diff --git a/docs/tutorials/01_algorithms_introduction.ipynb b/docs/tutorials/01_algorithms_introduction.ipynb
index 13baa14e..601d368f 100644
--- a/docs/tutorials/01_algorithms_introduction.ipynb
+++ b/docs/tutorials/01_algorithms_introduction.ipynb
@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# An Introduction to Algorithms in Qiskit\n",
+    "# An Introduction to Algorithms for Qiskit\n",
     "\n",
-    "This is an introduction to algorithms in Qiskit and provides a high-level overview to help understand the various aspects of the functionality to get started. Other tutorials will provide more in-depth material, on given algorithms, and ways to use them etc."
+    "This is an introduction to algorithms for Qiskit and provides a high-level overview to help understand the various aspects of the functionality to get started. Other tutorials will provide more in-depth material, on given algorithms, and ways to use them etc.\n"
    ]
   },
   {
@@ -15,16 +15,16 @@
    "source": [
     "## How is the algorithm library structured?\n",
     "\n",
-    "Qiskit provides a number of [Algorithms](https://qiskit.org/documentation/apidoc/algorithms.html) and they are grouped by category according to the task they can perform. For instance `Minimum Eigensolvers` to find the smallest eigen value of an operator, for example ground state energy of a chemistry Hamiltonian or a solution to an optimization problem when expressed as an Ising Hamiltonian. There are `Time Evolvers` for the time evolution of quantum systems and `Amplitude Estimators` for value estimation that can be used say in financial applications. The full set of categories can be seen in the Algorithms documentation link above.\n",
+    "Qiskit provides a number of [Algorithms](https://qiskit.org/ecosystem/algorithms/apidocs/algorithms.html) and they are grouped by category according to the task they can perform. For instance [Minimum Eigensolvers](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.html#module-qiskit_algorithms.minimum_eigensolvers) to find the smallest eigen value of an operator, for example ground state energy of a chemistry Hamiltonian or a solution to an optimization problem when expressed as an Ising Hamiltonian. There are [Time Evolvers](https://qiskit.org/ecosystem/algorithms/apidocs/algorithms.html#time-evolvers) for the time evolution of quantum systems and [Amplitude Estimators](https://qiskit.org/ecosystem/algorithms/apidocs/algorithms.html#amplitude-estimators) for value estimation that can be used say in financial applications. The full set of categories can be seen in the Algorithms documentation link above.\n",
     "\n",
-    "Algorithms are configurable and often part of the configuration will be in the form of smaller building blocks, of which different instances of the building block type can be given. For instance with `VQE`, the Variational Quantum Eigensolver, it takes a trial wavefunction, in the form of a `QuantumCircuit` and a classical optimizer among other things.\n",
+    "Algorithms are configurable and often part of the configuration will be in the form of smaller building blocks, of which different instances of the building block type can be given. For instance with [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE), the Variational Quantum Eigensolver, it takes a trial wavefunction, in the form of a [QuantumCircuit](https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.html#quantumcircuit) and a classical optimizer among other things.\n",
     "\n",
-    "Let's take a look at an example to construct a VQE instance. Here `TwoLocal` is the variational form (trial wavefunction), a parameterized circuit which can be varied, and `SLSQP` a classical optimizer. These are created as separate instances and passed to VQE when it is constructed. Trying, for example, a different classical optimizer, or variational form is simply a case of creating an instance of the one you want and passing it into VQE."
+    "Let's take a look at an example to construct a VQE instance. Here [TwoLocal](https://qiskit.org/documentation/stubs/qiskit.circuit.library.TwoLocal.html#twolocal) is the variational form (trial wavefunction), a parameterized circuit which can be varied, and [SLSQP](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.optimizers.SLSQP.html#qiskit_algorithms.optimizers.SLSQP) a classical optimizer. These are created as separate instances and passed to VQE when it is constructed. Trying, for example, a different classical optimizer, or variational form is simply a case of creating an instance of the one you want and passing it into VQE.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,22 +40,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's draw the ansatz so we can see it's a `QuantumCircuit` where θ\\[0\\] through θ\\[7\\] will be the parameters that are varied as VQE optimizer finds the minimum eigenvalue. We'll come back to the parameters later in a working example below."
+    "Let's draw the ansatz so we can see it's a [QuantumCircuit](https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.html#quantumcircuit) where θ\\[0\\] through θ\\[7\\] will be the parameters that are varied as VQE optimizer finds the minimum eigenvalue. We'll come back to the parameters later in a working example below.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 507.852x144.48 with 1 Axes>"
+       "<Figure size 705.35x200.667 with 1 Axes>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -68,7 +68,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "But more is needed before we can run the algorithm so let's get to that next."
+    "But more is needed before we can run the algorithm so let's get to that next.\n"
    ]
   },
   {
@@ -79,12 +79,12 @@
     "\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` which is shipped with the default Qiskit Terra installation."
