), 300)
+                gradient = grad(estimator, precision=0.6)
+            precision = gradient.precision
+            result = gradient.run([qc], [op], [[1]], precision=0.7).result()
+            self.assertEqual(result.precision, 0.7)
             # Only default + estimator options. Not run.
-            self.assertEqual(options.get("shots"), 200)
+            self.assertEqual(precision, 0.6)
 
     @data(
         FiniteDiffEstimatorGradient,
@@ -524,6 +525,31 @@ def test_product_rule_check(self):
         with self.assertRaises(NotImplementedError):
             _ = derive_circuit(qc, p)
 
+    def test_transpiler(self):
+        """Test that the transpiler is called for the LinCombEstimatorGradient"""
+        pass_manager = generate_preset_pass_manager(optimization_level=1, seed_transpiler=42)
+        counts = [0]
+
+        def callback(**kwargs):
+            counts[0] = kwargs["count"]
+
+        a = Parameter("a")
+        qc = QuantumCircuit(1)
+        qc.rx(a, 0)
+        op = SparsePauliOp.from_list([("Z", 1)])
+        # Test transpiler without options
+        estimator = StatevectorEstimator(default_precision=0.2)
+        gradient = LinCombEstimatorGradient(estimator, transpiler=pass_manager)
+        gradient.run([qc], [op], [[1]]).result()
+
+        # Test that transpiler is called using callback function
+        gradient = LinCombEstimatorGradient(
+            estimator, transpiler=pass_manager, transpiler_options={"callback": callback}
+        )
+        gradient.run([qc], [op], [[1]]).result()
+
+        self.assertGreater(counts[0], 0)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/test/gradients/test_qfi.py b/test/gradients/test_qfi.py
index ad4f5cdb..17ba279e 100644
--- a/test/gradients/test_qfi.py
+++ b/test/gradients/test_qfi.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -22,7 +22,7 @@
 from qiskit import QuantumCircuit
 from qiskit.circuit import Parameter
 from qiskit.circuit.parametervector import ParameterVector
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 
 from qiskit_algorithms.gradients import LinCombQGT, ReverseQGT, QFI, DerivativeType
 
@@ -33,7 +33,7 @@ class TestQFI(QiskitAlgorithmsTestCase):
 
     def setUp(self):
         super().setUp()
-        self.estimator = Estimator()
+        self.estimator = StatevectorEstimator()
         self.lcu_qgt = LinCombQGT(self.estimator, derivative_type=DerivativeType.REAL)
         self.reverse_qgt = ReverseQGT(derivative_type=DerivativeType.REAL)
 
@@ -111,41 +111,41 @@ def expiz(qubit0, qubit1):
         qfi_result = qfi.run([ansatz], [param]).result().qfis
         np.testing.assert_array_almost_equal(qfi_result[0], reference, decimal=3)
 
-    def test_options(self):
-        """Test QFI's options"""
+    def test_precision(self):
+        """Test QFI's precision option"""
         a = Parameter("a")
         qc = QuantumCircuit(1)
         qc.rx(a, 0)
-        qgt = LinCombQGT(estimator=self.estimator, options={"shots": 100})
+        qgt = LinCombQGT(estimator=self.estimator, precision=0.1)
 
         with self.subTest("QGT"):
             qfi = QFI(qgt=qgt)
-            options = qfi.options
+            precision = qfi.precision
             result = qfi.run([qc], [[1]]).result()
-            self.assertEqual(result.options.get("shots"), 100)
-            self.assertEqual(options.get("shots"), 100)
+            self.assertEqual(result.precision, 0.1)
+            self.assertEqual(precision, None)
 
         with self.subTest("QFI init"):
-            qfi = QFI(qgt=qgt, options={"shots": 200})
+            qfi = QFI(qgt=qgt, precision=0.2)
             result = qfi.run([qc], [[1]]).result()
-            options = qfi.options
-            self.assertEqual(result.options.get("shots"), 200)
-            self.assertEqual(options.get("shots"), 200)
+            precision = qfi.precision
+            self.assertEqual(result.precision, 0.2)
+            self.assertEqual(precision, 0.2)
 
         with self.subTest("QFI update"):
-            qfi = QFI(qgt, options={"shots": 200})
-            qfi.update_default_options(shots=100)
-            options = qfi.options
+            qfi = QFI(qgt, precision=0.2)
+            qfi.precision = 0.1
+            precision = qfi.precision
             result = qfi.run([qc], [[1]]).result()
-            self.assertEqual(result.options.get("shots"), 100)
-            self.assertEqual(options.get("shots"), 100)
+            self.assertEqual(result.precision, 0.1)
+            self.assertEqual(precision, 0.1)
 
         with self.subTest("QFI run"):
-            qfi = QFI(qgt=qgt, options={"shots": 200})
-            result = qfi.run([qc], [[0]], shots=300).result()
-            options = qfi.options
-            self.assertEqual(result.options.get("shots"), 300)
-            self.assertEqual(options.get("shots"), 200)
+            qfi = QFI(qgt=qgt, precision=0.2)
+            result = qfi.run([qc], [[0]], precision=0.3).result()
+            precision = qfi.precision
+            self.assertEqual(result.precision, 0.3)
+            self.assertEqual(precision, 0.2)
 
 
 if __name__ == "__main__":
diff --git a/test/gradients/test_qgt.py b/test/gradients/test_qgt.py
index 905f4142..69a60fd0 100644
--- a/test/gradients/test_qgt.py
+++ b/test/gradients/test_qgt.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -19,10 +19,10 @@
 from ddt import ddt, data
 import numpy as np
 
-from qiskit import QuantumCircuit
+from qiskit import QuantumCircuit, generate_preset_pass_manager
 from qiskit.circuit import Parameter
 from qiskit.circuit.library import RealAmplitudes
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 
 from qiskit_algorithms.gradients import DerivativeType, LinCombQGT, ReverseQGT
 
@@ -35,7 +35,7 @@ class TestQGT(QiskitAlgorithmsTestCase):
 
     def setUp(self):
         super().setUp()
-        self.estimator = Estimator()
+        self.estimator = StatevectorEstimator()
 
     @data(LinCombQGT, ReverseQGT)
     def test_qgt_derivative_type(self, qgt_type):
@@ -241,41 +241,41 @@ def test_qgt_validation(self, qgt_type):
             with self.assertRaises(ValueError):
                 qgt.run([qc], parameter_values, parameters=[[a], [a]])
 
-    def test_options(self):
-        """Test QGT's options"""
+    def test_precision(self):
+        """Test QGT's precision option"""
         a = Parameter("a")
         qc = QuantumCircuit(1)
         qc.rx(a, 0)
-        estimator = Estimator(options={"shots": 100})
+        estimator = StatevectorEstimator(default_precision=0.1)
 
         with self.subTest("estimator"):
             qgt = LinCombQGT(estimator)
-            options = qgt.options
+            precision = qgt.precision
             result = qgt.run([qc], [[1]]).result()
-            self.assertEqual(result.options.get("shots"), 100)
-            self.assertEqual(options.get("shots"), 100)
+            self.assertEqual(result.precision, 0.1)
+            self.assertEqual(precision, None)
 
         with self.subTest("QGT init"):
-            qgt = LinCombQGT(estimator, options={"shots": 200})
+            qgt = LinCombQGT(estimator, precision=0.2)
             result = qgt.run([qc], [[1]]).result()
-            options = qgt.options
-            self.assertEqual(result.options.get("shots"), 200)
-            self.assertEqual(options.get("shots"), 200)
+            precision = qgt.precision
+            self.assertEqual(result.precision, 0.2)
+            self.assertEqual(precision, 0.2)
 
         with self.subTest("QGT update"):
-            qgt = LinCombQGT(estimator, options={"shots": 200})
-            qgt.update_default_options(shots=100)
-            options = qgt.options
+            qgt = LinCombQGT(estimator, precision=0.2)
+            qgt.precision = 0.1
+            precision = qgt.precision
             result = qgt.run([qc], [[1]]).result()
-            self.assertEqual(result.options.get("shots"), 100)
-            self.assertEqual(options.get("shots"), 100)
+            self.assertEqual(result.precision, 0.1)
+            self.assertEqual(precision, 0.1)
 
         with self.subTest("QGT run"):
-            qgt = LinCombQGT(estimator, options={"shots": 200})
-            result = qgt.run([qc], [[0]], shots=300).result()
-            options = qgt.options
-            self.assertEqual(result.options.get("shots"), 300)
-            self.assertEqual(options.get("shots"), 200)
+            qgt = LinCombQGT(estimator, precision=0.2)
+            result = qgt.run([qc], [[0]], precision=0.3).result()
+            precision = qgt.precision
+            self.assertEqual(result.precision, 0.3)
+            self.assertEqual(precision, 0.2)
 
     def test_operations_preserved(self):
         """Test non-parameterized instructions are preserved and not unrolled."""
@@ -305,6 +305,30 @@ def operations_callback(op):
         with self.subTest(msg="assert result is correct"):
             np.testing.assert_allclose(result.qgts[0], expect, atol=1e-5)
 
+    def test_transpiler(self):
+        """Test that the transpiler is called for the LinCombQGT"""
+        pass_manager = generate_preset_pass_manager(optimization_level=1, seed_transpiler=42)
+        counts = [0]
+
+        def callback(**kwargs):
+            counts[0] = kwargs["count"]
+
+        a = Parameter("a")
+        qc = QuantumCircuit(1)
+        qc.rx(a, 0)
+        estimator = StatevectorEstimator(default_precision=0.1)
+        # Test transpiler without options
+        qgt = LinCombQGT(estimator, transpiler=pass_manager)
+        qgt.run([qc], [[1]]).result()
+
+        # Test transpiler is called using callback function
+        qgt = LinCombQGT(
+            estimator, transpiler=pass_manager, transpiler_options={"callback": callback}
+        )
+        qgt.run([qc], [[1]]).result()
+
+        self.assertGreater(counts[0], 0)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/test/gradients/test_sampler_gradient.py b/test/gradients/test_sampler_gradient.py
index a038d712..4e4066b8 100644
--- a/test/gradients/test_sampler_gradient.py
+++ b/test/gradients/test_sampler_gradient.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2019, 2024.
+# (C) Copyright IBM 2019, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -15,17 +15,14 @@
 
 import unittest
 from test import QiskitAlgorithmsTestCase
-from typing import List
 
 import numpy as np
-from ddt import ddt, data
-
-from qiskit import QuantumCircuit
+from ddt import ddt, data, unpack
+from qiskit import QuantumCircuit, generate_preset_pass_manager
 from qiskit.circuit import Parameter
 from qiskit.circuit.library import EfficientSU2, RealAmplitudes
 from qiskit.circuit.library.standard_gates import RXXGate
-from qiskit.primitives import Sampler
-from qiskit.result import QuasiDistribution
+from qiskit.primitives import StatevectorSampler
 
 from qiskit_algorithms.gradients import (
     FiniteDiffSamplerGradient,
@@ -33,15 +30,29 @@
     ParamShiftSamplerGradient,
     SPSASamplerGradient,
 )
-
 from .logging_primitives import LoggingSampler
 
 gradient_factories = [
-    lambda sampler: FiniteDiffSamplerGradient(sampler, epsilon=1e-6, method="central"),
-    lambda sampler: FiniteDiffSamplerGradient(sampler, epsilon=1e-6, method="forward"),
-    lambda sampler: FiniteDiffSamplerGradient(sampler, epsilon=1e-6, method="backward"),
-    ParamShiftSamplerGradient,
-    LinCombSamplerGradient,
+    (
+        lambda sampler: FiniteDiffSamplerGradient(sampler, epsilon=1e-2, method="central"),
+        3_000_000,
+        1e-1,
+        1e-1,
+    ),
+    (
+        lambda sampler: FiniteDiffSamplerGradient(sampler, epsilon=1e-2, method="forward"),
+        3_000_000,
+        1e-1,
+        1e-1,
+    ),
+    (
+        lambda sampler: FiniteDiffSamplerGradient(sampler, epsilon=1e-2, method="backward"),
+        3_000_000,
+        1e-1,
+        1e-1,
+    ),
+    (ParamShiftSamplerGradient, 1_000_000, 1e-2, 1e-2),
+    (LinCombSamplerGradient, 1_000_000, 1e-2, 1e-2),
 ]
 
 
@@ -49,10 +60,17 @@
 class TestSamplerGradient(QiskitAlgorithmsTestCase):
     """Test Sampler Gradient"""
 
+    def setUp(self):
+        super().setUp()
+        self.seed = 42
+
     @data(*gradient_factories)
-    def test_single_circuit(self, grad):
+    @unpack
+    def test_single_circuit(self, grad, shots, atol, rtol):
         """Test the sampler gradient for a single circuit"""
-        sampler = Sampler()
+        sampler = StatevectorSampler(
+            default_shots=shots, seed=np.random.default_rng(seed=self.seed)
+        )
         a = Parameter("a")
         qc = QuantumCircuit(1)
         qc.h(0)
@@ -70,12 +88,15 @@ def test_single_circuit(self, grad):
             gradients = gradient.run([qc], [param]).result().gradients[0]
             array1 = _quasi2array(gradients, num_qubits=1)
             array2 = _quasi2array(expected[i], num_qubits=1)
-            np.testing.assert_allclose(array1, array2, atol=1e-3)
+            np.testing.assert_allclose(array1, array2, rtol=rtol, atol=atol)
 
     @data(*gradient_factories)
-    def test_gradient_p(self, grad):
+    @unpack
+    def test_gradient_p(self, grad, shots, atol, rtol):
         """Test the sampler gradient for p"""
-        sampler = Sampler()
+        sampler = StatevectorSampler(
+            default_shots=shots, seed=np.random.default_rng(seed=self.seed)
+        )
         a = Parameter("a")
         qc = QuantumCircuit(1)
         qc.h(0)
@@ -87,18 +108,21 @@ def test_gradient_p(self, grad):
         expected = [
             [{0: -0.5 / np.sqrt(2), 1: 0.5 / np.sqrt(2)}],
             [{0: 0, 1: 0}],
-            [{0: -0.499999, 1: 0.499999}],
+            [{0: -0.5, 1: 0.5}],
         ]
         for i, param in enumerate(param_list):
             gradients = gradient.run([qc], [param]).result().gradients[0]
             array1 = _quasi2array(gradients, num_qubits=1)
             array2 = _quasi2array(expected[i], num_qubits=1)
-            np.testing.assert_allclose(array1, array2, atol=1e-3)
+            np.testing.assert_allclose(array1, array2, atol=atol, rtol=rtol)
 
     @data(*gradient_factories)
-    def test_gradient_u(self, grad):
+    @unpack
+    def test_gradient_u(self, grad, shots, atol, rtol):
         """Test the sampler gradient for u"""
-        sampler = Sampler()
+        sampler = StatevectorSampler(
+            default_shots=shots, seed=np.random.default_rng(seed=self.seed)
+        )
         a = Parameter("a")
         b = Parameter("b")
         c = Parameter("c")
@@ -117,30 +141,33 @@ def test_gradient_u(self, grad):
             gradients = gradient.run([qc], [param]).result().gradients[0]
             array1 = _quasi2array(gradients, num_qubits=1)
             array2 = _quasi2array(expected[i], num_qubits=1)
-            np.testing.assert_allclose(array1, array2, atol=1e-3)
+            np.testing.assert_allclose(array1, array2, atol=atol, rtol=rtol)
 
     @data(*gradient_factories)
-    def test_gradient_efficient_su2(self, grad):
+    @unpack
+    def test_gradient_efficient_su2(self, grad, shots, atol, rtol):
         """Test the sampler gradient for EfficientSU2"""
-        sampler = Sampler()
+        sampler = StatevectorSampler(
+            default_shots=shots, seed=np.random.default_rng(seed=self.seed)
+        )
         qc = EfficientSU2(2, reps=1)
         qc.measure_all()
         gradient = grad(sampler)
         param_list = [
-            [np.pi / 4 for param in qc.parameters],
-            [np.pi / 2 for param in qc.parameters],
+            [np.pi / 4 for _ in qc.parameters],
+            [np.pi / 2 for _ in qc.parameters],
         ]
         expected = [
             [
                 {
                     0: -0.11963834764831836,
                     1: -0.05713834764831845,
-                    2: -0.21875000000000003,
+                    2: -0.21875,
                     3: 0.39552669529663675,
                 },
                 {
                     0: -0.32230339059327373,
-                    1: -0.031250000000000014,
+                    1: -0.03125,
                     2: 0.2339150429449554,
                     3: 0.11963834764831843,
                 },
@@ -159,7 +186,7 @@ def test_gradient_efficient_su2(self, grad):
                 {
                     0: -0.11963834764831838,
                     1: 0.11963834764831838,
-                    2: -0.21875000000000003,
+                    2: -0.21875,
                     3: 0.21875,
                 },
                 {
@@ -173,37 +200,37 @@ def test_gradient_efficient_su2(self, grad):
             ],
             [
                 {
-                    0: -4.163336342344337e-17,
-                    1: 2.7755575615628914e-17,
-                    2: -4.163336342344337e-17,
+                    0: 0.0,
+                    1: 0.0,
+                    2: 0.0,
                     3: 0.0,
                 },
-                {0: 0.0, 1: -1.3877787807814457e-17, 2: 4.163336342344337e-17, 3: 0.0},
+                {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0},
                 {
-                    0: -0.24999999999999994,
-                    1: 0.24999999999999994,
-                    2: 0.24999999999999994,
-                    3: -0.24999999999999994,
+                    0: -0.25,
+                    1: 0.25,
+                    2: 0.25,
+                    3: -0.25,
                 },
                 {
-                    0: 0.24999999999999994,
-                    1: 0.24999999999999994,
-                    2: -0.24999999999999994,
-                    3: -0.24999999999999994,
+                    0: 0.25,
+                    1: 0.25,
+                    2: -0.25,
+                    3: -0.25,
                 },
                 {
-                    0: -4.163336342344337e-17,
-                    1: 4.163336342344337e-17,
-                    2: -4.163336342344337e-17,
-                    3: 5.551115123125783e-17,
+                    0: 0.0,
+                    1: 0.0,
+                    2: 0.0,
+                    3: 0.0,
                 },
                 {
-                    0: -0.24999999999999994,
-                    1: 0.24999999999999994,
-                    2: 0.24999999999999994,
-                    3: -0.24999999999999994,
+                    0: -0.25,
+                    1: 0.25,
+                    2: 0.25,
+                    3: -0.25,
                 },
-                {0: 0.0, 1: 2.7755575615628914e-17, 2: 0.0, 3: 2.7755575615628914e-17},
+                {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0},
                 {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0},
             ],
         ]
@@ -211,12 +238,15 @@ def test_gradient_efficient_su2(self, grad):
             gradients = gradient.run([qc], [param]).result().gradients[0]
             array1 = _quasi2array(gradients, num_qubits=2)
             array2 = _quasi2array(expected[i], num_qubits=2)
-            np.testing.assert_allclose(array1, array2, atol=1e-3)
+            np.testing.assert_allclose(array1, array2, atol=atol, rtol=rtol)
 