+    "In the VQE, we have to evaluate expectation values, so for example we can use the [qiskit.primitives.Estimator](https://qiskit.org/documentation/stubs/qiskit.primitives.Estimator.html) which is shipped with the default Qiskit Terra installation.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,14 +97,14 @@
    "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.org/documentation/apidoc/aer_primitives.html) and for the actual hardware [Qiskit IBM Runtime](https://qiskit.org/documentation/partners/qiskit_ibm_runtime/).\n",
+    "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.org/ecosystem/aer/apidocs/aer_primitives.html) and for the actual hardware [Qiskit IBM Runtime](https://qiskit.org/ecosystem/ibm-runtime/).\n",
     "\n",
-    "With all the ingredients ready, we can now instantiate the VQE:"
+    "With all the ingredients ready, we can now instantiate the VQE:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +117,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can call the [compute_mininum_eigenvalue()](https://qiskit.org/documentation/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.compute_minimum_eigenvalue.html#qiskit_algorithms.minimum_eigensolvers.VQE.compute_minimum_eigenvalue) method. The latter is the interface of choice for the application modules, such as Nature and Optimization, in order that they can work interchangeably with any algorithm within the specific category."
+    "Now we can call the [compute_mininum_eigenvalue()](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE.compute_minimum_eigenvalue) method. The latter is the interface of choice for the application modules, such as Nature and Optimization, in order that they can work interchangeably with any algorithm within the specific category.\n"
    ]
   },
   {
@@ -126,36 +126,38 @@
    "source": [
     "## A complete working example\n",
     "\n",
-    "Let's put what we have learned from above together and create a complete working example. VQE will find the minimum eigenvalue, i.e. minimum energy value of a Hamiltonian operator and hence we need such an operator for VQE to work with. Such an operator is given below. This was originally created by the Nature application module as the Hamiltonian for an H2 molecule at 0.735A interatomic distance. It's a sum of Pauli terms as below, but for now I am not going to say anything further about it since the goal is to run the algorithm, but further information on operators can be found in other tutorials."
+    "Let's put what we have learned from above together and create a complete working example. VQE will find the minimum eigenvalue, i.e. minimum energy value of a Hamiltonian operator and hence we need such an operator for VQE to work with. Such an operator is given below. This was originally created by the Nature application module as the Hamiltonian for an H2 molecule at 0.735A interatomic distance. It's a sum of Pauli terms as below, but for now I am not going to say anything further about it since the goal is to run the algorithm, but further information on operators can be found in other tutorials.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
-    "H2_op = SparsePauliOp.from_list([\n",
-    "    (\"II\", -1.052373245772859),\n",
-    "    (\"IZ\", 0.39793742484318045),\n",
-    "    (\"ZI\", -0.39793742484318045),\n",
-    "    (\"ZZ\", -0.01128010425623538),\n",
-    "    (\"XX\", 0.18093119978423156)\n",
-    "])"
+    "H2_op = SparsePauliOp.from_list(\n",
+    "    [\n",
+    "        (\"II\", -1.052373245772859),\n",
+    "        (\"IZ\", 0.39793742484318045),\n",
+    "        (\"ZI\", -0.39793742484318045),\n",
+    "        (\"ZZ\", -0.01128010425623538),\n",
+    "        (\"XX\", 0.18093119978423156),\n",
+    "    ]\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "So let's run VQE and print the result object it returns."
+    "So let's run VQE and print the result object it returns.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "scrolled": true
    },
@@ -165,23 +167,23 @@
      "output_type": "stream",
      "text": [
       "{   'aux_operators_evaluated': None,\n",
-      "    'cost_function_evals': 102,\n",
-      "    'eigenvalue': -1.857275020719397,\n",
-      "    'optimal_circuit': <qiskit.circuit.library.n_local.two_local.TwoLocal object at 0x7f96da26a470>,\n",
-      "    'optimal_parameters': {   ParameterVectorElement(θ[0]): -2.403507257619715,\n",
-      "                              ParameterVectorElement(θ[5]): 1.7060524493254914,\n",
-      "                              ParameterVectorElement(θ[1]): 3.085467047665086,\n",
-      "                              ParameterVectorElement(θ[2]): -2.1949965223522487,\n",
-      "                              ParameterVectorElement(θ[3]): 4.276089268519914,\n",
-      "                              ParameterVectorElement(θ[4]): -3.098644972035885,\n",
-      "                              ParameterVectorElement(θ[6]): 0.032773583818940334,\n",
-      "                              ParameterVectorElement(θ[7]): 2.8861019033185396},\n",
-      "    'optimal_point': array([-2.40350726,  3.08546705, -2.19499652,  4.27608927, -3.09864497,\n",
-      "        1.70605245,  0.03277358,  2.8861019 ]),\n",
-      "    'optimal_value': -1.857275020719397,\n",
+      "    'cost_function_evals': 54,\n",
+      "    'eigenvalue': -1.8572749751321997,\n",
+      "    'optimal_circuit': <qiskit.circuit.library.n_local.two_local.TwoLocal object at 0x10b47a050>,\n",
+      "    'optimal_parameters': {   ParameterVectorElement(θ[1]): -4.137380786621057,\n",
+      "                              ParameterVectorElement(θ[0]): 0.587854208432134,\n",
+      "                              ParameterVectorElement(θ[2]): 5.638873652671935,\n",
+      "                              ParameterVectorElement(θ[3]): 6.15338799125403,\n",
+      "                              ParameterVectorElement(θ[4]): -1.6196994394795583,\n",
+      "                              ParameterVectorElement(θ[5]): -1.2567360775493952,\n",
+      "                              ParameterVectorElement(θ[6]): -2.2594501173878827,\n",
+      "                              ParameterVectorElement(θ[7]): 5.24757903057859},\n",
+      "    'optimal_point': array([ 0.58785421, -4.13738079,  5.63887365,  6.15338799, -1.61969944,\n",
+      "       -1.25673608, -2.25945012,  5.24757903]),\n",
+      "    'optimal_value': -1.8572749751321997,\n",
       "    'optimizer_evals': None,\n",
-      "    'optimizer_result': <qiskit.algorithms.optimizers.optimizer.OptimizerResult object at 0x7f96da2a4d60>,\n",
-      "    'optimizer_time': 0.29071593284606934}\n"
+      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x13af68210>,\n",
+      "    'optimizer_time': 0.11543583869934082}\n"
      ]
     }
    ],
@@ -194,7 +196,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "From the above result we can see the number of cost function (=energy) evaluations the optimizer took until it found the minimum eigenvalue of $\\approx -1.85727$ which is the electronic ground state energy of the given H2 molecule. The optimal parameters of the ansatz can also be seen which are the values that were in the ansatz at the minimum value."
+    "From the above result we can see the number of cost function (=energy) evaluations the optimizer took until it found the minimum eigenvalue of $\\approx -1.85727$ which is the electronic ground state energy of the given H2 molecule. The optimal parameters of the ansatz can also be seen which are the values that were in the ansatz at the minimum value.\n"
    ]
   },
   {
@@ -205,14 +207,14 @@
     "\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",
     "\n",
-    "In this example we're changing to a shot-based estimator, still using Qiskit Terra's reference primitive. However you could replace the primitive by e.g. Qiskit Aer's estimator (`qiskit_aer.primitives.Estimator`) or even a real backend (`qiskit_ibm_runtime.Estimator`).\n",
+    "In this example we're changing to a shot-based estimator, still using Qiskit Terra's reference primitive. However you could replace the primitive by e.g. Qiskit Aer's estimator ([qiskit_aer.primitives.Estimator](https://qiskit.org/ecosystem/aer/stubs/qiskit_aer.primitives.Estimator.html#estimator)) or even a real backend ([qiskit_ibm_runtime.Estimator](https://qiskit.org/ecosystem/ibm-runtime/stubs/qiskit_ibm_runtime.Estimator.html#qiskit_ibm_runtime.Estimator)).\n",
     "\n",
-    "For noisy loss functions, the SPSA optimizer typically performs well, so we also update the optimizer. See also the [noisy VQE tutorial](https://qiskit.org/documentation/tutorials/algorithms/03_vqe_simulation_with_noise.html) for more details on shot-based and noisy simulations."
+    "For noisy loss functions, the SPSA optimizer typically performs well, so we also update the optimizer. See also the [noisy VQE tutorial](https://qiskit.org/ecosystem/algorithms/tutorials/03_vqe_simulation_with_noise.html) for more details on shot-based and noisy simulations.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -221,22 +223,22 @@
      "text": [
       "{   'aux_operators_evaluated': None,\n",
       "    'cost_function_evals': 200,\n",
-      "    'eigenvalue': -1.8574503552440247,\n",
-      "    'optimal_circuit': <qiskit.circuit.library.n_local.two_local.TwoLocal object at 0x7f96da2f4250>,\n",
-      "    'optimal_parameters': {   ParameterVectorElement(θ[0]): -7.7940259581467375,\n",
-      "                              ParameterVectorElement(θ[5]): 0.28827257835035214,\n",
-      "                              ParameterVectorElement(θ[1]): -1.8091021117029589,\n",
-      "                              ParameterVectorElement(θ[2]): -2.460381278734678,\n",
-      "                              ParameterVectorElement(θ[3]): -7.725013961075425,\n",
-      "                              ParameterVectorElement(θ[4]): -1.3793338621798832,\n",
-      "                              ParameterVectorElement(θ[6]): -2.4148423942537587,\n",
-      "                              ParameterVectorElement(θ[7]): -1.8555574263247812},\n",
-      "    'optimal_point': array([-7.79402596, -1.80910211, -2.46038128, -7.72501396, -1.37933386,\n",
-      "        0.28827258, -2.41484239, -1.85555743]),\n",
-      "    'optimal_value': -1.8574503552440247,\n",
+      "    'eigenvalue': -1.8580626437144234,\n",
+      "    'optimal_circuit': <qiskit.circuit.library.n_local.two_local.TwoLocal object at 0x13a940f50>,\n",
+      "    'optimal_parameters': {   ParameterVectorElement(θ[1]): -3.537993927798663,\n",
+      "                              ParameterVectorElement(θ[0]): -0.08794454569370512,\n",
+      "                              ParameterVectorElement(θ[2]): 2.544219333944697,\n",
+      "                              ParameterVectorElement(θ[3]): -4.489749290310527,\n",
+      "                              ParameterVectorElement(θ[4]): -4.290225228259743,\n",
+      "                              ParameterVectorElement(θ[5]): 5.040270846755398,\n",
+      "                              ParameterVectorElement(θ[6]): 4.183950819007894,\n",
+      "                              ParameterVectorElement(θ[7]): 5.583448915915113},\n",
+      "    'optimal_point': array([-0.08794455, -3.53799393,  2.54421933, -4.48974929, -4.29022523,\n",
+      "        5.04027085,  4.18395082,  5.58344892]),\n",
+      "    'optimal_value': -1.8580626437144234,\n",
       "    'optimizer_evals': None,\n",
-      "    'optimizer_result': <qiskit.algorithms.optimizers.optimizer.OptimizerResult object at 0x7f96da26a5f0>,\n",
-      "    'optimizer_time': 0.8142139911651611}\n"
+      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x12d61ea50>,\n",
+      "    'optimizer_time': 0.5061171054840088}\n"
      ]
     }
    ],
@@ -255,25 +257,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note: We do not fix the random seed in the simulators here, so re-running gives slightly varying results."