     @data(*gradient_factories)
-    def test_gradient_2qubit_gate(self, grad):
+    @unpack
+    def test_gradient_2qubit_gate(self, grad, shots, atol, rtol):
         """Test the sampler gradient for 2 qubit gates"""
-        sampler = Sampler()
+        sampler = StatevectorSampler(
+            default_shots=shots, seed=np.random.default_rng(seed=self.seed)
+        )
         for gate in [RXXGate]:
             param_list = [[np.pi / 4], [np.pi / 2]]
             correct_results = [
@@ -232,12 +262,15 @@ def test_gradient_2qubit_gate(self, grad):
                 gradients = gradient.run([qc], [param]).result().gradients[0]
                 array1 = _quasi2array(gradients, num_qubits=2)
                 array2 = _quasi2array(correct_results[i], num_qubits=2)
-                np.testing.assert_allclose(array1, array2, atol=1e-3)
+                np.testing.assert_allclose(array1, array2, atol=atol, rtol=rtol)
 
     @data(*gradient_factories)
-    def test_gradient_parameter_coefficient(self, grad):
+    @unpack
+    def test_gradient_parameter_coefficient(self, grad, shots, atol, rtol):
         """Test the sampler gradient for parameter variables with coefficients"""
-        sampler = Sampler()
+        sampler = StatevectorSampler(
+            default_shots=shots, seed=np.random.default_rng(seed=self.seed)
+        )
         qc = RealAmplitudes(num_qubits=2, reps=1)
         qc.rz(qc.parameters[0].exp() + 2 * qc.parameters[1], 0)
         qc.rx(3.0 * qc.parameters[0] + qc.parameters[1].sin(), 1)
@@ -306,12 +339,15 @@ def test_gradient_parameter_coefficient(self, grad):
             gradients = gradient.run([qc], [param]).result().gradients[0]
             array1 = _quasi2array(gradients, num_qubits=2)
             array2 = _quasi2array(correct_results[i], num_qubits=2)
-            np.testing.assert_allclose(array1, array2, atol=1e-3)
+            np.testing.assert_allclose(array1, array2, atol=atol, rtol=rtol)
 
     @data(*gradient_factories)
-    def test_gradient_parameters(self, grad):
+    @unpack
+    def test_gradient_parameters(self, grad, shots, atol, rtol):
         """Test the sampler gradient for parameters"""
-        sampler = Sampler()
+        sampler = StatevectorSampler(
+            default_shots=shots, seed=np.random.default_rng(seed=self.seed)
+        )
         a = Parameter("a")
         b = Parameter("b")
         qc = QuantumCircuit(1)
@@ -327,7 +363,7 @@ def test_gradient_parameters(self, grad):
             gradients = gradient.run([qc], [param], parameters=[[a]]).result().gradients[0]
             array1 = _quasi2array(gradients, num_qubits=1)
             array2 = _quasi2array(expected[i], num_qubits=1)
-            np.testing.assert_allclose(array1, array2, atol=1e-3)
+            np.testing.assert_allclose(array1, array2, atol=atol, rtol=rtol)
 
         # parameter order
         with self.subTest(msg="The order of gradients"):
@@ -363,12 +399,15 @@ def test_gradient_parameters(self, grad):
                 gradients = gradient.run([qc], param_values, parameters=[p]).result().gradients[0]
                 array1 = _quasi2array(gradients, num_qubits=1)
                 array2 = _quasi2array(expected[i], num_qubits=1)
-                np.testing.assert_allclose(array1, array2, atol=1e-3)
+                np.testing.assert_allclose(array1, array2, atol=atol, rtol=rtol)
 
     @data(*gradient_factories)
-    def test_gradient_multi_arguments(self, grad):
+    @unpack
+    def test_gradient_multi_arguments(self, grad, shots, atol, rtol):
         """Test the sampler gradient for multiple arguments"""
-        sampler = Sampler()
+        sampler = StatevectorSampler(
+            default_shots=shots, seed=np.random.default_rng(seed=self.seed)
+        )
         a = Parameter("a")
         b = Parameter("b")
         qc = QuantumCircuit(1)
@@ -381,13 +420,13 @@ def test_gradient_multi_arguments(self, grad):
         param_list = [[np.pi / 4], [np.pi / 2]]
         correct_results = [
             [{0: -0.5 / np.sqrt(2), 1: 0.5 / np.sqrt(2)}],
-            [{0: -0.499999, 1: 0.499999}],
+            [{0: -0.5, 1: 0.5}],
         ]
         gradients = gradient.run([qc, qc2], param_list).result().gradients
         for i, q_dists in enumerate(gradients):
             array1 = _quasi2array(q_dists, num_qubits=1)
             array2 = _quasi2array(correct_results[i], num_qubits=1)
-            np.testing.assert_allclose(array1, array2, atol=1e-3)
+            np.testing.assert_allclose(array1, array2, atol=atol, rtol=rtol)
 
         # parameters
         with self.subTest(msg="Different parameters"):
@@ -410,12 +449,14 @@ def test_gradient_multi_arguments(self, grad):
             for i, q_dists in enumerate(gradients):
                 array1 = _quasi2array(q_dists, num_qubits=1)
                 array2 = _quasi2array(correct_results[i], num_qubits=1)
-                np.testing.assert_allclose(array1, array2, atol=1e-3)
+                np.testing.assert_allclose(array1, array2, atol=atol, rtol=rtol)
 
     @data(*gradient_factories)
-    def test_gradient_validation(self, grad):
+    @unpack
+    # pylint: disable=unused-argument
+    def test_gradient_validation(self, grad, shots, atol, rtol):
         """Test sampler gradient's validation"""
-        sampler = Sampler()
+        sampler = StatevectorSampler()
         a = Parameter("a")
         qc = QuantumCircuit(1)
         qc.rx(a, 0)
@@ -431,7 +472,7 @@ def test_gradient_validation(self, grad):
 
     def test_spsa_gradient(self):
         """Test the SPSA sampler gradient"""
-        sampler = Sampler()
+        sampler = StatevectorSampler(default_shots=3_000_000, seed=np.random.default_rng(self.seed))
         with self.assertRaises(ValueError):
             _ = SPSASamplerGradient(sampler, epsilon=-0.1)
 
@@ -449,12 +490,12 @@ def test_spsa_gradient(self):
                 {0: -0.2273244, 1: 0.6480598, 2: -0.2273244, 3: -0.1934111},
             ],
         ]
-        gradient = SPSASamplerGradient(sampler, epsilon=1e-6, seed=123)
+        gradient = SPSASamplerGradient(sampler, epsilon=1e-2, seed=123)
         for i, param in enumerate(param_list):
             gradients = gradient.run([qc], [param]).result().gradients[0]
             array1 = _quasi2array(gradients, num_qubits=2)
             array2 = _quasi2array(correct_results[i], num_qubits=2)
-            np.testing.assert_allclose(array1, array2, atol=1e-3)
+            np.testing.assert_allclose(array1, array2, atol=1e-1, rtol=1e-1)
 
         # multi parameters
         with self.subTest(msg="Multiple parameters"):
@@ -472,19 +513,19 @@ def test_spsa_gradient(self):
                     {0: 0.0141129, 1: 0.0564471, 2: 0.3642884, 3: -0.4348484},
                 ],
             ]
-            gradient = SPSASamplerGradient(sampler, epsilon=1e-6, seed=123)
+            gradient = SPSASamplerGradient(sampler, epsilon=1e-2, seed=123)
             gradients = (
                 gradient.run([qc] * 3, param_list2, parameters=[None, [b], None]).result().gradients
             )
             for i, result in enumerate(gradients):
                 array1 = _quasi2array(result, num_qubits=2)
                 array2 = _quasi2array(correct_results2[i], num_qubits=2)
-                np.testing.assert_allclose(array1, array2, atol=1e-3)
+                np.testing.assert_allclose(array1, array2, atol=1e-1, rtol=1e-1)
 
         # batch size
         with self.subTest(msg="Batch size"):
             param_list = [[1, 1]]
-            gradient = SPSASamplerGradient(sampler, epsilon=1e-6, batch_size=4, seed=123)
+            gradient = SPSASamplerGradient(sampler, epsilon=1e-2, batch_size=4, seed=123)
             gradients = gradient.run([qc], param_list).result().gradients
             correct_results3 = [
                 [
@@ -505,7 +546,7 @@ def test_spsa_gradient(self):
             for i, q_dists in enumerate(gradients):
                 array1 = _quasi2array(q_dists, num_qubits=2)
                 array2 = _quasi2array(correct_results3[i], num_qubits=2)
-                np.testing.assert_allclose(array1, array2, atol=1e-3)
+                np.testing.assert_allclose(array1, array2, atol=1e-1, rtol=1e-1)
 
         # parameter order
         with self.subTest(msg="The order of gradients"):
@@ -537,12 +578,16 @@ def test_spsa_gradient(self):
                 ],
             ]
             for i, p in enumerate(param):  # pylint: disable=invalid-name
-                gradient = SPSASamplerGradient(sampler, epsilon=1e-6, seed=123)
+                gradient = SPSASamplerGradient(sampler, epsilon=1e-2, seed=123)
                 gradients = gradient.run([qc], param_list, parameters=[p]).result().gradients[0]
                 array1 = _quasi2array(gradients, num_qubits=1)
                 array2 = _quasi2array(correct_results[i], num_qubits=1)
-                np.testing.assert_allclose(array1, array2, atol=1e-3)
+                np.testing.assert_allclose(array1, array2, atol=1e-1, rtol=1e-1)
 
+    @unittest.skip(
+        "Should pass in approximately 3 hours and 20 minutes, and we're not sure to see the point "
+        "of this test?"
+    )
     @data(ParamShiftSamplerGradient, LinCombSamplerGradient)
     def test_gradient_random_parameters(self, grad):
         """Test param shift and lin comb w/ random parameters"""
@@ -569,8 +614,10 @@ def test_gradient_random_parameters(self, grad):
         qc.global_phase = params[0] * params[1] + params[2].cos().exp()
         qc.measure_all()
 
-        sampler = Sampler()
-        findiff = FiniteDiffSamplerGradient(sampler, 1e-6)
+        sampler = StatevectorSampler(
+            default_shots=1_000_000, seed=np.random.default_rng(seed=self.seed)
+        )
+        findiff = FiniteDiffSamplerGradient(sampler, 1e-2)
         gradient = grad(sampler)
 
         num_qubits = qc.num_qubits
@@ -582,7 +629,7 @@ def test_gradient_random_parameters(self, grad):
         for res1, res2 in zip(result1, result2):
             array1 = _quasi2array(res1, num_qubits)
             array2 = _quasi2array(res2, num_qubits)
-            np.testing.assert_allclose(array1, array2, rtol=1e-4)
+            np.testing.assert_allclose(array1, array2, rtol=1e-1, atol=1e-1)
 
     @data(
         FiniteDiffSamplerGradient,
@@ -590,54 +637,54 @@ def test_gradient_random_parameters(self, grad):
         LinCombSamplerGradient,
         SPSASamplerGradient,
     )
-    def test_options(self, grad):
-        """Test sampler gradient's run options"""
+    def test_shots(self, grad):
+        """Test sampler gradient's shots options"""
         a = Parameter("a")
         qc = QuantumCircuit(1)
         qc.rx(a, 0)
         qc.measure_all()
-        sampler = Sampler(options={"shots": 100})
+        sampler = StatevectorSampler(default_shots=100)
         with self.subTest("sampler"):
             if grad is FiniteDiffSamplerGradient or grad is SPSASamplerGradient:
                 gradient = grad(sampler, epsilon=1e-6)
             else:
                 gradient = grad(sampler)
-            options = gradient.options
+            shots = gradient.shots
             result = gradient.run([qc], [[1]]).result()
-            self.assertEqual(result.options.get("shots"), 100)
-            self.assertEqual(options.get("shots"), 100)
+            self.assertEqual(result.shots, 100)
+            self.assertEqual(shots, None)
 
         with self.subTest("gradient init"):
             if grad is FiniteDiffSamplerGradient or grad is SPSASamplerGradient:
-                gradient = grad(sampler, epsilon=1e-6, options={"shots": 200})
+                gradient = grad(sampler, epsilon=1e-6, shots=200)
             else:
-                gradient = grad(sampler, options={"shots": 200})
-            options = gradient.options
+                gradient = grad(sampler, shots=200)
+            shots = gradient.shots
             result = gradient.run([qc], [[1]]).result()
-            self.assertEqual(result.options.get("shots"), 200)
-            self.assertEqual(options.get("shots"), 200)
+            self.assertEqual(result.shots, 200)
+            self.assertEqual(shots, 200)
 
         with self.subTest("gradient update"):
             if grad is FiniteDiffSamplerGradient or grad is SPSASamplerGradient:
-                gradient = grad(sampler, epsilon=1e-6, options={"shots": 200})
+                gradient = grad(sampler, epsilon=1e-6, shots=200)
             else:
-                gradient = grad(sampler, options={"shots": 200})
-            gradient.update_default_options(shots=100)
-            options = gradient.options
+                gradient = grad(sampler, shots=200)
+            gradient.shots = 300
+            shots = gradient.shots
             result = gradient.run([qc], [[1]]).result()
-            self.assertEqual(result.options.get("shots"), 100)
-            self.assertEqual(options.get("shots"), 100)
+            self.assertEqual(result.shots, 300)
+            self.assertEqual(shots, 300)
 
         with self.subTest("gradient run"):
             if grad is FiniteDiffSamplerGradient or grad is SPSASamplerGradient:
-                gradient = grad(sampler, epsilon=1e-6, options={"shots": 200})
+                gradient = grad(sampler, epsilon=1e-6, shots=200)
             else:
-                gradient = grad(sampler, options={"shots": 200})
-            options = gradient.options
-            result = gradient.run([qc], [[1]], shots=300).result()
-            self.assertEqual(result.options.get("shots"), 300)
-            # Only default + sampler options. Not run.
-            self.assertEqual(options.get("shots"), 200)
+                gradient = grad(sampler, shots=200)
+            shots = gradient.shots
+            result = gradient.run([qc], [[1]], shots=400).result()
+            self.assertEqual(result.shots, 400)
+            # Only default + sampler shots. Not run.
+            self.assertEqual(shots, 200)
 
     @data(
         FiniteDiffSamplerGradient,
@@ -661,10 +708,10 @@ def test_operations_preserved(self, gradient_cls):
         def operations_callback(op):
             ops.append(op)
 
-        sampler = LoggingSampler(operations_callback=operations_callback)
+        sampler = LoggingSampler(shots=3_000_000, operations_callback=operations_callback)
 
         if gradient_cls in [SPSASamplerGradient, FiniteDiffSamplerGradient]:
-            gradient = gradient_cls(sampler, epsilon=0.01)
+            gradient = gradient_cls(sampler, epsilon=1e-2)
         else:
             gradient = gradient_cls(sampler)
 
@@ -677,10 +724,34 @@ def operations_callback(op):
         with self.subTest(msg="assert result is correct"):
             array1 = _quasi2array(result.gradients[0], num_qubits=2)
             array2 = _quasi2array(expect, num_qubits=2)
-            np.testing.assert_allclose(array1, array2, atol=1e-5)
+            np.testing.assert_allclose(array1, array2, atol=1e-1, rtol=1e-1)
+
+    def test_transpiler(self):
+        """Test that the transpiler is called for the LinCombSamplerGradient"""
+        pass_manager = generate_preset_pass_manager(optimization_level=1, seed_transpiler=42)
+        counts = [0]
+
+        def callback(**kwargs):
+            counts[0] = kwargs["count"]
+
+        a = Parameter("a")
+        qc = QuantumCircuit(1)
+        qc.rx(a, 0)
+        sampler = StatevectorSampler()
+        # Test transpilation without options
+        gradient = LinCombSamplerGradient(sampler, transpiler=pass_manager)
+        gradient.run([qc], [[1]]).result()
+
+        # Test transpiler is called using callback function
+        gradient = LinCombSamplerGradient(
+            sampler, transpiler=pass_manager, transpiler_options={"callback": callback}
+        )
+        gradient.run([qc], [[1]]).result()
+
+        self.assertGreater(counts[0], 0)
 
 
-def _quasi2array(quasis: List[QuasiDistribution], num_qubits: int) -> np.ndarray:
+def _quasi2array(quasis: list[dict], num_qubits: int) -> np.ndarray:
     ret = np.zeros((len(quasis), 2**num_qubits))
     for i, quasi in enumerate(quasis):
         ret[i, list(quasi.keys())] = list(quasi.values())
diff --git a/test/minimum_eigensolvers/test_adapt_vqe.py b/test/minimum_eigensolvers/test_adapt_vqe.py
index 4a13c9ca..3081384e 100644
--- a/test/minimum_eigensolvers/test_adapt_vqe.py
+++ b/test/minimum_eigensolvers/test_adapt_vqe.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,7 +21,7 @@
 
 from qiskit.circuit import QuantumCircuit, QuantumRegister
 from qiskit.circuit.library import EvolvedOperatorAnsatz
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 from qiskit.quantum_info import SparsePauliOp
 
 from qiskit_algorithms.minimum_eigensolvers import VQE
@@ -83,7 +83,7 @@ def setUp(self):
 
     def test_default(self):
         """Default execution"""
-        calc = AdaptVQE(VQE(Estimator(), self.ansatz, self.optimizer))
+        calc = AdaptVQE(VQE(StatevectorEstimator(), self.ansatz, self.optimizer))
 
         res = calc.compute_minimum_eigenvalue(operator=self.h2_op)
 