+    "Note: We do not fix the random seed in the simulators here, so re-running gives slightly varying results.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This concludes this introduction to algorithms in Qiskit. Please check out the other algorithm tutorials in this series for both broader as well as more in depth coverage of the algorithms."
+    "This concludes this introduction to algorithms in Qiskit. Please check out the other algorithm tutorials in this series for both broader as well as more in depth coverage of the algorithms.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0.dev0+f52bb33</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.1</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.2</td></tr><tr><td><code>qiskit-nature</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.10.4</td></tr><tr><td>Python compiler</td><td>Clang 12.0.0 </td></tr><tr><td>Python build</td><td>main, Mar 31 2022 03:38:35</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Wed Dec 07 11:02:26 2022 CET</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>None</td></tr><tr><td><code>qiskit-terra</code></td><td>0.25.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.11.4</td></tr><tr><td>Python compiler</td><td>Clang 14.0.3 (clang-1403.0.22.14.1)</td></tr><tr><td>Python build</td><td>main, Jul 25 2023 17:07:07</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>6</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Sun Aug 06 16:39:42 2023 BST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -285,7 +287,7 @@
     {
      "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 Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</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 Qiskit</h3><p>&copy; Copyright IBM 2017, 2023.</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>"
@@ -300,13 +302,6 @@
     "%qiskit_version_table\n",
     "%qiskit_copyright"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -325,7 +320,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.4"
   },
   "vscode": {
    "interpreter": {
@@ -335,4 +330,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/docs/tutorials/02_vqe_advanced_options.ipynb b/docs/tutorials/02_vqe_advanced_options.ipynb
new file mode 100644
index 00000000..477bf07e
--- /dev/null
+++ b/docs/tutorials/02_vqe_advanced_options.ipynb
@@ -0,0 +1,539 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced VQE Options\n",
+    "\n",
+    "In the first algorithms tutorial, you learned how to set up a basic [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE) algorithm. Now, you will see how to provide more advanced configuration parameters to explore the full range of capabilities of variational algorithms for Qiskit: [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE), [QAOA](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.QAOA.html#qiskit_algorithms.minimum_eigensolvers.QAOA) and [VQD](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.eigensolvers.VQD.html#vqd) among others. In particular, this tutorial will cover how to set up a [callback](https://qiskit.org/ecosystem/algorithms/search.html?q=callback&check_keywords=yes&area=default) to monitor convergence and the use of custom [initial points](https://qiskit.org/ecosystem/algorithms/search.html?q=initial_point&check_keywords=yes&area=default) and [gradient](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE.gradient)s.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Callback\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Callback methods can be used to monitor optimization progress as the algorithm runs and converges to the minimum. The callback is invoked for each functional evaluation by the optimizer and provides the current optimizer value, evaluation count, current optimizer parameters etc. Note that, depending on the specific optimizer this may not be each iteration (step) of the optimizer, so for example if the optimizer is calling the cost function to compute a finite difference based gradient this will be visible via the callback.\n",