@@ -98,7 +98,7 @@ def test_with_quantum_info(self):
             self.excitation_pool,
             initial_state=self.initial_state,
         )
-        calc = AdaptVQE(VQE(Estimator(), ansatz, self.optimizer))
+        calc = AdaptVQE(VQE(StatevectorEstimator(), ansatz, self.optimizer))
 
         res = calc.compute_minimum_eigenvalue(operator=self.h2_op)
 
@@ -110,7 +110,7 @@ def test_with_quantum_info(self):
     def test_converged(self):
         """Test to check termination criteria"""
         calc = AdaptVQE(
-            VQE(Estimator(), self.ansatz, self.optimizer),
+            VQE(StatevectorEstimator(), self.ansatz, self.optimizer),
             gradient_threshold=1e-3,
         )
         res = calc.compute_minimum_eigenvalue(operator=self.h2_op)
@@ -120,7 +120,7 @@ def test_converged(self):
     def test_maximum(self):
         """Test to check termination criteria"""
         calc = AdaptVQE(
-            VQE(Estimator(), self.ansatz, self.optimizer),
+            VQE(StatevectorEstimator(), self.ansatz, self.optimizer),
             max_iterations=1,
         )
         res = calc.compute_minimum_eigenvalue(operator=self.h2_op)
@@ -144,7 +144,7 @@ def test_eigenvalue_threshold(self):
         )
 
         calc = AdaptVQE(
-            VQE(Estimator(), ansatz, self.optimizer),
+            VQE(StatevectorEstimator(), ansatz, self.optimizer),
             eigenvalue_threshold=1,
         )
         res = calc.compute_minimum_eigenvalue(operator)
@@ -185,14 +185,14 @@ def test_cyclicity(self, seq, is_cycle):
 
     def test_vqe_solver(self):
         """Test to check if the VQE solver remains the same or not"""
-        solver = VQE(Estimator(), self.ansatz, self.optimizer)
+        solver = VQE(StatevectorEstimator(), self.ansatz, self.optimizer)
         calc = AdaptVQE(solver)
         _ = calc.compute_minimum_eigenvalue(operator=self.h2_op)
         self.assertEqual(solver.ansatz, calc.solver.ansatz)
 
     def test_gradient_calculation(self):
         """Test to check if the gradient calculation"""
-        solver = VQE(Estimator(), QuantumCircuit(1), self.optimizer)
+        solver = VQE(StatevectorEstimator(), QuantumCircuit(1), self.optimizer)
         calc = AdaptVQE(solver)
         calc._excitation_pool = [SparsePauliOp("X")]
         res = calc._compute_gradients(operator=SparsePauliOp("Y"), theta=[])
@@ -201,7 +201,7 @@ def test_gradient_calculation(self):
 
     def test_supports_aux_operators(self):
         """Test that auxiliary operators are supported"""
-        calc = AdaptVQE(VQE(Estimator(), self.ansatz, self.optimizer))
+        calc = AdaptVQE(VQE(StatevectorEstimator(), self.ansatz, self.optimizer))
         res = calc.compute_minimum_eigenvalue(operator=self.h2_op, aux_operators=[self.h2_op])
 
         expected_eigenvalue = -1.85727503
diff --git a/test/minimum_eigensolvers/test_qaoa.py b/test/minimum_eigensolvers/test_qaoa.py
index 9770e7a2..dbda0325 100644
--- a/test/minimum_eigensolvers/test_qaoa.py
+++ b/test/minimum_eigensolvers/test_qaoa.py
@@ -19,18 +19,18 @@
 import numpy as np
 import rustworkx as rx
 from ddt import ddt, idata, unpack
-from scipy.optimize import minimize as scipy_minimize
-
-from qiskit import QuantumCircuit
+from qiskit import QuantumCircuit, generate_preset_pass_manager
 from qiskit.circuit import Parameter
-from qiskit.primitives import Sampler
+from qiskit.primitives import StatevectorSampler
 from qiskit.quantum_info import Pauli, SparsePauliOp
 from qiskit.result import QuasiDistribution
+from scipy.optimize import minimize as scipy_minimize
 
 from qiskit_algorithms.minimum_eigensolvers import QAOA
 from qiskit_algorithms.optimizers import COBYLA, NELDER_MEAD
 from qiskit_algorithms.utils import algorithm_globals
 
+
 W1 = np.array([[0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0]])
 P1 = 1
 M1 = SparsePauliOp.from_list(
@@ -65,9 +65,9 @@ class TestQAOA(QiskitAlgorithmsTestCase):
 
     def setUp(self):
         super().setUp()
-        self.seed = 10598
+        self.seed = 123
         algorithm_globals.random_seed = self.seed
-        self.sampler = Sampler()
+        self.sampler = StatevectorSampler(seed=42)
 
     @idata(
         [
@@ -85,8 +85,7 @@ def test_qaoa(self, w, reps, mixer, solutions):
         qaoa = QAOA(self.sampler, COBYLA(), reps=reps, mixer=mixer)
         result = qaoa.compute_minimum_eigenvalue(operator=qubit_op)
 
-        x = self._sample_most_likely(result.eigenstate)
-        graph_solution = self._get_graph_solution(x)
+        graph_solution = self._sample_most_likely(result.eigenstate)
         self.assertIn(graph_solution, solutions)
 
     @idata(
@@ -114,8 +113,7 @@ def test_qaoa_qc_mixer(self, w, prob, solutions):
 
         qaoa = QAOA(self.sampler, optimizer, reps=prob, mixer=mixer)
         result = qaoa.compute_minimum_eigenvalue(operator=qubit_op)
-        x = self._sample_most_likely(result.eigenstate)
-        graph_solution = self._get_graph_solution(x)
+        graph_solution = self._sample_most_likely(result.eigenstate)
         self.assertIn(graph_solution, solutions)
 
     def test_qaoa_qc_mixer_many_parameters(self):
@@ -131,9 +129,9 @@ def test_qaoa_qc_mixer_many_parameters(self):
 
         qaoa = QAOA(self.sampler, optimizer, reps=2, mixer=mixer, initial_point=[1] * 10)
         result = qaoa.compute_minimum_eigenvalue(operator=qubit_op)
-        x = self._sample_most_likely(result.eigenstate)
-        self.log.debug(x)
-        graph_solution = self._get_graph_solution(x)
+
+        graph_solution = self._sample_most_likely(result.eigenstate)
+        self.log.debug(graph_solution)
         self.assertIn(graph_solution, S1)
 
     def test_qaoa_qc_mixer_no_parameters(self):
@@ -157,8 +155,7 @@ def test_change_operator_size(self):
         )
         qaoa = QAOA(self.sampler, COBYLA(), reps=1)
         result = qaoa.compute_minimum_eigenvalue(operator=qubit_op)
-        x = self._sample_most_likely(result.eigenstate)
-        graph_solution = self._get_graph_solution(x)
+        graph_solution = self._sample_most_likely(result.eigenstate)
         with self.subTest(msg="QAOA 4x4"):
             self.assertIn(graph_solution, {"0101", "1010"})
 
@@ -175,12 +172,13 @@ def test_change_operator_size(self):
             )
         )
         result = qaoa.compute_minimum_eigenvalue(operator=qubit_op)
-        x = self._sample_most_likely(result.eigenstate)
-        graph_solution = self._get_graph_solution(x)
+        graph_solution = self._sample_most_likely(result.eigenstate)
         with self.subTest(msg="QAOA 6x6"):
             self.assertIn(graph_solution, {"010101", "101010"})
 
-    @idata([[W2, S2, None], [W2, S2, [0.0, 0.5]], [W2, S2, [1.0, 0.8]]])
+    # Can't start from [0.0, 0.0] with a seed, otherwise all initially tested points return the same
+    # value and the optimizer gets stuck
+    @idata([[W2, S2, None], [W2, S2, [3.0, 2.5]], [W2, S2, [1.0, 0.8]]])
     @unpack
     def test_qaoa_initial_point(self, w, solutions, init_pt):
         """Check first parameter value used is initial point as expected"""
@@ -200,9 +198,7 @@ def cb_callback(eval_count, parameters, mean, metadata):
             callback=cb_callback,
         )
         result = qaoa.compute_minimum_eigenvalue(operator=qubit_op)
-
-        x = self._sample_most_likely(result.eigenstate)
-        graph_solution = self._get_graph_solution(x)
+        graph_solution = self._sample_most_likely(result.eigenstate)
 
         with self.subTest("Initial Point"):
             # If None the preferred random initial point of QAOA variational form
@@ -238,6 +234,37 @@ def test_optimizer_scipy_callable(self):
         result = qaoa.compute_minimum_eigenvalue(qubit_op)
         self.assertEqual(result.cost_function_evals, 5)
 
+    def test_transpiler(self):
+        """Test that the transpiler is called"""
+        pass_manager = generate_preset_pass_manager(optimization_level=1, seed_transpiler=42)
+        counts = [0]
+
+        def callback(**kwargs):
+            counts[0] = kwargs["count"]
+
+        qubit_op, _ = self._get_operator(W1)
+
+        # Test transpiler without options
+        qaoa = QAOA(
+            self.sampler,
+            COBYLA(),
+            reps=2,
+            transpiler=pass_manager,
+        )
+        _ = qaoa.compute_minimum_eigenvalue(operator=qubit_op)
+
+        # Test transpiler is called using callback function
+        qaoa = QAOA(
+            self.sampler,
+            COBYLA(),
+            reps=2,
+            transpiler=pass_manager,
+            transpiler_options={"callback": callback},
+        )
+        _ = qaoa.compute_minimum_eigenvalue(operator=qubit_op)
+
+        self.assertGreater(counts[0], 0)
+
     def _get_operator(self, weight_matrix):
         """Generate Hamiltonian for the max-cut problem of a graph.
 
@@ -264,34 +291,15 @@ def _get_operator(self, weight_matrix):
         lst = [(pauli[1].to_label(), pauli[0]) for pauli in pauli_list]
         return SparsePauliOp.from_list(lst), shift
 
-    def _get_graph_solution(self, x: np.ndarray) -> str:
-        """Get graph solution from binary string.
-
-        Args:
-            x : binary string as numpy array.
-
-        Returns:
-            a graph solution as string.
-        """
-
-        return "".join([str(int(i)) for i in 1 - x])
-
-    def _sample_most_likely(self, state_vector: QuasiDistribution) -> np.ndarray:
+    def _sample_most_likely(self, state_vector: QuasiDistribution) -> str:
         """Compute the most likely binary string from state vector.
         Args:
             state_vector: Quasi-distribution.
 
         Returns:
-            Binary string as numpy.ndarray of ints.
+            Binary string.
         """
-        values = list(state_vector.values())
-        n = int(np.log2(len(values)))
-        k = np.argmax(np.abs(values))
-        x = np.zeros(n)
-        for i in range(n):
-            x[i] = k % 2
-            k >>= 1
-        return x
+        return max(state_vector.items(), key=lambda x: x[1])[0][::-1]
 
 
 if __name__ == "__main__":
diff --git a/test/minimum_eigensolvers/test_sampling_vqe.py b/test/minimum_eigensolvers/test_sampling_vqe.py
index a6452ba2..ab0cd200 100644
--- a/test/minimum_eigensolvers/test_sampling_vqe.py
+++ b/test/minimum_eigensolvers/test_sampling_vqe.py
@@ -23,12 +23,12 @@
 
 from qiskit.circuit import ParameterVector, QuantumCircuit
 from qiskit.circuit.library import RealAmplitudes, TwoLocal
-from qiskit.primitives import Sampler
+from qiskit.primitives import StatevectorSampler
 from qiskit.quantum_info import Pauli, SparsePauliOp
 
 from qiskit_algorithms import AlgorithmError
 from qiskit_algorithms.minimum_eigensolvers import SamplingVQE
-from qiskit_algorithms.optimizers import L_BFGS_B, QNSPSA, SLSQP, OptimizerResult
+from qiskit_algorithms.optimizers import SPSA, QNSPSA, SLSQP, OptimizerResult
 from qiskit_algorithms.state_fidelities import ComputeUncompute
 from qiskit_algorithms.utils import algorithm_globals
 
@@ -73,13 +73,15 @@ def test_exact_sampler(self, op):
         ansatz.ry(thetas[2], 0)
         ansatz.ry(thetas[3], 1)
 
-        optimizer = L_BFGS_B()
+        optimizer = SPSA()
 
         # start in maximal superposition
         initial_point = np.zeros(ansatz.num_parameters)
         initial_point[-ansatz.num_qubits :] = np.pi / 2
 
-        vqe = SamplingVQE(Sampler(), ansatz, optimizer, initial_point=initial_point)
+        vqe = SamplingVQE(
+            StatevectorSampler(seed=42), ansatz, optimizer, initial_point=initial_point
+        )
         result = vqe.compute_minimum_eigenvalue(operator=op)
 
         with self.subTest(msg="test eigenvalue"):
@@ -107,7 +109,7 @@ def test_invalid_initial_point(self, op):
         ansatz = RealAmplitudes(2, reps=1)
         initial_point = np.array([1])
 
-        vqe = SamplingVQE(Sampler(), ansatz, SLSQP(), initial_point=initial_point)
+        vqe = SamplingVQE(StatevectorSampler(), ansatz, SLSQP(), initial_point=initial_point)
 
         with self.assertRaises(ValueError):
             _ = vqe.compute_minimum_eigenvalue(operator=op)
@@ -116,7 +118,7 @@ def test_invalid_initial_point(self, op):
     def test_ansatz_resize(self, op):
         """Test the ansatz is properly resized if it's a blueprint circuit."""
         ansatz = RealAmplitudes(1, reps=1)
-        vqe = SamplingVQE(Sampler(), ansatz, SLSQP())
+        vqe = SamplingVQE(StatevectorSampler(seed=42), ansatz, SPSA())
         result = vqe.compute_minimum_eigenvalue(operator=op)
         self.assertAlmostEqual(result.eigenvalue, self.optimal_value, places=5)
 
@@ -125,7 +127,7 @@ def test_invalid_ansatz_size(self, op):
         """Test an error is raised if the ansatz has the wrong number of qubits."""
         ansatz = QuantumCircuit(1)
         ansatz.compose(RealAmplitudes(1, reps=2))
-        vqe = SamplingVQE(Sampler(), ansatz, SLSQP())
+        vqe = SamplingVQE(StatevectorSampler(), ansatz, SLSQP())
 
         with self.assertRaises(AlgorithmError):
             _ = vqe.compute_minimum_eigenvalue(operator=op)
@@ -134,15 +136,18 @@ def test_invalid_ansatz_size(self, op):
     def test_missing_varform_params(self, op):
         """Test specifying a variational form with no parameters raises an error."""
         circuit = QuantumCircuit(op.num_qubits)
-        vqe = SamplingVQE(Sampler(), circuit, SLSQP())
+        vqe = SamplingVQE(StatevectorSampler(), circuit, SLSQP())
         with self.assertRaises(AlgorithmError):
             vqe.compute_minimum_eigenvalue(operator=op)
 
+    @unittest.skip("SLSQP returns wrong results because of shot noise")
     @data(PAULI_OP)
     def test_batch_evaluate_slsqp(self, op):
         """Test batching with SLSQP (as representative of SciPyOptimizer)."""
         optimizer = SLSQP(max_evals_grouped=10)
-        vqe = SamplingVQE(Sampler(), RealAmplitudes(), optimizer)
+        vqe = SamplingVQE(
+            StatevectorSampler(default_shots=1_000_000, seed=42), RealAmplitudes(), optimizer
+        )
         result = vqe.compute_minimum_eigenvalue(operator=op)
         self.assertAlmostEqual(result.eigenvalue, self.optimal_value, places=5)
 
@@ -150,14 +155,14 @@ def test_batch_evaluate_with_qnspsa(self):
         """Test batch evaluating with QNSPSA works."""
         ansatz = TwoLocal(2, rotation_blocks=["ry", "rz"], entanglement_blocks="cz")
 
-        wrapped_sampler = Sampler()
-        inner_sampler = Sampler()
+        wrapped_sampler = StatevectorSampler(seed=42)
+        inner_sampler = StatevectorSampler(seed=43)
 
         callcount = {"count": 0}
 
         def wrapped_run(*args, **kwargs):
             kwargs["callcount"]["count"] += 1
-            return inner_sampler.run(*args, **kwargs)
+            return inner_sampler.run(*args)
 
         wrapped_sampler.run = partial(wrapped_run, callcount=callcount)
 
@@ -183,7 +188,7 @@ def fidelity_callable(left, right):
     def test_optimizer_scipy_callable(self):
         """Test passing a SciPy optimizer directly as callable."""
         vqe = SamplingVQE(
-            Sampler(),
+            StatevectorSampler(),
             RealAmplitudes(),
             partial(scipy_minimize, method="COBYLA", options={"maxiter": 8}),
         )
@@ -193,7 +198,7 @@ def test_optimizer_scipy_callable(self):
     def test_optimizer_callable(self):
         """Test passing a optimizer directly as callable."""
         ansatz = RealAmplitudes(1, reps=1)
-        vqe = SamplingVQE(Sampler(), ansatz, _mock_optimizer)
+        vqe = SamplingVQE(StatevectorSampler(), ansatz, _mock_optimizer)
         result = vqe.compute_minimum_eigenvalue(Pauli("Z"))
         self.assertTrue(np.all(result.optimal_point == np.zeros(ansatz.num_parameters)))
 
@@ -201,7 +206,7 @@ def test_optimizer_callable(self):
     def test_auxops(self, op):
         """Test passing auxiliary operators."""
         ansatz = RealAmplitudes(2, reps=1)
-        vqe = SamplingVQE(Sampler(), ansatz, SLSQP())
+        vqe = SamplingVQE(StatevectorSampler(seed=42), ansatz, SPSA())
 
         as_list = [Pauli("ZZ"), Pauli("II")]
         with self.subTest(auxops=as_list):
@@ -220,7 +225,7 @@ def test_auxops(self, op):
 
     def test_nondiag_observable_raises(self):
         """Test passing a non-diagonal observable raises an error."""
-        vqe = SamplingVQE(Sampler(), RealAmplitudes(), SLSQP())
+        vqe = SamplingVQE(StatevectorSampler(), RealAmplitudes(), SLSQP())
 
         with self.assertRaises(ValueError):
             _ = vqe.compute_minimum_eigenvalue(Pauli("X"))
@@ -242,7 +247,7 @@ def store_intermediate_result(eval_count, parameters, mean, metadata):
             history["metadata"].append(metadata)
 
         sampling_vqe = SamplingVQE(
-            Sampler(),
+            StatevectorSampler(),
             RealAmplitudes(2, reps=1),
             SLSQP(),
             callback=store_intermediate_result,
@@ -250,7 +255,7 @@ def store_intermediate_result(eval_count, parameters, mean, metadata):
         sampling_vqe.compute_minimum_eigenvalue(operator=op)
 
         self.assertTrue(all(isinstance(count, int) for count in history["eval_count"]))
-        self.assertTrue(all(isinstance(mean, complex) for mean in history["mean"]))
+        self.assertTrue(all(isinstance(mean, float) for mean in history["mean"]))
         self.assertTrue(all(isinstance(metadata, dict) for metadata in history["metadata"]))
         for params in history["parameters"]:
             self.assertTrue(all(isinstance(param, float) for param in params))
@@ -271,7 +276,9 @@ def best_measurement(measurements):
 
         for aggregation in [alpha, best_measurement]:
             with self.subTest(aggregation=aggregation):
-                vqe = SamplingVQE(Sampler(), ansatz, _mock_optimizer, aggregation=best_measurement)
+                vqe = SamplingVQE(
+                    StatevectorSampler(), ansatz, _mock_optimizer, aggregation=best_measurement
+                )
                 result = vqe.compute_minimum_eigenvalue(Pauli("Z"))
 
                 # evaluation at x0=0 samples -1 and 1 with 50% probability, and our aggregation
diff --git a/test/minimum_eigensolvers/test_vqe.py b/test/minimum_eigensolvers/test_vqe.py
index 053cfd05..fedb8cfd 100644
--- a/test/minimum_eigensolvers/test_vqe.py
+++ b/test/minimum_eigensolvers/test_vqe.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,6 +13,9 @@
 """Test the variational quantum eigensolver algorithm."""
 
 import unittest
+
+from qiskit.providers.fake_provider import GenericBackendV2
+
 from test import QiskitAlgorithmsTestCase
 
 from functools import partial
@@ -20,10 +23,10 @@
 from scipy.optimize import minimize as scipy_minimize
 from ddt import data, ddt
 
-from qiskit import QuantumCircuit
+from qiskit import QuantumCircuit, generate_preset_pass_manager
 from qiskit.circuit.library import RealAmplitudes, TwoLocal
 from qiskit.quantum_info import SparsePauliOp, Operator, Pauli
-from qiskit.primitives import Estimator, Sampler
+from qiskit.primitives import StatevectorEstimator, StatevectorSampler
 
 from qiskit_algorithms import AlgorithmError
 from qiskit_algorithms.gradients import ParamShiftEstimatorGradient
@@ -44,6 +47,9 @@
 from qiskit_algorithms.utils import algorithm_globals
 
 
+THREE_QUBITS_BACKEND = GenericBackendV2(num_qubits=3, coupling_map=[[0, 1], [1, 2]], seed=54)
+
+
 # pylint: disable=invalid-name
 def _mock_optimizer(fun, x0, jac=None, bounds=None, inputs=None) -> OptimizerResult:
     """A mock of a callable that can be used as minimizer in the VQE."""
@@ -83,7 +89,7 @@ def setUp(self):
     @data(L_BFGS_B(), COBYLA())
     def test_using_ref_estimator(self, optimizer):
         """Test VQE using reference Estimator."""
-        vqe = VQE(Estimator(), self.ryrz_wavefunction, optimizer)
+        vqe = VQE(StatevectorEstimator(seed=42), self.ryrz_wavefunction, optimizer)
 
         result = vqe.compute_minimum_eigenvalue(operator=self.h2_op)
 
@@ -109,9 +115,9 @@ def test_using_ref_estimator(self, optimizer):
             self.assertAlmostEqual(result.optimizer_result.fun, self.h2_energy, places=5)
 
         with self.subTest(msg="assert return ansatz is set"):
-            estimator = Estimator()
-            job = estimator.run(result.optimal_circuit, self.h2_op, result.optimal_point)
-            np.testing.assert_array_almost_equal(job.result().values, result.eigenvalue, 6)
+            estimator = StatevectorEstimator(seed=42)
+            job = estimator.run([(result.optimal_circuit, self.h2_op, result.optimal_point)])
+            np.testing.assert_array_almost_equal(job.result()[0].data.evs, result.eigenvalue, 6)
 
     def test_invalid_initial_point(self):
         """Test the proper error is raised when the initial point has the wrong size."""
@@ -119,7 +125,7 @@ def test_invalid_initial_point(self):
         initial_point = np.array([1])
 
         vqe = VQE(
-            Estimator(),
+            StatevectorEstimator(seed=42),
             ansatz,
             SLSQP(),
             initial_point=initial_point,
@@ -131,7 +137,7 @@ def test_invalid_initial_point(self):
     def test_ansatz_resize(self):
         """Test the ansatz is properly resized if it's a blueprint circuit."""
         ansatz = RealAmplitudes(1, reps=1)
-        vqe = VQE(Estimator(), ansatz, SLSQP())
+        vqe = VQE(StatevectorEstimator(seed=42), ansatz, SLSQP())
         result = vqe.compute_minimum_eigenvalue(self.h2_op)
         self.assertAlmostEqual(result.eigenvalue.real, self.h2_energy, places=5)
 
@@ -139,7 +145,7 @@ def test_invalid_ansatz_size(self):
         """Test an error is raised if the ansatz has the wrong number of qubits."""
         ansatz = QuantumCircuit(1)
         ansatz.compose(RealAmplitudes(1, reps=2))
-        vqe = VQE(Estimator(), ansatz, SLSQP())
+        vqe = VQE(StatevectorEstimator(), ansatz, SLSQP())
 
         with self.assertRaises(AlgorithmError):
             _ = vqe.compute_minimum_eigenvalue(operator=self.h2_op)
@@ -147,7 +153,7 @@ def test_invalid_ansatz_size(self):
     def test_missing_ansatz_params(self):
         """Test specifying an ansatz with no parameters raises an error."""
         ansatz = QuantumCircuit(self.h2_op.num_qubits)
-        vqe = VQE(Estimator(), ansatz, SLSQP())
+        vqe = VQE(StatevectorEstimator(), ansatz, SLSQP())
         with self.assertRaises(AlgorithmError):
             vqe.compute_minimum_eigenvalue(operator=self.h2_op)
 
@@ -155,7 +161,7 @@ def test_max_evals_grouped(self):
         """Test with SLSQP with max_evals_grouped."""
         optimizer = SLSQP(maxiter=50, max_evals_grouped=5)
         vqe = VQE(
-            Estimator(),
+            StatevectorEstimator(seed=42),
             self.ryrz_wavefunction,
             optimizer,
         )
@@ -171,7 +177,7 @@ def test_max_evals_grouped(self):
     )
     def test_with_gradient(self, optimizer):
         """Test VQE using gradient primitive."""
-        estimator = Estimator()
+        estimator = StatevectorEstimator(seed=42)
         vqe = VQE(
             estimator,
             self.ry_wavefunction,
@@ -184,7 +190,7 @@ def test_with_gradient(self, optimizer):
     def test_gradient_passed(self):
         """Test the gradient is properly passed into the optimizer."""
         inputs = {}
-        estimator = Estimator()
+        estimator = StatevectorEstimator(seed=42)
         vqe = VQE(
             estimator,
             RealAmplitudes(),
@@ -197,7 +203,7 @@ def test_gradient_passed(self):
 
     def test_gradient_run(self):
         """Test using the gradient to calculate the minimum."""
-        estimator = Estimator()
+        estimator = StatevectorEstimator(seed=42)
         vqe = VQE(
             estimator,
             RealAmplitudes(),
@@ -220,7 +226,7 @@ def store_intermediate_result(eval_count, parameters, mean, metadata):
         optimizer = COBYLA(maxiter=3)
         wavefunction = self.ry_wavefunction
 
-        estimator = Estimator()
+        estimator = StatevectorEstimator(seed=42)
 
         vqe = VQE(
             estimator,
@@ -239,7 +245,7 @@ def store_intermediate_result(eval_count, parameters, mean, metadata):
     def test_reuse(self):
         """Test re-using a VQE algorithm instance."""
         ansatz = TwoLocal(rotation_blocks=["ry", "rz"], entanglement_blocks="cz")
-        vqe = VQE(Estimator(), ansatz, SLSQP(maxiter=300))
+        vqe = VQE(StatevectorEstimator(seed=42), ansatz, SLSQP(maxiter=300))
         with self.subTest(msg="assert VQE works once all info is available"):
             result = vqe.compute_minimum_eigenvalue(operator=self.h2_op)
             self.assertAlmostEqual(result.eigenvalue.real, self.h2_energy, places=5)
@@ -254,7 +260,7 @@ def test_reuse(self):
     def test_vqe_optimizer_reuse(self):
         """Test running same VQE twice to re-use optimizer, then switch optimizer"""
         vqe = VQE(
-            Estimator(),
+            StatevectorEstimator(seed=42),
             self.ryrz_wavefunction,
             SLSQP(),
         )
@@ -276,14 +282,14 @@ def test_default_batch_evaluation_on_spsa(self):
         """Test the default batching works."""
         ansatz = TwoLocal(2, rotation_blocks=["ry", "rz"], entanglement_blocks="cz")
 
-        wrapped_estimator = Estimator()
-        inner_estimator = Estimator()
+        wrapped_estimator = StatevectorEstimator(seed=42)
+        inner_estimator = StatevectorEstimator(seed=43)
 
         callcount = {"estimator": 0}
 
         def wrapped_estimator_run(*args, **kwargs):
             kwargs["callcount"]["estimator"] += 1
-            return inner_estimator.run(*args, **kwargs)
+            return inner_estimator.run(*args)
 
         wrapped_estimator.run = partial(wrapped_estimator_run, callcount=callcount)
 
@@ -305,24 +311,24 @@ def test_batch_evaluate_with_qnspsa(self):
         """Test batch evaluating with QNSPSA works."""
         ansatz = TwoLocal(2, rotation_blocks=["ry", "rz"], entanglement_blocks="cz")
 
-        wrapped_sampler = Sampler()
-        inner_sampler = Sampler()
+        wrapped_sampler = StatevectorSampler(seed=42)
+        inner_sampler = StatevectorSampler(seed=43)
 
-        wrapped_estimator = Estimator()
-        inner_estimator = Estimator()
+        wrapped_estimator = StatevectorEstimator(seed=44)
+        inner_estimator = StatevectorEstimator(seed=45)
 
         callcount = {"sampler": 0, "estimator": 0}
 
-        def wrapped_estimator_run(*args, **kwargs):
-            kwargs["callcount"]["estimator"] += 1
-            return inner_estimator.run(*args, **kwargs)
+        def wrapped_estimator_run(*args):
+            callcount["estimator"] += 1
+            return inner_estimator.run(*args)
 
-        def wrapped_sampler_run(*args, **kwargs):
-            kwargs["callcount"]["sampler"] += 1
-            return inner_sampler.run(*args, **kwargs)
+        def wrapped_sampler_run(*args):
+            callcount["sampler"] += 1
+            return inner_sampler.run(*args)
 
-        wrapped_estimator.run = partial(wrapped_estimator_run, callcount=callcount)
-        wrapped_sampler.run = partial(wrapped_sampler_run, callcount=callcount)
+        wrapped_estimator.run = wrapped_estimator_run
+        wrapped_sampler.run = wrapped_sampler_run
 
         fidelity = ComputeUncompute(wrapped_sampler)
 
@@ -353,7 +359,7 @@ def fidelity_callable(left, right):
     def test_optimizer_scipy_callable(self):
         """Test passing a SciPy optimizer directly as callable."""
         vqe = VQE(
-            Estimator(),
+            StatevectorEstimator(seed=42),
             self.ryrz_wavefunction,
             partial(scipy_minimize, method="L-BFGS-B", options={"maxiter": 10}),
         )
@@ -363,13 +369,30 @@ def test_optimizer_scipy_callable(self):
     def test_optimizer_callable(self):
         """Test passing a optimizer directly as callable."""
         ansatz = RealAmplitudes(1, reps=1)
-        vqe = VQE(Estimator(), ansatz, _mock_optimizer)
+        vqe = VQE(StatevectorEstimator(seed=42), ansatz, _mock_optimizer)
         result = vqe.compute_minimum_eigenvalue(SparsePauliOp("Z"))
         self.assertTrue(np.all(result.optimal_point == np.zeros(ansatz.num_parameters)))
 
-    def test_aux_operators_list(self):
+    # Since we perform actions on the aux_operators when a transpiler is set, we have to check that it
+    # doesn't affect the final result
+    @data(
+        None,
+        generate_preset_pass_manager(
+            backend=THREE_QUBITS_BACKEND,
+            optimization_level=1,
+            seed_transpiler=42
+        )
+    )
+    def test_aux_operators_list(self, transpiler):
         """Test list-based aux_operators."""
-        vqe = VQE(Estimator(), self.ry_wavefunction, SLSQP(maxiter=300))
+        wavefunction = self.ry_wavefunction
+        wavefunction.num_qubits = 2
+        vqe = VQE(
+            StatevectorEstimator(seed=42),
+            wavefunction,
+            SLSQP(maxiter=300),
+            transpiler=transpiler
+        )
 
         with self.subTest("Test with an empty list."):
             result = vqe.compute_minimum_eigenvalue(self.h2_op, aux_operators=[])
@@ -406,9 +429,26 @@ def test_aux_operators_list(self):
             self.assertIsInstance(result.aux_operators_evaluated[1][1], dict)
             self.assertIsInstance(result.aux_operators_evaluated[2][1], dict)
 
-    def test_aux_operators_dict(self):
+    # Since we perform actions on the aux_operators when a transpiler is set, we have to check that it
+    # doesn't affect the final result
+    @data(
+        None,
+        generate_preset_pass_manager(
+            backend=THREE_QUBITS_BACKEND,
+            optimization_level=1,
+            seed_transpiler=42
+        )
+    )
+    def test_aux_operators_dict(self, transpiler):
         """Test dictionary compatibility of aux_operators"""
-        vqe = VQE(Estimator(), self.ry_wavefunction, SLSQP(maxiter=300))
+        wavefunction = self.ry_wavefunction
+        wavefunction.num_qubits = 2
+        vqe = VQE(
+            StatevectorEstimator(seed=42),
+            wavefunction,
+            SLSQP(maxiter=300),
+            transpiler=transpiler
+        )
 
         with self.subTest("Test with an empty dictionary."):
             result = vqe.compute_minimum_eigenvalue(self.h2_op, aux_operators={})
@@ -445,6 +485,33 @@ def test_aux_operators_dict(self):
             self.assertIsInstance(result.aux_operators_evaluated["aux_op2"][1], dict)
             self.assertIsInstance(result.aux_operators_evaluated["zero_operator"][1], dict)
 
+    @data(None, THREE_QUBITS_BACKEND)
+    def test_transpiler(self, backend):
+        """Test that the transpiler is called"""
+        pass_manager = generate_preset_pass_manager(
+            backend=backend,
+            optimization_level=1,
+            seed_transpiler=42
+        )
+        counts = [0]
+
+        def callback(**kwargs):
+            counts[0] = kwargs["count"]
+
+        wavefunction = self.ryrz_wavefunction
+        wavefunction.num_qubits = 2
+        vqe = VQE(
+            estimator=StatevectorEstimator(),
+            ansatz=wavefunction,
+            optimizer=COBYLA(),
+            transpiler=pass_manager,
+            transpiler_options={"callback": callback},
+        )
+
+        vqe.compute_minimum_eigenvalue(operator=self.h2_op)
+
+        self.assertGreater(counts[0], 0)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/test/optimizers/test_optimizer_aqgd.py b/test/optimizers/test_optimizer_aqgd.py
index a2d4a1f0..81f06d89 100644
--- a/test/optimizers/test_optimizer_aqgd.py
+++ b/test/optimizers/test_optimizer_aqgd.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2019, 2024.
+# (C) Copyright IBM 2019, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -17,7 +17,7 @@
 import numpy as np
 from ddt import ddt, data
 from qiskit.circuit.library import RealAmplitudes
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 from qiskit.quantum_info import SparsePauliOp
 
 from qiskit_algorithms import AlgorithmError
@@ -43,7 +43,7 @@ def setUp(self):
                 ("XX", 0.18093119978423156),
             ]
         )
-        self.estimator = Estimator()
+        self.estimator = StatevectorEstimator()
         self.gradient = LinCombEstimatorGradient(self.estimator)
 
     @slow_test
diff --git a/test/optimizers/test_optimizer_nft.py b/test/optimizers/test_optimizer_nft.py
index a99163d8..da00ecae 100644
--- a/test/optimizers/test_optimizer_nft.py
+++ b/test/optimizers/test_optimizer_nft.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2020, 2023.
+# (C) Copyright IBM 2020, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -15,7 +15,7 @@
 import unittest
 from test import QiskitAlgorithmsTestCase
 from qiskit.circuit.library import RealAmplitudes
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 from qiskit.quantum_info import SparsePauliOp
 
 from qiskit_algorithms.optimizers import NFT
@@ -40,7 +40,7 @@ def setUp(self):
     def test_nft(self):
         """Test NFT optimizer by using it"""
 