+    "\n",
+    "This section demonstrates how to leverage callbacks in [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE) to plot the convergence path to the ground state energy with a selected set of optimizers.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, you need a qubit operator for VQE. For this example, you can use the same operator as used in the algorithms introduction, which was originally computed by [Qiskit Nature](https://qiskit.org/ecosystem/nature/) for an H2 molecule.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "\n",
+    "H2_op = SparsePauliOp.from_list(\n",
+    "    [\n",
+    "        (\"II\", -1.052373245772859),\n",
+    "        (\"IZ\", 0.39793742484318045),\n",
+    "        (\"ZI\", -0.39793742484318045),\n",
+    "        (\"ZZ\", -0.01128010425623538),\n",
+    "        (\"XX\", 0.18093119978423156),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step is to instantiate the [Estimator](https://qiskit.org/documentation/stubs/qiskit.primitives.Estimator.html#qiskit.primitives.Estimator) of choice for the evaluation of expectation values within [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE). For simplicity, you can select the [qiskit.primitives.Estimator]() shipped with the default [Qiskit Terra](https://qiskit.org/documentation/apidoc/terra.html) installation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit.primitives import Estimator\n",
+    "\n",
+    "estimator = Estimator()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are now ready to compare a set of optimizers through the [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE) callback. The minimum energy of the H2 Hamiltonian can be found quite easily, so the maximum number of iterations (`maxiter`) does not have to be very large. You can once again use [TwoLocal](https://qiskit.org/documentation/stubs/qiskit.circuit.library.TwoLocal.html#twolocal) as the selected trial wavefunction (i.e. ansatz).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization complete      \n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from qiskit_algorithms.minimum_eigensolvers import VQE\n",
+    "from qiskit_algorithms.optimizers import COBYLA, L_BFGS_B, SLSQP\n",
+    "from qiskit.circuit.library import TwoLocal\n",
+    "from qiskit.utils import algorithm_globals\n",
+    "\n",
+    "# we will iterate over these different optimizers\n",
+    "optimizers = [COBYLA(maxiter=80), L_BFGS_B(maxiter=60), SLSQP(maxiter=60)]\n",
+    "converge_counts = np.empty([len(optimizers)], dtype=object)\n",
+    "converge_vals = np.empty([len(optimizers)], dtype=object)\n",
+    "\n",
+    "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",
+    "\n",
+    "    counts = []\n",
+    "    values = []\n",
+    "\n",
+    "    def store_intermediate_result(eval_count, parameters, mean, std):\n",
+    "        counts.append(eval_count)\n",
+    "        values.append(mean)\n",
+    "\n",
+    "    vqe = VQE(estimator, ansatz, optimizer, callback=store_intermediate_result)\n",
+    "    result = vqe.compute_minimum_eigenvalue(operator=H2_op)\n",
+    "    converge_counts[i] = np.asarray(counts)\n",
+    "    converge_vals[i] = np.asarray(values)\n",
+    "\n",
+    "print(\"\\rOptimization complete      \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, from the callback data you stored, you can plot the energy value at each objective function call each optimizer makes. An optimizer using a finite difference method for computing gradient has that characteristic step-like plot where for a number of evaluations it is computing the value for close by points to establish a gradient (the close by points having very similar values whose difference cannot be seen on the scale of the graph here).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false,
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pylab\n",
+    "\n",
+    "pylab.rcParams[\"figure.figsize\"] = (12, 8)\n",
+    "for i, optimizer in enumerate(optimizers):\n",
+    "    pylab.plot(converge_counts[i], converge_vals[i], label=type(optimizer).__name__)\n",
+    "pylab.xlabel(\"Eval count\")\n",
+    "pylab.ylabel(\"Energy\")\n",
+    "pylab.title(\"Energy convergence for various optimizers\")\n",
+    "pylab.legend(loc=\"upper right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, since the above problem is still easily tractable classically, you can use [NumPyMinimumEigensolver](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.NumPyMinimumEigensolver.html#qiskit_algorithms.minimum_eigensolvers.NumPyMinimumEigensolver) to compute a reference value for the solution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reference value: -1.85728\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_algorithms.minimum_eigensolvers import NumPyMinimumEigensolver\n",
+    "\n",
+    "numpy_solver = NumPyMinimumEigensolver()\n",
+    "result = numpy_solver.compute_minimum_eigenvalue(operator=H2_op)\n",
+    "ref_value = result.eigenvalue.real\n",
+    "print(f\"Reference value: {ref_value:.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "You can now plot the difference between the [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE) solution and this exact reference value as the algorithm converges towards the minimum energy.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pylab.rcParams[\"figure.figsize\"] = (12, 8)\n",
+    "for i, optimizer in enumerate(optimizers):\n",
+    "    pylab.plot(\n",
+    "        converge_counts[i],\n",
+    "        abs(ref_value - converge_vals[i]),\n",
+    "        label=type(optimizer).__name__,\n",
+    "    )\n",
+    "pylab.xlabel(\"Eval count\")\n",
+    "pylab.ylabel(\"Energy difference from solution reference value\")\n",
+    "pylab.title(\"Energy convergence for various optimizers\")\n",
+    "pylab.yscale(\"log\")\n",
+    "pylab.legend(loc=\"upper right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gradients\n",
+    "\n",
+    "In the variational algorithms for Qiskit, if the provided optimizer uses a gradient-based technique, the default gradient method will be finite differences. However, these classes include an option to pass custom gradients via the [gradient](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.gradients.html) parameter, which can be any of the provided methods within Qiskit's [gradient](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.gradients.html) framework, which fully supports the use of primitives. This section shows how to use custom gradients in the VQE workflow.\n",
+    "\n",
+    "The first step is to initialize both the corresponding primitive and primitive gradient:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_algorithms.gradients import FiniteDiffEstimatorGradient\n",
+    "\n",
+    "estimator = Estimator()\n",