-        vqe = VQE(Estimator(), ansatz=RealAmplitudes(), optimizer=NFT())
+        vqe = VQE(StatevectorEstimator(), ansatz=RealAmplitudes(), optimizer=NFT())
 
         result = vqe.compute_minimum_eigenvalue(operator=self.qubit_op)
 
diff --git a/test/optimizers/test_optimizers.py b/test/optimizers/test_optimizers.py
index c74aad13..eba5e0d6 100644
--- a/test/optimizers/test_optimizers.py
+++ b/test/optimizers/test_optimizers.py
@@ -13,6 +13,7 @@
 """Test Optimizers"""
 
 import unittest
+
 from test import QiskitAlgorithmsTestCase
 
 from typing import Optional, List, Tuple
@@ -22,8 +23,9 @@
 
 from qiskit.circuit.library import RealAmplitudes
 from qiskit.exceptions import MissingOptionalLibraryError
+from qiskit.primitives import StatevectorSampler
+from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
 from qiskit.utils import optionals
-from qiskit.primitives import Sampler
 
 from qiskit_algorithms.optimizers import (
     ADAM,
@@ -409,7 +411,12 @@ def steps():
     def test_qnspsa(self):
         """Test QN-SPSA optimizer is serializable."""
         ansatz = RealAmplitudes(1)
-        fidelity = QNSPSA.get_fidelity(ansatz, sampler=Sampler())
+        fidelity = QNSPSA.get_fidelity(
+            ansatz,
+            sampler=StatevectorSampler(seed=123),
+            transpiler=generate_preset_pass_manager(optimization_level=1, seed_transpiler=42),
+            transpiler_options={"callable": lambda x: x},
+        )
         options = {
             "fidelity": fidelity,
             "maxiter": 100,
diff --git a/test/optimizers/test_optimizers_scikitquant.py b/test/optimizers/test_optimizers_scikitquant.py
index 37f257db..0ac2a206 100644
--- a/test/optimizers/test_optimizers_scikitquant.py
+++ b/test/optimizers/test_optimizers_scikitquant.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2020, 2023.
+# (C) Copyright IBM 2020, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -20,7 +20,7 @@
 import numpy
 from qiskit.circuit.library import RealAmplitudes
 from qiskit.exceptions import MissingOptionalLibraryError
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 from qiskit.quantum_info import SparsePauliOp
 
 from qiskit_algorithms.minimum_eigensolvers import VQE
@@ -49,7 +49,7 @@ def setUp(self):
     def _optimize(self, optimizer):
         """launch vqe"""
 
-        vqe = VQE(Estimator(), ansatz=RealAmplitudes(), optimizer=optimizer)
+        vqe = VQE(StatevectorEstimator(), ansatz=RealAmplitudes(), optimizer=optimizer)
         result = vqe.compute_minimum_eigenvalue(operator=self.qubit_op)
 
         self.assertAlmostEqual(result.eigenvalue.real, -1.857, places=1)
diff --git a/test/optimizers/test_spsa.py b/test/optimizers/test_spsa.py
index dca054e4..09bcfa2a 100644
--- a/test/optimizers/test_spsa.py
+++ b/test/optimizers/test_spsa.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2024.
+# (C) Copyright IBM 2021, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -18,7 +18,8 @@
 import numpy as np
 
 from qiskit.circuit.library import PauliTwoDesign
-from qiskit.primitives import Estimator, Sampler
+from qiskit.primitives import StatevectorEstimator, StatevectorSampler
+
 from qiskit.quantum_info import SparsePauliOp, Statevector
 
 from qiskit_algorithms.optimizers import SPSA, QNSPSA
@@ -57,7 +58,9 @@ def objective(x):
             settings["regularization"] = 0.01
             expected_nfev = settings["maxiter"] * 5 + 1
         elif method == "qnspsa":
-            settings["fidelity"] = QNSPSA.get_fidelity(circuit, sampler=Sampler())
+            settings["fidelity"] = QNSPSA.get_fidelity(
+                circuit, sampler=StatevectorSampler(seed=123)
+            )
             settings["regularization"] = 0.001
             settings["learning_rate"] = 0.05
             settings["perturbation"] = 0.05
@@ -204,7 +207,7 @@ def test_qnspsa_fidelity_primitives(self):
         initial_point = np.random.random(ansatz.num_parameters)
 
         with self.subTest(msg="pass as kwarg"):
-            fidelity = QNSPSA.get_fidelity(ansatz, sampler=Sampler())
+            fidelity = QNSPSA.get_fidelity(ansatz, sampler=StatevectorSampler(seed=123))
             result = fidelity(initial_point, initial_point)
 
             self.assertAlmostEqual(result[0], 1)
@@ -212,21 +215,21 @@ def test_qnspsa_fidelity_primitives(self):
     def test_qnspsa_max_evals_grouped(self):
         """Test using max_evals_grouped with QNSPSA."""
         circuit = PauliTwoDesign(3, reps=1, seed=1)
-        num_parameters = circuit.num_parameters
 
         obs = SparsePauliOp("ZZI")  # Z^Z^I
-        estimator = Estimator(options={"seed": 12})
+        estimator = StatevectorEstimator(seed=12)
 
         initial_point = np.array(
             [0.82311034, 0.02611798, 0.21077064, 0.61842177, 0.09828447, 0.62013131]
         )
 
         def objective(x):
-            x = np.reshape(x, (-1, num_parameters)).tolist()
-            n = len(x)
-            return estimator.run(n * [circuit], n * [obs], x).result().values.real
+            results = estimator.run([(circuit, obs, x)]).result()
+            return np.array([res.data.evs for res in results]).real.reshape(-1)
 
-        fidelity = QNSPSA.get_fidelity(circuit, sampler=Sampler())
+        fidelity = QNSPSA.get_fidelity(
+            circuit, sampler=StatevectorSampler(seed=12, default_shots=10_000)
+        )
         optimizer = QNSPSA(fidelity)
         optimizer.maxiter = 1
         optimizer.learning_rate = 0.05
diff --git a/test/state_fidelities/test_compute_uncompute.py b/test/state_fidelities/test_compute_uncompute.py
index a8cfe8f3..263c1240 100644
--- a/test/state_fidelities/test_compute_uncompute.py
+++ b/test/state_fidelities/test_compute_uncompute.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,14 +16,16 @@
 from test import QiskitAlgorithmsTestCase
 
 import numpy as np
-
+from ddt import ddt
 from qiskit.circuit import QuantumCircuit, ParameterVector
 from qiskit.circuit.library import RealAmplitudes
-from qiskit.primitives import Sampler
+from qiskit.primitives import StatevectorSampler
+from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
 
 from qiskit_algorithms.state_fidelities import ComputeUncompute
 
 
+@ddt
 class TestComputeUncompute(QiskitAlgorithmsTestCase):
     """Test Compute-Uncompute Fidelity class"""
 
@@ -49,7 +51,7 @@ def setUp(self):
         rx_rotation.h(1)
 
         self._circuit = [rx_rotations, ry_rotations, plus, zero, rx_rotation]
-        self._sampler = Sampler()
+        self._sampler = StatevectorSampler(seed=123, default_shots=10_000)
         self._left_params = np.array([[0, 0], [np.pi / 2, 0], [0, np.pi / 2], [np.pi, np.pi]])
         self._right_params = np.array([[0, 0], [0, 0], [np.pi / 2, 0], [0, 0]])
 
@@ -80,7 +82,7 @@ def test_local(self):
             job = fidelity.run(self._circuit[2], self._circuit[3])
             result = job.result()
             fidelities.append(result.fidelities[0])
-        np.testing.assert_allclose(fidelities, np.array([0.25, 0.5]), atol=1e-16)
+        np.testing.assert_allclose(fidelities, np.array([0.25, 0.5]), atol=1e-2, rtol=1e-2)
 
     def test_4param_pairs(self):
         """test for fidelity with four pairs of parameters"""
@@ -90,7 +92,9 @@ def test_4param_pairs(self):
             [self._circuit[0]] * n, [self._circuit[1]] * n, self._left_params, self._right_params
         )
         results = job.result()
-        np.testing.assert_allclose(results.fidelities, np.array([1.0, 0.5, 0.25, 0.0]), atol=1e-16)
+        np.testing.assert_allclose(
+            results.fidelities, np.array([1.0, 0.5, 0.25, 0.0]), atol=1e-2, rtol=1e-2
+        )
 
     def test_symmetry(self):
         """test for fidelity with the same circuit"""
@@ -111,11 +115,11 @@ def test_no_params(self):
         fidelity = ComputeUncompute(self._sampler)
         job = fidelity.run([self._circuit[2]], [self._circuit[3]])
         results = job.result()
-        np.testing.assert_allclose(results.fidelities, np.array([0.25]), atol=1e-16)
+        np.testing.assert_allclose(results.fidelities, np.array([0.25]), atol=1e-2, rtol=1e-2)
 
         job = fidelity.run([self._circuit[2]], [self._circuit[3]], [], [])
         results = job.result()
-        np.testing.assert_allclose(results.fidelities, np.array([0.25]), atol=1e-16)
+        np.testing.assert_allclose(results.fidelities, np.array([0.25]), atol=1e-2, rtol=1e-2)
 
     def test_left_param(self):
         """test for fidelity with only left parameters"""
@@ -125,7 +129,9 @@ def test_left_param(self):
             [self._circuit[1]] * n, [self._circuit[3]] * n, values_1=self._left_params
         )
         results = job.result()
-        np.testing.assert_allclose(results.fidelities, np.array([1.0, 0.5, 0.5, 0.0]), atol=1e-16)
+        np.testing.assert_allclose(
+            results.fidelities, np.array([1.0, 0.5, 0.5, 0.0]), atol=1e-2, rtol=1e-2
+        )
 
     def test_right_param(self):
         """test for fidelity with only right parameters"""
@@ -135,7 +141,9 @@ def test_right_param(self):
             [self._circuit[3]] * n, [self._circuit[1]] * n, values_2=self._left_params
         )
         results = job.result()
-        np.testing.assert_allclose(results.fidelities, np.array([1.0, 0.5, 0.5, 0.0]), atol=1e-16)
+        np.testing.assert_allclose(
+            results.fidelities, np.array([1.0, 0.5, 0.5, 0.0]), atol=1e-2, rtol=1e-2
+        )
 
     def test_not_set_circuits(self):
         """test for fidelity with no circuits."""
@@ -173,7 +181,9 @@ def test_asymmetric_params(self):
             [self._circuit[0]] * n, [self._circuit[4]] * n, self._left_params, right_params
         )
         result = job.result()
-        np.testing.assert_allclose(result.fidelities, np.array([0.5, 0.25, 0.25, 0.0]), atol=1e-16)
+        np.testing.assert_allclose(
+            result.fidelities, np.array([0.5, 0.25, 0.25, 0.0]), atol=1e-2, rtol=1e-2
+        )
 
     def test_input_format(self):
         """test for different input format variations"""
@@ -217,48 +227,68 @@ def test_input_measurements(self):
         result = job.result()
         np.testing.assert_allclose(result.fidelities, np.array([1.0]))
 
-    def test_options(self):
-        """Test fidelity's run options"""
-        sampler_shots = Sampler(options={"shots": 1024})
+    def test_shots(self):
+        """Test fidelity's run shots setting"""
+        sampler_shots = StatevectorSampler(default_shots=1024)
 
         with self.subTest("sampler"):
             # Only options in sampler
             fidelity = ComputeUncompute(sampler_shots)
-            options = fidelity.options
+            shots = fidelity.shots
             job = fidelity.run(self._circuit[2], self._circuit[3])
             result = job.result()
-            self.assertEqual(options.__dict__, {"shots": 1024})
-            self.assertEqual(result.options.__dict__, {"shots": 1024})
+            self.assertEqual(shots, None)
+            self.assertEqual(result.shots, 1024)
 
         with self.subTest("fidelity init"):
             # Fidelity default options override sampler
             # options and add new fields
-            fidelity = ComputeUncompute(sampler_shots, options={"shots": 2048, "dummy": 100})
-            options = fidelity.options
+            fidelity = ComputeUncompute(sampler_shots, shots=2048)
+            shots = fidelity.shots
             job = fidelity.run(self._circuit[2], self._circuit[3])
             result = job.result()
-            self.assertEqual(options.__dict__, {"shots": 2048, "dummy": 100})
-            self.assertEqual(result.options.__dict__, {"shots": 2048, "dummy": 100})
+            self.assertEqual(shots, 2048)
+            self.assertEqual(result.shots, 2048)
 
         with self.subTest("fidelity update"):
             # Update fidelity options
-            fidelity = ComputeUncompute(sampler_shots, options={"shots": 2048, "dummy": 100})
-            fidelity.update_default_options(shots=100)
-            options = fidelity.options
+            fidelity = ComputeUncompute(sampler_shots, shots=2048)
+            fidelity.shots = 100
+            shots = fidelity.shots
             job = fidelity.run(self._circuit[2], self._circuit[3])
             result = job.result()
-            self.assertEqual(options.__dict__, {"shots": 100, "dummy": 100})
-            self.assertEqual(result.options.__dict__, {"shots": 100, "dummy": 100})
+            self.assertEqual(shots, 100)
+            self.assertEqual(result.shots, 100)
 
         with self.subTest("fidelity run"):
             # Run options override fidelity options
-            fidelity = ComputeUncompute(sampler_shots, options={"shots": 2048, "dummy": 100})
-            job = fidelity.run(self._circuit[2], self._circuit[3], shots=50, dummy=None)
-            options = fidelity.options
+            fidelity = ComputeUncompute(sampler_shots, shots=2048)
+            job = fidelity.run(self._circuit[2], self._circuit[3], shots=50)
+            shots = fidelity.shots
             result = job.result()
             # Only default + sampler options. Not run.
-            self.assertEqual(options.__dict__, {"shots": 2048, "dummy": 100})
-            self.assertEqual(result.options.__dict__, {"shots": 50, "dummy": None})
+            self.assertEqual(shots, 2048)
+            self.assertEqual(result.shots, 50)
+
+    def test_transpiler(self):
+        """Test that the transpiler is called"""
+        pass_manager = generate_preset_pass_manager(optimization_level=1, seed_transpiler=42)
+        counts = [0]
+
+        def callback(**kwargs):
+            counts[0] = kwargs["count"]
+
+        # Test transpilation without options
+        fidelity = ComputeUncompute(StatevectorSampler(), transpiler=pass_manager)
+        fidelity._construct_circuits(QuantumCircuit(1), QuantumCircuit(1))
+
+        # Test transpiler is called using callback function
+        fidelity = ComputeUncompute(
+            StatevectorSampler(), transpiler=pass_manager, transpiler_options={"callback": callback}
+        )
+        fidelity._construct_circuits(QuantumCircuit(1), QuantumCircuit(1))
+
+        self.assertGreater(counts[0], 0)
 
 
 if __name__ == "__main__":
diff --git a/test/test_amplitude_estimators.py b/test/test_amplitude_estimators.py
index 0969ddb9..938d9025 100644
--- a/test/test_amplitude_estimators.py
+++ b/test/test_amplitude_estimators.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2024.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,10 +16,10 @@
 from test import QiskitAlgorithmsTestCase
 import numpy as np
 from ddt import ddt, idata, data, unpack
-from qiskit import QuantumRegister, QuantumCircuit
+from qiskit import QuantumRegister, QuantumCircuit, generate_preset_pass_manager
 from qiskit.circuit.library import QFT, GroverOperator
 from qiskit.quantum_info import Operator, Statevector
-from qiskit.primitives import Sampler
+from qiskit.primitives import StatevectorSampler
 
 from qiskit_algorithms import (
     AmplitudeEstimation,
@@ -92,55 +92,22 @@ class TestBernoulli(QiskitAlgorithmsTestCase):
     def setUp(self):
         super().setUp()
 
-        self._sampler = Sampler(options={"seed": 2})
-
-        def sampler_shots(shots=100):
-            return Sampler(options={"shots": shots, "seed": 2})
+        def sampler_shots(shots=10_000):
+            return StatevectorSampler(default_shots=shots, seed=42)
 
         self._sampler_shots = sampler_shots
 
     @idata(
         [
-            [0.2, AmplitudeEstimation(2), {"estimation": 0.5, "mle": 0.2}],
-            [0.49, AmplitudeEstimation(3), {"estimation": 0.5, "mle": 0.49}],
-            [0.2, MaximumLikelihoodAmplitudeEstimation([0, 1, 2]), {"estimation": 0.2}],
-            [0.49, MaximumLikelihoodAmplitudeEstimation(3), {"estimation": 0.49}],
-            [0.2, IterativeAmplitudeEstimation(0.1, 0.1), {"estimation": 0.2}],
-            [0.49, IterativeAmplitudeEstimation(0.001, 0.01), {"estimation": 0.49}],
-            [0.2, FasterAmplitudeEstimation(0.1, 3, rescale=False), {"estimation": 0.199}],
-            [0.12, FasterAmplitudeEstimation(0.1, 2, rescale=False), {"estimation": 0.12}],
-        ]
-    )
-    @unpack
-    def test_sampler(self, prob, qae, expect):
-        """sampler test"""
-        qae.sampler = self._sampler
-        problem = EstimationProblem(BernoulliStateIn(prob), 0, BernoulliGrover(prob))
-
-        result = qae.estimate(problem)
-        for key, value in expect.items():
-            self.assertAlmostEqual(
-                value, getattr(result, key), places=3, msg=f"estimate `{key}` failed"
-            )
-
-    @idata(
-        [
-            [0.2, 100, AmplitudeEstimation(4), {"estimation": 0.14644, "mle": 0.198783}],
-            [0.0, 1000, AmplitudeEstimation(2), {"estimation": 0.0, "mle": 0.0}],
-            [
-                0.2,
-                100,
-                MaximumLikelihoodAmplitudeEstimation([0, 1, 2, 4, 8]),
-                {"estimation": 0.200308},
-            ],
-            [0.8, 10, IterativeAmplitudeEstimation(0.1, 0.05), {"estimation": 0.811711}],
-            [0.2, 1000, FasterAmplitudeEstimation(0.1, 3, rescale=False), {"estimation": 0.198640}],
-            [
-                0.12,
-                100,
-                FasterAmplitudeEstimation(0.01, 3, rescale=False),
-                {"estimation": 0.120017},
-            ],
+            [0.2, 100_000, AmplitudeEstimation(2), {"estimation": 0.5, "mle": 0.2}],
+            [0.49, 1_000_000, AmplitudeEstimation(3), {"estimation": 0.5, "mle": 0.49}],
+            [0.2, 100_000, MaximumLikelihoodAmplitudeEstimation([0, 1, 2]), {"estimation": 0.2}],
+            [0.49, 100_000, MaximumLikelihoodAmplitudeEstimation(3), {"estimation": 0.49}],
+            [0.2, 1_000_000, IterativeAmplitudeEstimation(0.1, 0.1), {"estimation": 0.2}],
+            [0.49, 100_000, IterativeAmplitudeEstimation(0.001, 0.01), {"estimation": 0.49}],
+            # Number of shots for Sampler is not used in FasterAmplitudeEstimation
+            [0.2, 1, FasterAmplitudeEstimation(0.01, 5, rescale=False), {"estimation": 0.2}],
+            [0.12, 1, FasterAmplitudeEstimation(0.1, 3, rescale=False), {"estimation": 0.12}],
         ]
     )
     @unpack
@@ -302,41 +269,17 @@ class TestSineIntegral(QiskitAlgorithmsTestCase):
     def setUp(self):
         super().setUp()
 
-        self._sampler = Sampler(options={"seed": 123})
-
         def sampler_shots(shots=100):
-            return Sampler(options={"shots": shots, "seed": 7192})
+            return StatevectorSampler(default_shots=shots, seed=42)
 
         self._sampler_shots = sampler_shots
 
     @idata(
         [
-            [2, AmplitudeEstimation(2), {"estimation": 0.5, "mle": 0.2702}],
-            [4, MaximumLikelihoodAmplitudeEstimation(4), {"estimation": 0.2725}],
-            [3, IterativeAmplitudeEstimation(0.1, 0.1), {"estimation": 0.2721}],
-            [3, FasterAmplitudeEstimation(0.01, 1), {"estimation": 0.2792}],
-        ]
-    )
-    @unpack
-    def test_sampler(self, n, qae, expect):
-        """sampler end-to-end test"""
-        # construct factories for A and Q
-        # qae.state_preparation = SineIntegral(n)
-        qae.sampler = self._sampler
-        estimation_problem = EstimationProblem(SineIntegral(n), objective_qubits=[n])
-
-        result = qae.estimate(estimation_problem)
-        for key, value in expect.items():
-            self.assertAlmostEqual(
-                value, getattr(result, key), places=3, msg=f"estimate `{key}` failed"
-            )
-
-    @idata(
-        [
-            [4, 1000, AmplitudeEstimation(2), {"estimation": 0.5, "mle": 0.2636}],
-            [3, 10, MaximumLikelihoodAmplitudeEstimation(2), {"estimation": 0.2904}],
-            [3, 1000, IterativeAmplitudeEstimation(0.01, 0.01), {"estimation": 0.2706}],
-            [3, 1000, FasterAmplitudeEstimation(0.1, 4), {"estimation": 0.2764}],
+            [2, 1_000_000, AmplitudeEstimation(2), {"estimation": 0.5, "mle": 0.2702}],
+            [4, 100_000, MaximumLikelihoodAmplitudeEstimation(4), {"estimation": 0.2725}],
+            [3, 1_000_000, IterativeAmplitudeEstimation(0.1, 0.1), {"estimation": 0.2721}],
+            [3, 1, FasterAmplitudeEstimation(0.01, 6), {"estimation": 0.2721}],
         ]
     )
     @unpack
@@ -379,18 +322,6 @@ def test_confidence_intervals(self, qae, key, expect):
         """End-to-end test for all confidence intervals."""
         n = 3
 
-        estimation_problem = EstimationProblem(SineIntegral(n), objective_qubits=[n])
-        qae.sampler = self._sampler
-        result = qae.estimate(estimation_problem)
-
-        methods = ["lr", "fi", "oi"]  # short for likelihood_ratio, fisher, observed_fisher
-        alphas = [0.1, 0.00001, 0.9]  # alpha shouldn't matter in statevector
-        for alpha, method in zip(alphas, methods):
-            confint = qae.compute_confidence_interval(result, alpha, method, exact=True)
-            # confidence interval based on statevector should be empty, as we are sure of the result
-            self.assertAlmostEqual(confint[1] - confint[0], 0.0)
-            self.assertAlmostEqual(confint[0], getattr(result, key))
-
         # shots
         shots = 100
         alpha = 0.01
@@ -407,26 +338,16 @@ def test_confidence_intervals(self, qae, key, expect):
     def test_iqae_confidence_intervals(self):
         """End-to-end test for the IQAE confidence interval."""
         n = 3
-        # expected_confint = (0.1984050, 0.3511015)
+        # Careful, changes according to the seed
         expected_confint = (
-            0.263977,
-            0.3511015,
-        )  # change from qasm to shot-based statevector simulation
+            0.26,
+            0.32,
+        )
 
         estimation_problem = EstimationProblem(SineIntegral(n), objective_qubits=[n])
 
-        qae = IterativeAmplitudeEstimation(0.1, 0.01, sampler=self._sampler)
-
-        result = qae.estimate(estimation_problem)
-
-        confint = result.confidence_interval
-        # confidence interval based on statevector should be empty, as we are sure of the result
-        self.assertAlmostEqual(confint[1] - confint[0], 0.0)
-        self.assertAlmostEqual(confint[0], result.estimation)
-
-        # shots
         shots = 100
-        qae.sampler = self._sampler_shots(shots)
+        qae = IterativeAmplitudeEstimation(0.1, 0.01, sampler=self._sampler_shots(shots))
         result = qae.estimate(estimation_problem)
 
         confint = result.confidence_interval
@@ -434,6 +355,78 @@ def test_iqae_confidence_intervals(self):
         self.assertTrue(confint[0] <= result.estimation <= confint[1])
 
 
+@ddt
+class TestTranspiler(QiskitAlgorithmsTestCase):
+    """Tests to check that the transpiler is indeed called."""
+
+    def setUp(self):
+        super().setUp()
+
+        self.pm = generate_preset_pass_manager(optimization_level=1, seed_transpiler=42)
+        self.counts = [0]
+
+        # pylint: disable=unused-argument
+        def callback(**kwargs):
+            self.counts[0] += 1
+
+        self.callback = callback
+
+        circuit = QuantumCircuit(1)
+        self.problem = EstimationProblem(
+            circuit, objective_qubits=[0], is_good_state=lambda x: True
+        )
+
+    @idata(
+        [
+            [AmplitudeEstimation, {"num_eval_qubits": 1}],
+            [IterativeAmplitudeEstimation, {"epsilon_target": 0.1, "alpha": 0.1}],
+        ]
+    )
+    @unpack
+    def test_transpiler_ae_iae(self, qae_class, kwargs):
+        """Test that the transpiler is called on AE and IAE"""
+        # Test transpilation without setting options
+        qae = qae_class(transpiler=self.pm, **kwargs)
+        qae.construct_circuit(self.problem)
+
+        # Test transpiler is called using callback function
+        qae = qae_class(
+            transpiler=self.pm, transpiler_options={"callback": self.callback}, **kwargs
+        )
+        qae.construct_circuit(self.problem)
+
+        self.assertGreater(self.counts[0], 0)
+
+    @unittest.skip("Won't pass until Qiskit/qiskit#14250 is fixed")
+    def test_transpiler_mlae(self):
+        """Test that the transpiler is called on MLAE"""
+        # Test transpilation without setting options
+        mlae = MaximumLikelihoodAmplitudeEstimation([0, 1], transpiler=self.pm)
+        mlae.construct_circuits(self.problem)
+
+        # Test transpiler is called using callback function
+        mlae = MaximumLikelihoodAmplitudeEstimation(
+            [0, 1], transpiler=self.pm, transpiler_options={"callback": self.callback}
+        )
+        mlae.construct_circuits(self.problem)
+
+        self.assertGreater(self.counts[0], 0)
+
+    def test_transpiler_fae(self):
+        """Test that the transpiler is called on FAE"""
+        # Test transpilation without setting options
+        fae = FasterAmplitudeEstimation(0.1, 1, transpiler=self.pm)
+        fae.construct_circuit(self.problem, k=1)
+
+        # Test transpiler is called using callback function
+        fae = FasterAmplitudeEstimation(
+            0.1, 1, transpiler=self.pm, transpiler_options={"callback": self.callback}
+        )
+        fae.construct_circuit(self.problem, k=1)
+
+        self.assertGreater(self.counts[0], 0)
+
+
 class TestAmplitudeEstimation(QiskitAlgorithmsTestCase):
     """Specific tests for canonical AE."""
 
@@ -442,7 +435,7 @@ def test_warns_if_good_state_set(self):
         circuit = QuantumCircuit(1)
         problem = EstimationProblem(circuit, objective_qubits=[0], is_good_state=lambda x: True)
 
-        qae = AmplitudeEstimation(num_eval_qubits=1, sampler=Sampler())
+        qae = AmplitudeEstimation(num_eval_qubits=1, sampler=StatevectorSampler())
 
         with self.assertWarns(Warning):
             _ = qae.estimate(problem)
@@ -453,7 +446,7 @@ class TestFasterAmplitudeEstimation(QiskitAlgorithmsTestCase):
 
     def setUp(self):
         super().setUp()
-        self._sampler = Sampler(options={"seed": 2})
+        self._sampler = StatevectorSampler(default_shots=10_000, seed=2)
 
     def test_rescaling(self):
         """Test the rescaling."""
diff --git a/test/test_grover.py b/test/test_grover.py
index 966c69a6..c74f240e 100644
--- a/test/test_grover.py
+++ b/test/test_grover.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2024.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -12,20 +12,20 @@
 
 """Test Grover's algorithm."""
 
-import itertools
 import unittest
+from itertools import product
 from test import QiskitAlgorithmsTestCase
 
 import numpy as np
-from ddt import data, ddt, idata, unpack
-
+from ddt import data, ddt
 from qiskit import QuantumCircuit
 from qiskit.circuit.library import GroverOperator, PhaseOracle
-from qiskit.primitives import Sampler
+from qiskit.primitives import StatevectorSampler
 from qiskit.quantum_info import Operator, Statevector
-from qiskit.utils.optionals import HAS_TWEEDLEDUM
+from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
 
 from qiskit_algorithms import AmplificationProblem, Grover
+from qiskit_algorithms.utils.optionals import CAN_USE_PHASE_ORACLE
 
 
 @ddt
@@ -72,9 +72,7 @@ def is_good_state(bitstr):
                 # same as ``bitstr in ['01', '11']``
                 return bitstr[1] == "1"
 
-        possible_states = [
-            "".join(list(map(str, item))) for item in itertools.product([0, 1], repeat=2)
-        ]
+        possible_states = ["".join(list(map(str, item))) for item in product([0, 1], repeat=2)]
 
         oracle = QuantumCircuit(2)
         problem = AmplificationProblem(oracle, is_good_state=is_good_state)
@@ -91,50 +89,51 @@ class TestGrover(QiskitAlgorithmsTestCase):
 
     def setUp(self):
         super().setUp()
-        self._sampler = Sampler()
-        self._sampler_with_shots = Sampler(options={"shots": 1024, "seed": 123})
+        self._sampler = StatevectorSampler(seed=123)
 
-    @unittest.skipUnless(HAS_TWEEDLEDUM, "tweedledum required for this test")
-    @data("ideal", "shots")
-    def test_implicit_phase_oracle_is_good_state(self, use_sampler):
+    @unittest.skipUnless(
+        CAN_USE_PHASE_ORACLE, "tweedledum or qiskit >= 2.0.0 required for this test"
+    )
+    def test_implicit_phase_oracle_is_good_state(self):
         """Test implicit default for is_good_state with PhaseOracle."""
-        grover = self._prepare_grover(use_sampler)
+        grover = self._prepare_grover()
         oracle = PhaseOracle("x & y")
         problem = AmplificationProblem(oracle)
         result = grover.amplify(problem)
         self.assertEqual(result.top_measurement, "11")
 
-    @idata(itertools.product(["ideal", "shots"], [[1, 2, 3], None, 2]))
-    @unpack
-    def test_iterations_with_good_state(self, use_sampler, iterations):
+    @data([1, 2, 3], None, 2)
+    def test_iterations_with_good_state(self, iterations):
         """Test the algorithm with different iteration types and with good state"""
-        grover = self._prepare_grover(use_sampler, iterations)
+        grover = self._prepare_grover(iterations)
         problem = AmplificationProblem(Statevector.from_label("111"), is_good_state=["111"])
         result = grover.amplify(problem)
         self.assertEqual(result.top_measurement, "111")
 
-    @idata(itertools.product(["shots"], [[1, 2, 3], None, 2]))
-    @unpack
-    def test_iterations_with_good_state_sample_from_iterations(self, use_sampler, iterations):
+    @unittest.skip(
+        "Skipped until "
+        "https://github.com/qiskit-community/qiskit-algorithms/issues/136#issuecomment-2291169158 is "
+        "resolved"
+    )
+    @data([1, 2, 3], None, 2)
+    def test_iterations_with_good_state_sample_from_iterations(self, iterations):
         """Test the algorithm with different iteration types and with good state"""
-        grover = self._prepare_grover(use_sampler, iterations, sample_from_iterations=True)
+        grover = self._prepare_grover(iterations, sample_from_iterations=True)
         problem = AmplificationProblem(Statevector.from_label("111"), is_good_state=["111"])
         result = grover.amplify(problem)
         self.assertEqual(result.top_measurement, "111")
 
-    @data("ideal", "shots")
-    def test_fixed_iterations_without_good_state(self, use_sampler):
+    def test_fixed_iterations_without_good_state(self):
         """Test the algorithm with iterations as an int and without good state"""
-        grover = self._prepare_grover(use_sampler, iterations=2)
+        grover = self._prepare_grover(iterations=2)
         problem = AmplificationProblem(Statevector.from_label("111"))
         result = grover.amplify(problem)
         self.assertEqual(result.top_measurement, "111")
 
-    @idata(itertools.product(["ideal", "shots"], [[1, 2, 3], None]))
-    @unpack
-    def test_iterations_without_good_state(self, use_sampler, iterations):
+    @data([1, 2, 3], None)
+    def test_iterations_without_good_state(self, iterations):
         """Test the correct error is thrown for none/list of iterations and without good state"""
-        grover = self._prepare_grover(use_sampler, iterations=iterations)
+        grover = self._prepare_grover(iterations=iterations)
         problem = AmplificationProblem(Statevector.from_label("111"))
 
         with self.assertRaisesRegex(
@@ -142,8 +141,7 @@ def test_iterations_without_good_state(self, use_sampler, iterations):
         ):
             grover.amplify(problem)
 
-    @data("ideal", "shots")
-    def test_iterator(self, use_sampler):
+    def test_iterator(self):
         """Test running the algorithm on an iterator."""
 
         # step-function iterator
@@ -155,63 +153,57 @@ def iterator():
                 if count % wait == 0:
                     value += 1
 
-        grover = self._prepare_grover(use_sampler, iterations=iterator())
+        grover = self._prepare_grover(iterations=iterator())
         problem = AmplificationProblem(Statevector.from_label("111"), is_good_state=["111"])
         result = grover.amplify(problem)
         self.assertEqual(result.top_measurement, "111")
 
-    @data("ideal", "shots")
-    def test_growth_rate(self, use_sampler):
+    def test_growth_rate(self):
         """Test running the algorithm on a growth rate"""
-        grover = self._prepare_grover(use_sampler, growth_rate=8 / 7)
+        grover = self._prepare_grover(growth_rate=8 / 7)
         problem = AmplificationProblem(Statevector.from_label("111"), is_good_state=["111"])
         result = grover.amplify(problem)
         self.assertEqual(result.top_measurement, "111")
 
-    @data("ideal", "shots")
-    def test_max_num_iterations(self, use_sampler):
+    def test_max_num_iterations(self):
         """Test the iteration stops when the maximum number of iterations is reached."""
 
         def zero():
             while True:
                 yield 0
 
-        grover = self._prepare_grover(use_sampler, iterations=zero())
+        grover = self._prepare_grover(iterations=zero())
         n = 5
         problem = AmplificationProblem(Statevector.from_label("1" * n), is_good_state=["1" * n])
         result = grover.amplify(problem)
         self.assertEqual(len(result.iterations), 2**n)
 
-    @data("ideal", "shots")
-    def test_max_power(self, use_sampler):
+    def test_max_power(self):
         """Test the iteration stops when the maximum power is reached."""
         lam = 10.0
-        grover = self._prepare_grover(use_sampler, growth_rate=lam)
+        grover = self._prepare_grover(growth_rate=lam)
         problem = AmplificationProblem(Statevector.from_label("111"), is_good_state=["111"])
         result = grover.amplify(problem)
         self.assertEqual(len(result.iterations), 0)
 
-    @data("ideal", "shots")
-    def test_run_circuit_oracle(self, use_sampler):
+    def test_run_circuit_oracle(self):
         """Test execution with a quantum circuit oracle"""
         oracle = QuantumCircuit(2)
         oracle.cz(0, 1)
         problem = AmplificationProblem(oracle, is_good_state=["11"])
-        grover = self._prepare_grover(use_sampler)
+        grover = self._prepare_grover()
         result = grover.amplify(problem)
         self.assertIn(result.top_measurement, ["11"])
 
-    @data("ideal", "shots")
-    def test_run_state_vector_oracle(self, use_sampler):
+    def test_run_state_vector_oracle(self):
         """Test execution with a state vector oracle"""
         mark_state = Statevector.from_label("11")
         problem = AmplificationProblem(mark_state, is_good_state=["11"])
-        grover = self._prepare_grover(use_sampler)
+        grover = self._prepare_grover()
         result = grover.amplify(problem)
         self.assertIn(result.top_measurement, ["11"])
 
-    @data("ideal", "shots")
-    def test_run_custom_grover_operator(self, use_sampler):
+    def test_run_custom_grover_operator(self):
         """Test execution with a grover operator oracle"""
         oracle = QuantumCircuit(2)
         oracle.cz(0, 1)
@@ -219,7 +211,7 @@ def test_run_custom_grover_operator(self, use_sampler):
         problem = AmplificationProblem(
             oracle=oracle, grover_operator=grover_op, is_good_state=["11"]
         )
-        grover = self._prepare_grover(use_sampler)
+        grover = self._prepare_grover()
         result = grover.amplify(problem)
         self.assertIn(result.top_measurement, ["11"])
 
@@ -230,7 +222,7 @@ def test_optimal_num_iterations(self):
             amplitude = np.sqrt(num_solutions / 2**num_qubits)
             expected = round(np.arccos(amplitude) / (2 * np.arcsin(amplitude)))
             actual = Grover.optimal_num_iterations(num_solutions, num_qubits)
-        self.assertEqual(actual, expected)
+            self.assertEqual(actual, expected)
 
     def test_construct_circuit(self):
         """Test construct_circuit"""
@@ -247,14 +239,13 @@ def test_construct_circuit(self):
 
         self.assertTrue(Operator(constructed).equiv(Operator(expected)))
 
-    @data("ideal", "shots")
-    def test_circuit_result(self, use_sampler):
+    def test_circuit_result(self):
         """Test circuit_result"""
         oracle = QuantumCircuit(2)
         oracle.cz(0, 1)
         # is_good_state=['00'] is intentionally selected to obtain a list of results
         problem = AmplificationProblem(oracle, is_good_state=["00"])
-        grover = self._prepare_grover(use_sampler, iterations=[1, 2, 3, 4])
+        grover = self._prepare_grover(iterations=[1, 2, 3, 4])
 
         result = grover.amplify(problem)
 
@@ -267,23 +258,23 @@ def test_circuit_result(self, use_sampler):
                 self.assertTupleEqual(keys, ("00", "01", "10", "11"))
                 np.testing.assert_allclose(values, [0.25, 0.25, 0.25, 0.25], atol=0.2)
 
-    @data("ideal", "shots")
-    def test_max_probability(self, use_sampler):
+    def test_max_probability(self):
         """Test max_probability"""
         oracle = QuantumCircuit(2)
         oracle.cz(0, 1)
         problem = AmplificationProblem(oracle, is_good_state=["11"])
-        grover = self._prepare_grover(use_sampler)
+        grover = self._prepare_grover()
         result = grover.amplify(problem)
         self.assertAlmostEqual(result.max_probability, 1.0)
 
-    @unittest.skipUnless(HAS_TWEEDLEDUM, "tweedledum required for this test")
-    @data("ideal", "shots")
-    def test_oracle_evaluation(self, use_sampler):
+    @unittest.skipUnless(
+        CAN_USE_PHASE_ORACLE, "tweedledum or qiskit >= 2.0.0 required for this test"
+    )
+    def test_oracle_evaluation(self):
         """Test oracle_evaluation for PhaseOracle"""
         oracle = PhaseOracle("x1 & x2 & (not x3)")
         problem = AmplificationProblem(oracle, is_good_state=oracle.evaluate_bitstring)
-        grover = self._prepare_grover(use_sampler)
+        grover = self._prepare_grover()
         result = grover.amplify(problem)
         self.assertTrue(result.oracle_evaluation)
         self.assertEqual("011", result.top_measurement)
@@ -294,27 +285,49 @@ def test_sampler_setter(self):
         grover.sampler = self._sampler
         self.assertEqual(grover.sampler, self._sampler)
 
+    def test_transpiler(self):
+        """Test that the transpiler is called"""
+        pass_manager = generate_preset_pass_manager(optimization_level=1, seed_transpiler=42)
+        counts = [0]
+
+        def callback(**kwargs):
+            counts[0] = kwargs["count"]
+
+        oracle = QuantumCircuit(2)
+        oracle.cz(0, 1)
+        # is_good_state=['00'] is intentionally selected to obtain a list of results
+        problem = AmplificationProblem(oracle)
+
+        # Test transpilation without setting options
+        Grover(
+            iterations=1,
+            sampler=StatevectorSampler(seed=42),
+            transpiler=pass_manager,
+        ).amplify(problem)
+
+        # Test that transpiler is called using callback function
+        Grover(
+            iterations=1,
+            sampler=StatevectorSampler(seed=42),
+            transpiler=pass_manager,
+            transpiler_options={"callback": callback},
+        ).amplify(problem)
+
+        self.assertGreater(counts[0], 0)
+
     def _prepare_grover(
-        self, use_sampler, iterations=None, growth_rate=None, sample_from_iterations=False
+        self,
+        iterations=None,
+        growth_rate=None,
+        sample_from_iterations=False,
     ):
         """Prepare Grover instance for test"""
-        if use_sampler == "ideal":
-            grover = Grover(
-                sampler=self._sampler,
-                iterations=iterations,
-                growth_rate=growth_rate,
-                sample_from_iterations=sample_from_iterations,
-            )
-        elif use_sampler == "shots":
-            grover = Grover(
-                sampler=self._sampler_with_shots,
-                iterations=iterations,
-                growth_rate=growth_rate,
-                sample_from_iterations=sample_from_iterations,
-            )
-        else:
-            raise RuntimeError("Unexpected `use_sampler` value {use_sampler}")
-        return grover
+        return Grover(
+            sampler=self._sampler,
+            iterations=iterations,
+            growth_rate=growth_rate,
+            sample_from_iterations=sample_from_iterations,
+        )
 
 
 if __name__ == "__main__":
diff --git a/test/test_phase_estimator.py b/test/test_phase_estimator.py
index d2edad1a..386f1e8b 100644
--- a/test/test_phase_estimator.py
+++ b/test/test_phase_estimator.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2024.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -11,22 +11,23 @@
 # that they have been altered from the originals.
 
 """Test phase estimation"""
-
 import unittest
 from test import QiskitAlgorithmsTestCase
-from ddt import ddt, data, unpack
+
 import numpy as np
-from qiskit.circuit.library import ZGate, XGate, HGate, IGate
-from qiskit.quantum_info import Pauli, SparsePauliOp, Statevector, Operator
-from qiskit.synthesis import MatrixExponential, SuzukiTrotter
-from qiskit.primitives import Sampler
+from ddt import ddt, data, unpack
 from qiskit import QuantumCircuit
+from qiskit.circuit.library import HGate, XGate, IGate, ZGate
+from qiskit.primitives import StatevectorSampler, BaseSamplerV2
+from qiskit.quantum_info import SparsePauliOp, Pauli, Statevector, Operator
+from qiskit.synthesis import MatrixExponential, SuzukiTrotter
+from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
 
-from qiskit_algorithms import PhaseEstimationScale
-from qiskit_algorithms.phase_estimators import (
-    PhaseEstimation,
+from qiskit_algorithms import (
     HamiltonianPhaseEstimation,
     IterativePhaseEstimation,
+    PhaseEstimation,
+    PhaseEstimationScale,
 )
 
 
@@ -45,9 +46,10 @@ def hamiltonian_pe_sampler(
         bound=None,
     ):
         """Run HamiltonianPhaseEstimation and return result with all phases."""
-        sampler = Sampler()
+        sampler = StatevectorSampler(default_shots=10_000, seed=42)
         phase_est = HamiltonianPhaseEstimation(
-            num_evaluation_qubits=num_evaluation_qubits, sampler=sampler
+            num_evaluation_qubits=num_evaluation_qubits,
+            sampler=sampler,
         )
         result = phase_est.estimate(
             hamiltonian=hamiltonian,
@@ -102,6 +104,24 @@ def test_single_pauli_op_sampler(self):
         with self.subTest("Second eigenvalue"):
             self.assertAlmostEqual(eigv, -0.98, delta=0.01)
 
+    def test_single_pauli_op_sampler_with_transpiler(self):
+        """Check that the transpilation does happen"""
+        pass_manager = generate_preset_pass_manager(optimization_level=1, seed_transpiler=42)
+        counts = [0]
+
+        def callback(**kwargs):
+            counts[0] = kwargs["count"]
+
+        phase_est = HamiltonianPhaseEstimation(
+            1,
+            StatevectorSampler(),
+            transpiler=pass_manager,
+            transpiler_options={"callback": callback},
+        )
+        phase_est.estimate(hamiltonian=SparsePauliOp(Pauli("Z")))
+
+        self.assertGreater(counts[0], 0)
+
     @data(
         (Statevector(QuantumCircuit(2).compose(IGate()).compose(HGate()))),
         (QuantumCircuit(2).compose(IGate()).compose(HGate())),
@@ -164,11 +184,11 @@ def one_phase_sampler(
         """Run phase estimation with operator, eigenvalue pair `unitary_circuit`,
         `state_preparation`. Return the estimated phase as a value in :math:`[0,1)`.
         """
+
         if shots is not None:
-            options = {"shots": shots}
+            sampler = StatevectorSampler(default_shots=shots, seed=42)
         else:
-            options = {}
-        sampler = Sampler(options=options)
+            sampler = StatevectorSampler(seed=42)
         if phase_estimator is None:
             phase_estimator = IterativePhaseEstimation
         if phase_estimator == IterativePhaseEstimation:
@@ -211,11 +231,7 @@ def test_qpe_Z_sampler(self, state_preparation, expected_phase, shots, phase_est
     def test_qpe_X_plus_minus_sampler(self, state_preparation, expected_phase, phase_estimator):
         """eigenproblem X, (|+>, |->)"""
         unitary_circuit = QuantumCircuit(1).compose(XGate())
-        phase = self.one_phase_sampler(
-            unitary_circuit,
-            state_preparation,
-            phase_estimator,
-        )
+        phase = self.one_phase_sampler(unitary_circuit, state_preparation, phase_estimator)
         self.assertEqual(phase, expected_phase)
 
     @data(
@@ -230,11 +246,7 @@ def test_qpe_RZ_sampler(self, state_preparation, expected_phase, phase_estimator
         alpha = np.pi / 2
         unitary_circuit = QuantumCircuit(1)
         unitary_circuit.rz(alpha, 0)
-        phase = self.one_phase_sampler(
-            unitary_circuit,
-            state_preparation,
-            phase_estimator,
-        )
+        phase = self.one_phase_sampler(unitary_circuit, state_preparation, phase_estimator)
         self.assertEqual(phase, expected_phase)
 
     @data(
@@ -265,11 +277,7 @@ def test_qpe_two_qubit_unitary(self, state_preparation, expected_phase, phase_es
         unitary_circuit = QuantumCircuit(2)
         unitary_circuit.t(0)
         unitary_circuit.t(1)
-        phase = self.one_phase_sampler(
-            unitary_circuit,
-            state_preparation,
-            phase_estimator,
-        )
+        phase = self.one_phase_sampler(unitary_circuit, state_preparation, phase_estimator)
         self.assertEqual(phase, expected_phase)
 
     def test_check_num_iterations_sampler(self):
@@ -286,11 +294,59 @@ def test_phase_estimation_scale_from_operator(self):
         scale = PhaseEstimationScale.from_pauli_sum(op)
         self.assertEqual(scale._bound, 4.0)
 
+    @data(PhaseEstimation, IterativePhaseEstimation)
+    def test_transpiler(self, phase_estimator):
+        """Test that the transpiler is called"""
+        pass_manager = generate_preset_pass_manager(optimization_level=1, seed_transpiler=42)
+        counts = [0]
+
+        def callback(**kwargs):
+            counts[0] = kwargs["count"]
+
+        # Test transpiler without options
+        if phase_estimator == IterativePhaseEstimation:
+            p_est = IterativePhaseEstimation(
+                num_iterations=6,
+                sampler=StatevectorSampler(),
+                transpiler=pass_manager,
+            )
+        elif phase_estimator == PhaseEstimation:
+            p_est = PhaseEstimation(
+                num_evaluation_qubits=6,
+                sampler=StatevectorSampler(),
+                transpiler=pass_manager,
+            )
+        else:
+            raise ValueError("Unrecognized phase_estimator")
+
+        p_est.estimate(unitary=QuantumCircuit(1), state_preparation=None)
+
+        # Test transpiler is called using callback function
+        if phase_estimator == IterativePhaseEstimation:
+            p_est = IterativePhaseEstimation(
+                num_iterations=6,
+                sampler=StatevectorSampler(),
+                transpiler=pass_manager,
+                transpiler_options={"callback": callback},
+            )
+        elif phase_estimator == PhaseEstimation:
+            p_est = PhaseEstimation(
+                num_evaluation_qubits=6,
+                sampler=StatevectorSampler(),
+                transpiler=pass_manager,
+                transpiler_options={"callback": callback},
+            )
+        else:
+            raise ValueError("Unrecognized phase_estimator")
+
+        p_est.estimate(unitary=QuantumCircuit(1), state_preparation=None)
+        self.assertGreater(counts[0], 0)
+
     # pylint: disable=too-many-positional-arguments
     def phase_estimation_sampler(
         self,
         unitary_circuit,
-        sampler: Sampler,
+        sampler: BaseSamplerV2,
         state_preparation=None,
         num_evaluation_qubits=6,
         construct_circuit=False,
@@ -313,7 +369,7 @@ def test_qpe_Zplus_sampler(self, construct_circuit):
         """superposition eigenproblem Z, |+>"""
         unitary_circuit = QuantumCircuit(1).compose(ZGate())
         state_preparation = QuantumCircuit(1).compose(HGate())  # prepare |+>
-        sampler = Sampler()
+        sampler = StatevectorSampler(default_shots=10_000, seed=42)
         result = self.phase_estimation_sampler(
             unitary_circuit,
             sampler,
@@ -326,7 +382,7 @@ def test_qpe_Zplus_sampler(self, construct_circuit):
             self.assertEqual(list(phases.keys()), [0.0, 0.5])
 
         with self.subTest("test phases has correct probabilities"):
-            np.testing.assert_allclose(list(phases.values()), [0.5, 0.5])
+            np.testing.assert_allclose(list(phases.values()), [0.5, 0.5], atol=1e-2, rtol=1e-2)
 
         with self.subTest("test bitstring representation"):
             phases = result.filter_phases(1e-15, as_float=False)
diff --git a/test/time_evolvers/test_pvqd.py b/test/time_evolvers/test_pvqd.py
index 740ac69f..4ff1e40b 100644
--- a/test/time_evolvers/test_pvqd.py
+++ b/test/time_evolvers/test_pvqd.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2024.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -20,7 +20,7 @@
 
 from qiskit.circuit import Gate, Parameter, QuantumCircuit
 from qiskit.circuit.library import EfficientSU2
-from qiskit.primitives import Estimator, Sampler
+from qiskit.primitives import StatevectorEstimator, StatevectorSampler
 from qiskit.quantum_info import Pauli, SparsePauliOp
 
 from qiskit_algorithms import AlgorithmError
@@ -81,8 +81,8 @@ def test_pvqd(self, hamiltonian_type, gradient, num_timesteps):
         else:
             optimizer = L_BFGS_B(maxiter=1)
 
-        sampler = Sampler()
-        estimator = Estimator()
+        sampler = StatevectorSampler()
+        estimator = StatevectorEstimator()
         fidelity_primitive = ComputeUncompute(sampler)
 
         # run pVQD keeping track of the energy and the magnetization
@@ -110,8 +110,8 @@ def test_pvqd(self, hamiltonian_type, gradient, num_timesteps):
 
     def test_step(self):
         """Test calling the step method directly."""
-        sampler = Sampler()
-        estimator = Estimator()
+        sampler = StatevectorSampler()
+        estimator = StatevectorEstimator()
         fidelity_primitive = ComputeUncompute(sampler)
         pvqd = PVQD(
             fidelity_primitive,
@@ -137,8 +137,8 @@ def test_step(self):
     def test_get_loss(self):
         """Test getting the loss function directly."""
 
-        sampler = Sampler()
-        estimator = Estimator()
+        sampler = StatevectorSampler()
+        estimator = StatevectorEstimator()
         fidelity_primitive = ComputeUncompute(sampler)
 
         pvqd = PVQD(
@@ -165,8 +165,8 @@ def test_get_loss(self):
 
     def test_invalid_num_timestep(self):
         """Test raises if the num_timestep is not positive."""
-        sampler = Sampler()
-        estimator = Estimator()
+        sampler = StatevectorSampler()
+        estimator = StatevectorEstimator()
         fidelity_primitive = ComputeUncompute(sampler)
         pvqd = PVQD(
             fidelity_primitive,
@@ -186,8 +186,8 @@ def test_invalid_num_timestep(self):
     def test_initial_guess_and_observables(self):
         """Test doing no optimizations stays at initial guess."""
         initial_guess = np.zeros(self.ansatz.num_parameters)
-        sampler = Sampler()
-        estimator = Estimator()
+        sampler = StatevectorSampler()
+        estimator = StatevectorEstimator()
         fidelity_primitive = ComputeUncompute(sampler)
 
         pvqd = PVQD(
@@ -212,7 +212,7 @@ def test_initial_guess_and_observables(self):
     def test_zero_parameters(self):
         """Test passing an ansatz with zero parameters raises an error."""
         problem = TimeEvolutionProblem(self.hamiltonian, time=0.02)
-        sampler = Sampler()
+        sampler = StatevectorSampler()
         fidelity_primitive = ComputeUncompute(sampler)
 
         pvqd = PVQD(
@@ -236,7 +236,7 @@ def test_initial_state_raises(self):
             initial_state=initial_state,
         )
 
-        sampler = Sampler()
+        sampler = StatevectorSampler()
         fidelity_primitive = ComputeUncompute(sampler)
 
         pvqd = PVQD(
@@ -256,7 +256,7 @@ def test_aux_ops_raises(self):
             self.hamiltonian, time=0.02, aux_operators=[self.hamiltonian, self.observable]
         )
 
-        sampler = Sampler()
+        sampler = StatevectorSampler()
         fidelity_primitive = ComputeUncompute(sampler)
 
         pvqd = PVQD(
@@ -310,8 +310,8 @@ def test_gradient_supported(self):
         info = {"has_gradient": None}
         optimizer = partial(gradient_supplied, info=info)
 
-        sampler = Sampler()
-        estimator = Estimator()
+        sampler = StatevectorSampler()
+        estimator = StatevectorEstimator()
         fidelity_primitive = ComputeUncompute(sampler)
 
         pvqd = PVQD(
diff --git a/test/time_evolvers/test_trotter_qrte.py b/test/time_evolvers/test_trotter_qrte.py
index 3dda5710..ecaaeb57 100644
--- a/test/time_evolvers/test_trotter_qrte.py
+++ b/test/time_evolvers/test_trotter_qrte.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2024.
+# (C) Copyright IBM 2021, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -14,6 +14,7 @@
 
 import unittest
 from test import QiskitAlgorithmsTestCase
+
 from ddt import ddt, data, unpack
 import numpy as np
 from scipy.linalg import expm
@@ -23,8 +24,9 @@
 from qiskit.circuit.library import ZGate
 from qiskit.quantum_info import Statevector, Pauli, SparsePauliOp
 from qiskit.circuit import Parameter
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 from qiskit.synthesis import SuzukiTrotter, QDrift
+from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
 
 from qiskit_algorithms import TimeEvolutionProblem, TrotterQRTE
 from qiskit_algorithms.utils import algorithm_globals
@@ -80,7 +82,7 @@ def test_trotter_qrte_trotter(self, operator, t_param):
         evolution_problem = TimeEvolutionProblem(
             operator, time, initial_state, aux_ops, t_param=t_param
         )
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
 
         expected_psi, expected_observables_result = self._get_expected_trotter_qrte(
             operator,
@@ -239,6 +241,36 @@ def test_barriers(self, insert_barrier):
             expected_circuit.decompose(reps=3), evolution_result.evolved_state.decompose(reps=5)
         )
 
+    def test_transpiler(self):
+        """Test that the transpiler is called"""
+        pass_manager = generate_preset_pass_manager(optimization_level=1, seed_transpiler=42)
+        counts = [0]
+
+        def callback(**kwargs):
+            counts[0] = kwargs["count"]
+
+        operator = SparsePauliOp([Pauli("X"), Pauli("Z")])
+        initial_state = QuantumCircuit(1)
+        time = 1
+        evolution_problem = TimeEvolutionProblem(operator, time, initial_state)
+
+        # Test transpilation without options
+        trotter_qrte = TrotterQRTE(
+            estimator=StatevectorEstimator(),
+            transpiler=pass_manager,
+        )
+        trotter_qrte.evolve(evolution_problem)
+
+        # Test transpiler is called using callback function
+        trotter_qrte = TrotterQRTE(
+            estimator=StatevectorEstimator(),
+            transpiler=pass_manager,
+            transpiler_options={"callback": callback},
+        )
+        trotter_qrte.evolve(evolution_problem)
+
+        self.assertGreater(counts[0], 0)
+
     # pylint: disable=too-many-positional-arguments
     @staticmethod
     def _run_error_test(initial_state, operator, aux_ops, estimator, t_param, param_value_dict):
diff --git a/test/time_evolvers/variational/test_var_qite.py b/test/time_evolvers/variational/test_var_qite.py
index 8ec64f54..109cb6f4 100644
--- a/test/time_evolvers/variational/test_var_qite.py
+++ b/test/time_evolvers/variational/test_var_qite.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023, 2024.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -19,16 +19,14 @@
 
 from qiskit import QuantumCircuit
 from qiskit.circuit import Parameter
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 from qiskit.quantum_info import SparsePauliOp, Pauli
 from qiskit.circuit.library import EfficientSU2
 from qiskit.quantum_info import Statevector
 
 from qiskit_algorithms.gradients import LinCombQGT, LinCombEstimatorGradient
 from qiskit_algorithms import TimeEvolutionProblem, VarQITE
-from qiskit_algorithms.time_evolvers.variational import (
-    ImaginaryMcLachlanPrinciple,
-)
+from qiskit_algorithms.time_evolvers.variational import ImaginaryMcLachlanPrinciple
 from qiskit_algorithms.utils import algorithm_globals
 
 
@@ -80,19 +78,23 @@ def test_run_d_1_with_aux_ops(self):
         ]
 
         thetas_expected_shots = [
-            0.9392668013702317,
-            1.8756706968454864,
-            2.6915067128662398,
-            2.655420131540562,
-            2.174687086978046,
-            1.6997059390911056,
-            1.8056912289547045,
-            1.939353810908912,
+            0.87665726,
+            2.04313234,
+            2.67702257,
+            2.74971934,
+            2.38728532,
+            1.78404205,
+            2.11388396,
+            1.92959433,
         ]
 
-        with self.subTest(msg="Test exact backend."):
+        # SHould be roughly the same in both Exact and shot-based backends
+        expected_aux_ops = (-0.2177982985749799, 0.2556790598588627)
+
+        with self.subTest(msg="Test exact backend"):
             algorithm_globals.random_seed = self.seed
-            estimator = Estimator()
+
+            estimator = StatevectorEstimator(seed=self.seed)
             qgt = LinCombQGT(estimator)
             gradient = LinCombEstimatorGradient(estimator)
             var_principle = ImaginaryMcLachlanPrinciple(qgt, gradient)
@@ -106,8 +108,6 @@ def test_run_d_1_with_aux_ops(self):
 
             parameter_values = evolution_result.parameter_values[-1]
 
-            expected_aux_ops = (-0.2177982985749799, 0.2556790598588627)
-
             for i, parameter_value in enumerate(parameter_values):
                 np.testing.assert_almost_equal(
                     float(parameter_value), thetas_expected[i], decimal=2
@@ -117,10 +117,11 @@ def test_run_d_1_with_aux_ops(self):
                 [result[0] for result in aux_ops], expected_aux_ops
             )
 
-        with self.subTest(msg="Test shot-based backend."):
+        with self.subTest(msg="Test non-zero precision backend."):
             algorithm_globals.random_seed = self.seed
 
-            estimator = Estimator(options={"shots": 4096, "seed": self.seed})
+            # A precision of pow(2, -6) roughly corresponds to 4096 shots
+            estimator = StatevectorEstimator(default_precision=pow(2, -6), seed=self.seed)
             qgt = LinCombQGT(estimator)
             gradient = LinCombEstimatorGradient(estimator)
             var_principle = ImaginaryMcLachlanPrinciple(qgt, gradient)
@@ -134,15 +135,13 @@ def test_run_d_1_with_aux_ops(self):
 
             parameter_values = evolution_result.parameter_values[-1]
 
-            expected_aux_ops = (-0.24629853310903974, 0.2518122871921184)
-
             for i, parameter_value in enumerate(parameter_values):
                 np.testing.assert_almost_equal(
                     float(parameter_value), thetas_expected_shots[i], decimal=2
                 )
 
             np.testing.assert_array_almost_equal(
-                [result[0] for result in aux_ops], expected_aux_ops
+                [result[0] for result in aux_ops], expected_aux_ops, decimal=1
             )
 
     def test_run_d_1_t_7(self):
@@ -258,21 +257,23 @@ def test_run_d_1_time_dependent(self):
 
         thetas_expected = [1.83881002737137e-18, 2.43224994794434, -3.05311331771918e-18]
 
-        thetas_expected_shots = [1.83881002737137e-18, 2.43224994794434, -3.05311331771918e-18]
-
         state_expected = Statevector([0.34849948 + 0.0j, 0.93730897 + 0.0j]).to_dict()
         # the expected final state is Statevector([0.34849948+0.j, 0.93730897+0.j])
 
         with self.subTest(msg="Test exact backend."):
             algorithm_globals.random_seed = self.seed
-            estimator = Estimator()
+
+            estimator = StatevectorEstimator(seed=self.seed)
             var_principle = ImaginaryMcLachlanPrinciple()
 
             var_qite = VarQITE(
                 ansatz, init_param_values, var_principle, estimator, num_timesteps=100
             )
+
             evolution_result = var_qite.evolve(evolution_problem)
+
             evolved_state = evolution_result.evolved_state
+
             parameter_values = evolution_result.parameter_values[-1]
 
             for key, evolved_value in Statevector(evolved_state).to_dict().items():
@@ -284,10 +285,11 @@ def test_run_d_1_time_dependent(self):
                     float(parameter_value), thetas_expected[i], decimal=2
                 )
 
-        with self.subTest(msg="Test shot-based backend."):
+        with self.subTest(msg="Test non-zero precision backend."):
             algorithm_globals.random_seed = self.seed
 
-            estimator = Estimator(options={"shots": 4 * 4096, "seed": self.seed})
+            # A precision of pow(2, -6) roughly corresponds to 4096 shots
+            estimator = StatevectorEstimator(default_precision=pow(2, -6), seed=self.seed)
             var_principle = ImaginaryMcLachlanPrinciple()
 
             var_qite = VarQITE(
@@ -306,7 +308,7 @@ def test_run_d_1_time_dependent(self):
 
             for i, parameter_value in enumerate(parameter_values):
                 np.testing.assert_almost_equal(
-                    float(parameter_value), thetas_expected_shots[i], decimal=2
+                    float(parameter_value), thetas_expected[i], decimal=2
                 )
 
     # pylint: disable=too-many-positional-arguments
diff --git a/test/time_evolvers/variational/test_var_qrte.py b/test/time_evolvers/variational/test_var_qrte.py
index 3dc026e1..d25d06b0 100644
--- a/test/time_evolvers/variational/test_var_qrte.py
+++ b/test/time_evolvers/variational/test_var_qrte.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -21,14 +21,12 @@
 from qiskit import QuantumCircuit
 from qiskit.circuit import Parameter, ParameterVector
 from qiskit.circuit.library import EfficientSU2
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 from qiskit.quantum_info import SparsePauliOp, Pauli, Statevector
 
 from qiskit_algorithms.gradients import LinCombQGT, DerivativeType, LinCombEstimatorGradient
 from qiskit_algorithms import TimeEvolutionProblem, VarQRTE
-from qiskit_algorithms.time_evolvers.variational import (
-    RealMcLachlanPrinciple,
-)
+from qiskit_algorithms.time_evolvers.variational import RealMcLachlanPrinciple
 from qiskit_algorithms.utils import algorithm_globals
 
 
@@ -60,7 +58,7 @@ def expected_state(time):
 
         final_time = 0.75
         evolution_problem = TimeEvolutionProblem(hamiltonian, t_param=t_param, time=final_time)
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         varqrte = VarQRTE(circuit, initial_parameters, estimator=estimator)
 
         result = varqrte.evolve(evolution_problem)
@@ -110,20 +108,11 @@ def test_run_d_1_with_aux_ops(self):
             1.53853696496673,
         ]
 
-        thetas_expected_shots = [
-            0.886975892820015,
-            1.53822607733397,
-            1.57058096749141,
-            1.59023223608564,
-            1.60105707043745,
-            1.57018042397236,
-            1.64010900210835,
-            1.53959523034133,
-        ]
+        expected_aux_ops = [0.06836996703935797, 0.7711574493422457]
 
         with self.subTest(msg="Test exact backend."):
             algorithm_globals.random_seed = self.seed
-            estimator = Estimator()
+            estimator = StatevectorEstimator(seed=self.seed)
             qgt = LinCombQGT(estimator)
             gradient = LinCombEstimatorGradient(estimator, derivative_type=DerivativeType.IMAG)
             var_principle = RealMcLachlanPrinciple(qgt, gradient)
@@ -137,8 +126,6 @@ def test_run_d_1_with_aux_ops(self):
 
             parameter_values = evolution_result.parameter_values[-1]
 
-            expected_aux_ops = [0.06836996703935797, 0.7711574493422457]
-
             for i, parameter_value in enumerate(parameter_values):
                 np.testing.assert_almost_equal(
                     float(parameter_value), thetas_expected[i], decimal=2
@@ -148,10 +135,10 @@ def test_run_d_1_with_aux_ops(self):
                 [result[0] for result in aux_ops], expected_aux_ops
             )
 
-        with self.subTest(msg="Test shot-based backend."):
+        with self.subTest(msg="Test non-zero precision backend."):
             algorithm_globals.random_seed = self.seed
-
-            estimator = Estimator(options={"shots": 4 * 4096, "seed": self.seed})
+            # A precision of pow(2, -7) roughly corresponds to 4 * 4096 shots
+            estimator = StatevectorEstimator(seed=self.seed, default_precision=pow(2, -7))
             qgt = LinCombQGT(estimator)
             gradient = LinCombEstimatorGradient(estimator, derivative_type=DerivativeType.IMAG)
             var_principle = RealMcLachlanPrinciple(qgt, gradient)
@@ -165,14 +152,9 @@ def test_run_d_1_with_aux_ops(self):
 
             parameter_values = evolution_result.parameter_values[-1]
 
-            expected_aux_ops = [
-                0.070436,
-                0.777938,
-            ]
-
             for i, parameter_value in enumerate(parameter_values):
                 np.testing.assert_almost_equal(
-                    float(parameter_value), thetas_expected_shots[i], decimal=2
+                    float(parameter_value), thetas_expected[i], decimal=2
                 )
 
             np.testing.assert_array_almost_equal(
@@ -199,7 +181,7 @@ def test_run_d_2(self):
         init_param_values = np.zeros(len(parameters))
         for i in range(len(parameters)):
             init_param_values[i] = np.pi / 4
-        estimator = Estimator()
+        estimator = StatevectorEstimator()
         qgt = LinCombQGT(estimator)
         gradient = LinCombEstimatorGradient(estimator, derivative_type=DerivativeType.IMAG)
 
@@ -251,51 +233,23 @@ def test_run_d_1_time_dependent(self):
 
         evolution_problem = TimeEvolutionProblem(observable, time, t_param=t_param)
 
-        thetas_expected = [1.27675647831902e-18, 1.5707963267949, 0.990000000000001]
-
-        thetas_expected_shots = [0.00534345821469238, 1.56260960200375, 0.990017403734316]
+        thetas_expected = [0.0, 1.5707963267949, 0.99]
 
         # the expected final state is Statevector([0.62289306-0.33467034j, 0.62289306+0.33467034j])
 
-        with self.subTest(msg="Test exact backend."):
-            algorithm_globals.random_seed = self.seed
-            estimator = Estimator()
-            qgt = LinCombQGT(estimator)
-            gradient = LinCombEstimatorGradient(estimator, derivative_type=DerivativeType.IMAG)
-            var_principle = RealMcLachlanPrinciple(qgt, gradient)
-
-            var_qrte = VarQRTE(
-                ansatz, init_param_values, var_principle, estimator, num_timesteps=100
-            )
-            evolution_result = var_qrte.evolve(evolution_problem)
-
-            parameter_values = evolution_result.parameter_values[-1]
-
-            for i, parameter_value in enumerate(parameter_values):
-                np.testing.assert_almost_equal(
-                    float(parameter_value), thetas_expected[i], decimal=2
-                )
-
-        with self.subTest(msg="Test shot-based backend."):
-            algorithm_globals.random_seed = self.seed
-
-            estimator = Estimator(options={"shots": 4 * 4096, "seed": self.seed})
-            qgt = LinCombQGT(estimator)
-            gradient = LinCombEstimatorGradient(estimator, derivative_type=DerivativeType.IMAG)
-            var_principle = RealMcLachlanPrinciple(qgt, gradient)
-
-            var_qrte = VarQRTE(
-                ansatz, init_param_values, var_principle, estimator, num_timesteps=100
-            )
+        algorithm_globals.random_seed = self.seed
+        estimator = StatevectorEstimator()
+        qgt = LinCombQGT(estimator)
+        gradient = LinCombEstimatorGradient(estimator, derivative_type=DerivativeType.IMAG)
+        var_principle = RealMcLachlanPrinciple(qgt, gradient)
 
-            evolution_result = var_qrte.evolve(evolution_problem)
+        var_qrte = VarQRTE(ansatz, init_param_values, var_principle, estimator, num_timesteps=100)
+        evolution_result = var_qrte.evolve(evolution_problem)
 
-            parameter_values = evolution_result.parameter_values[-1]
+        parameter_values = evolution_result.parameter_values[-1]
 
-            for i, parameter_value in enumerate(parameter_values):
-                np.testing.assert_almost_equal(
-                    float(parameter_value), thetas_expected_shots[i], decimal=2
-                )
+        for i, parameter_value in enumerate(parameter_values):
+            np.testing.assert_almost_equal(float(parameter_value), thetas_expected[i], decimal=2)
 
     def _test_helper(self, observable, thetas_expected, time, var_qrte):
         evolution_problem = TimeEvolutionProblem(observable, time)
diff --git a/test/time_evolvers/variational/variational_principles/imaginary/test_imaginary_mc_lachlan_principle.py b/test/time_evolvers/variational/variational_principles/imaginary/test_imaginary_mc_lachlan_principle.py
index 9d8c54fa..df60c3f2 100644
--- a/test/time_evolvers/variational/variational_principles/imaginary/test_imaginary_mc_lachlan_principle.py
+++ b/test/time_evolvers/variational/variational_principles/imaginary/test_imaginary_mc_lachlan_principle.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -23,7 +23,7 @@
 
 from qiskit.quantum_info import SparsePauliOp
 from qiskit.circuit.library import EfficientSU2
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 
 from qiskit_algorithms.gradients import LinCombEstimatorGradient, DerivativeType
 from qiskit_algorithms.time_evolvers.variational import (
@@ -103,7 +103,7 @@ def test_calc_calc_evolution_gradient(self):
 
     def test_gradient_setting(self):
         """Test reactions to wrong gradient settings.."""
-        estimator = Estimator()
+        estimator = StatevectorEstimator(seed=123)
         gradient = LinCombEstimatorGradient(estimator, derivative_type=DerivativeType.IMAG)
 
         with self.assertWarns(Warning):
diff --git a/test/time_evolvers/variational/variational_principles/real/test_real_mc_lachlan_principle.py b/test/time_evolvers/variational/variational_principles/real/test_real_mc_lachlan_principle.py
index 40195d16..a65eab8c 100644
--- a/test/time_evolvers/variational/variational_principles/real/test_real_mc_lachlan_principle.py
+++ b/test/time_evolvers/variational/variational_principles/real/test_real_mc_lachlan_principle.py
@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023.
+# (C) Copyright IBM 2023, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -24,12 +24,10 @@
 
 from qiskit.quantum_info import SparsePauliOp
 from qiskit.circuit.library import EfficientSU2
-from qiskit.primitives import Estimator
+from qiskit.primitives import StatevectorEstimator
 
 from qiskit_algorithms.gradients import LinCombEstimatorGradient, DerivativeType
-from qiskit_algorithms.time_evolvers.variational import (
-    RealMcLachlanPrinciple,
-)
+from qiskit_algorithms.time_evolvers.variational import RealMcLachlanPrinciple
 
 
 class TestRealMcLachlanPrinciple(QiskitAlgorithmsTestCase):
@@ -108,7 +106,7 @@ def test_calc_evolution_gradient(self):
 
     def test_gradient_setting(self):
         """Test reactions to wrong gradient settings.."""
-        estimator = Estimator()
+        estimator = StatevectorEstimator(seed=123)
         gradient = LinCombEstimatorGradient(estimator, derivative_type=DerivativeType.REAL)
 
         with self.assertWarns(Warning):