+    "gradient = FiniteDiffEstimatorGradient(estimator, epsilon=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, you can inspect an [SLSQP](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.optimizers.SLSQP.html#slsqp) run using the [FiniteDiffEstimatorGradient](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.gradients.FiniteDiffEstimatorGradient.html#qiskit_algorithms.gradients.FiniteDiffEstimatorGradient) from above:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Value using Gradient: -1.85728\n"
+     ]
+    }
+   ],
+   "source": [
+    "algorithm_globals.random_seed = 50\n",
+    "ansatz = TwoLocal(rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
+    "\n",
+    "optimizer = SLSQP(maxiter=100)\n",
+    "\n",
+    "counts = []\n",
+    "values = []\n",
+    "\n",
+    "\n",
+    "def store_intermediate_result(eval_count, parameters, mean, std):\n",
+    "    counts.append(eval_count)\n",
+    "    values.append(mean)\n",
+    "\n",
+    "\n",
+    "vqe = VQE(estimator, ansatz, optimizer, callback=store_intermediate_result, gradient=gradient)\n",
+    "\n",
+    "result = vqe.compute_minimum_eigenvalue(operator=H2_op)\n",
+    "print(f\"Value using Gradient: {result.eigenvalue.real:.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2bdLll19ernvD16lTRykpKcWWJyQkFBttDgwM1MiRIzVy5Ejl5ubqpptu0rRp0zRlypQK364tOjq6VD/nkgQGBmrgwIFatmyZDh06VOIkhmWxdOlSJSUl6fPPP9ell17qWr5v375imaWCsyUKzxCRpLy8PO3bt0+dO3c+7z4q4/e8JOX5NwAAnoZr6gEAVapBgwYaOHCg3nrrLR05cqTY62ffwup8unfvrnr16umdd95Rfn6+a/ncuXPPe6runXfeqe+//16vvPKK6tWrp6uvvvqi+xk+fLhM0yxxpLZw9PCaa66RVDAz99kKR5aHDh160f2UpG3bturYsaM+/vhjffzxx2rcuHGRAubl5aXhw4dr/vz5JRbj0vwcIyIi1KFDB/3nP/8pcmr3smXLtGXLliLr3nLLLXI4HHr22WeLvU9+fn6Jxflihg8frk2bNhW7Q4D0x8/3lltu0aFDh/TOO+8UW+f06dOuuyGcT2xsrFavXq3c3FzXsq+//loHDhwosl5SUlKR576+vmrXrp1M01ReXl6pP9P5DB48WL/88ovi4+Ndy5KTk8975sO5pk6dKofDoTvuuKPE0/DLMppdOCJ+9ja5ubl64403iqzXvXt3hYeHa+bMmUV+frNmzbro910Zv+clCQwMLNe/NQDwJIzUAwBK9O2337om0Dpbnz59Lnp97blef/119evXTx07dtR9992nmJgYHTt2TL/88osOHjyoTZs2XXB7X19f/fWvf9Ujjzyiyy67TLfccov279+vWbNmKTY2tsTRvNtvv11PPPGEFixYoHHjxhW5/dr5DBo0SHfeeadee+017dq1S0OGDJHT6dSKFSs0aNAgjR8/Xp07d9bdd9+tt99+23Va89q1azV79mzdcMMNRc4kKKuRI0dq6tSp8vPz09ixY2WzFT32/txzz2nJkiXq1auX7rvvPrVr107JycnauHGjFi9erOTk5Ivu4+9//7uGDRumvn376p577tGpU6c0Y8YMdejQoUh5HDBggB544AFNnz5d8fHxuuqqq+Tj46Ndu3bp008/1auvvqqbb765TJ/v8ccf12effaYRI0ZozJgx6tatm5KTk7Vw4ULNnDlTnTt31p133qlPPvlEDz74oJYsWaK+ffvK4XBo+/bt+uSTT7Ro0aILzvVw77336rPPPtOQIUN0yy23aM+ePZozZ45iY2OLrHfVVVepUaNG6tu3rxo2bKht27ZpxowZGjp0aIn3hS+rJ554QnPmzNGVV16pRx55xHVLu6ZNmyo5OfmiI9D9+/fXjBkz9Mgjj6hly5YaNWqU2rRpo9zcXO3cuVNz586Vr6+vGjVqdNEsffr0UZ06dXT33Xfr0UcflWEY+uCDD4odGPDx8dHf/vY3PfDAA7rssss0cuRI7du3T++//36pfucr+ntekm7duunNN9/U3/72N7Vo0UINGjQochYBAEDc0g4AUNSFbmmns25hVXhLqxdffLHYe0gyn3766SLL9uzZY951111mo0aNTB8fH7NJkybmtddea3722WfF9r1u3boSs7322mtmdHS0abfbzZ49e5qrVq0yu3XrZg4ZMqTE9a+55hpTkvnzzz+X+vPn5+ebL774otmmTRvT19fXDA8PN6+++mpzw4YNrnXy8vLMZ555xmzevLnp4+NjRkVFmVOmTClyKy/TLLj92dChQ4vt49zbqxXatWuX6+e8cuXKEvMdO3bMfPjhh82oqCjTx8fHbNSokXn55Zebb7/9tmudwlvaffrppyW+x0cffWS2adPGtNvtZocOHcyFCxeaw4cPN9u0aVNs3bffftvs1q2b6e/vbwYHB5sdO3Y0n3jiCfPw4cPl+pxJSUnm+PHjzSZNmpi+vr5mZGSkeffddxe5TV9ubq75/PPPm+3btzftdrtZp04ds1u3buYzzzxjpqamlviZzvbPf/7TbNKkiWm3282+ffua69evL5blrbfeMi+99FKzXr16pt1uN2NjY83HH3+8yPuf75Z2pf2sv/76q9m/f3/TbrebkZGR5vTp083XXnvNlGQePXr0op+j8D3uuusus2nTpqavr68ZGBhodurUyZw0aZK5e/fuYhnOd5u+VatWmb179zb9/f3NiIgI84knnnDd/nHJkiVF1n3jjTfM5s2bm3a73ezevbu5fPnyYp+vpFvamWbFfs8L/92enefo0aPm0KFDzeDg4GK31QMAFDBMk9lIAADuyel0Kjw8XDfddFOJp2vfeOON2rJlS6muY/Z0Xbp0UXh4uH744Qero9RqEyZM0FtvvaWMjIzzThQHAEBZcE09AMAtZGdnFztd+D//+Y+Sk5M1cODAYusfOXJE33zzje68885qSuge8vLyisxLIBVMpLZp06YSf44ov9OnTxd5npSUpA8++ED9+vWj0AMAKg0j9QAAt7B06VJNnDhRI0aMUL169bRx40a9++67atu2rTZs2OC6N/e+ffu0atUq/fvf/9a6deu0Z8+eUl137Cn279+vK664QnfccYciIiK0fft2zZw5U6Ghodq6davq1atndcRao0uXLho4cKDatm2rY8eO6d1339Xhw4f1448/FpkEEQCAimCiPACAW2jWrJmioqL02muvuW45d9ddd+m5555zFXqpYCb3e+65R02bNtXs2bMp9OeoU6eOunXrpn//+986ceKEAgMDNXToUD333HMU+kp2zTXX6LPPPtPbb78twzDUtWtXvfvuuxR6AEClYqQeAAAAAAA3xTX1AAAAAAC4KUo9AAAAAABuimvqL8LpdOrw4cMKDg6WYRhWxwEAAAAA1HKmaSo9PV0RERGy2S48Fk+pv4jDhw8rKirK6hgAAAAAAA9z4MABRUZGXnAdSv1FBAcHSyr4YYaEhFicBgAAAABQ26WlpSkqKsrVRy+EUn8Rhafch4SEUOoBAAAAANWmNJeAM1EeAAAAAABuilIPAAAAAICbotQDAAAAAOCmuKYeAAAAACCHw6G8vDyrY3gELy8veXt7V8pt0yn1AAAAAODhMjIydPDgQZmmaXUUjxEQEKDGjRvL19e3Qu9DqQcAAAAAD+ZwOHTw4EEFBAQoPDy8UkaPcX6maSo3N1cnTpzQvn371LJlS9ls5b8ynlIPAAAAAB4sLy9PpmkqPDxc/v7+VsfxCP7+/vLx8VFCQoJyc3Pl5+dX7vdiojwAAAAAACP01awio/NF3qdS3gUAAAAAAFQ7Sj0AAAAAAG6KUg8AAAAAgJui1AMAAAAA3NKJEyc0btw4NW3aVHa7XY0aNdLgwYO1atUqSVKzZs30yiuvnHf7BQsWqHfv3goNDVVwcLDat2+vCRMmFFnn9OnTevrpp9WqVSvZ7XbVr19fI0aM0G+//VZkvb/+9a8yDEOGYcjb21vNmjXTxIkTlZGRUdkfuwhmvwcAAAAAuKXhw4crNzdXs2fPVkxMjI4dO6Yff/xRSUlJF932xx9/1MiRIzVt2jRdf/31MgxDv//+u3744QfXOjk5ObriiiuUmJiof/7zn+rVq5eOHTum6dOnq1evXlq8eLF69+7tWr99+/ZavHix8vPztWrVKo0ZM0ZZWVl66623quTzS5R6AAAAAMBZTNPU6TyHJfv29/Eq9Sz8KSkpWrFihZYuXaoBAwZIkqKjo9WzZ89Sbf/VV1+pb9++evzxx13LWrVqpRtuuMH1/JVXXtEvv/yiX3/9VZ07d3btY/78+erVq5fGjh2rrVu3ujJ7e3urUaNGkqSRI0fqxx9/1MKFCyn1AAAAAIDqcTrPoXZTF1my79//b7ACfEtXU4OCghQUFKQvvvhCvXv3lt1uL9O+GjVqpHnz5mnr1q3q0KFDievMmzdPV155pavQF7LZbJo4caJGjRqlTZs2qUuXLiVu7+/vr9zc3DLlKiuuqQcAAAAAuB1vb2/NmjVLs2fPVlhYmPr27asnn3xSmzdvLtX2jzzyiHr06KGOHTuqWbNmuvXWW/Xee+8pJyfHtc7OnTvVtm3bErcvXL5z584SX9+wYYPmzZunyy67rIyfrGwYqQcAAAAAuPj7eOn3/xts2b7LYvjw4Ro6dKhWrFih1atX69tvv9ULL7ygf//73xo9evQFtw0MDNQ333yjPXv2aMmSJVq9erUmTZqkV199Vb/88osCAgIkFVyOcCG+vr6uv2/ZskVBQUFyOBzKzc3V0KFDNWPGjDJ9prKi1AMAAAAAXAzDKPUp8DWBn5+frrzySl155ZV66qmndO+99+rpp5++aKkvFBsbq9jYWN177736n//5H7Vq1Uoff/yx7rnnHrVs2VLbtm0rcbvC5a1atXIta926tRYuXChvb29FREQUKfxVhdPvAQAAAAC1Rrt27ZSZmVmubZs1a6aAgADX9rfddpsWL16sTZs2FVnP6XTq5ZdfVvfu3dWuXTvXcl9fX7Vo0ULNmjWrlkIvMVIPAAAAAHBDSUlJGjFihMaMGaNOnTopODhY69ev1wsvvKBhw4a51jt06JDi4+OLbBsdHa1XX31VWVlZuuaaaxQdHa2UlBS99tprysvL05VXXilJmjhxor788ktdd911RW5p9/e//127du3Szz//XJ0fuUSUegAAAACA2wkKClKvXr308ssva8+ePcrLy1NUVJTuu+8+Pfnkk671/vGPf+gf//hHkW0/+OADDRgwQK+//rruuusuHTt2THXq1FFcXJy+//57tW7dWlLBqf0//vijpk+frilTpighIUH5+flq0aKFtm7dqsjIyGr9zCUxzItd9e/h0tLSFBoaqtTUVIWEhFgdBwAAAAAqVXZ2tvbt26fmzZvLz8/P6jg13rfffqsbb7xR//jHPzR+/Phyv8+Ffu5l6aFcUw8AAAAAQCldffXV+vbbb5WcnKyTJ09aHYfT72uLY2nZ+nzjIY3t11y+3hyrAQAAAICqMmjQIA0aNMjqGJIo9bWC02nqxtdX6XBqtprWDdDQTo2tjgQAAAAAqAYM6dYCNpuh4d0KJmiYuybB4jQAAAAAgOpCqa8lbu3ZVDZD+nlPkvacyLA6DgAAAAA3wxzq1auyft6U+lqiSZi/LmvTQJI0b02ixWkAAAAAuAsvLy9JUm5ursVJPEtWVpYkycfHp0LvwzX1tcioXtFavO24PttwUI8Pbi0/Hy+rIwEAAACo4by9vRUQEKATJ07Ix8dHNhtjv1XJNE1lZWXp+PHjCgsLcx1UKS9KfS1yaatwNQnz16GU0/pm8xHXdfYAAAAAcD6GYahx48bat2+fEhKYo6u6hIWFqVGjRhV+H0p9LeJlM3R7r6Z6cdEOzVmTQKkHAAAAUCq+vr5q2bIlp+BXEx8fnwqP0Bei1Ncyt3SP0ss/7NSviSn67XCq2keEWh0JAAAAgBuw2Wzy8/OzOgbKiIslapnwYLsGdyg4hYMJ8wAAAACgdqPU10KjejWVJH3x6yFl5ORbnAYAAAAAUFUo9bXQJTH1FBMeqMxch76MP2R1HAAAAABAFaHU10KGYWhUr2hJ0pzViTJN0+JEAAAAAICqQKmvpYZ3bSK7t03bjqTp1wMpVscBAAAAAFQBSn0tFRbgq2s7RUiS5q5mwjwAAAAAqI0o9bXYqN4FE+Z9vfmwUrK43yQAAAAA1DZuU+qnTZumPn36KCAgQGFhYaXa5vPPP9dVV12levXqyTAMxcfHV2nGmiYuKkztGocoJ9+pzzYctDoOAAAAAKCSuU2pz83N1YgRIzRu3LhSb5OZmal+/frp+eefr8JkNZdhGK7R+nlrmDAPAAAAAGobb6sDlNYzzzwjSZo1a1apt7nzzjslSfv376+CRO5hWJcm+vs327T3ZKZ+2ZukPrH1rY4EAAAAAKgkbjNSX11ycnKUlpZW5OHOguzeuiGuiSRp7homzAMAAACA2oRSf47p06crNDTU9YiKirI6UoUV3rN+0dajOp6ebXEaAAAAAEBlsbTUT548WYZhXPCxffv2as00ZcoUpaamuh4HDhyo1v1XhXYRIeraNEz5TlOfrmfCPAAAAACoLSy9pn7SpEkaPXr0BdeJiYmpnjBn2O122e32at1ndRjVK1obE1M0b02iHhwQKy+bYXUkAAAAAEAFWVrqw8PDFR4ebmUEjzG0U2P939e/61DKaS3feUKD2jSwOhIAAAAAoILc5pr6xMRExcfHKzExUQ6HQ/Hx8YqPj1dGRoZrnTZt2mjBggWu58nJyYqPj9fvv/8uSdqxY4fi4+N19OjRas9vNT8fL93cLVKSNGd1gsVpAAAAAACVwW1K/dSpUxUXF6enn35aGRkZiouLU1xcnNavX+9aZ8eOHUpNTXU9X7hwoeLi4jR06FBJ0q233qq4uDjNnDmz2vPXBLf3Krhn/U87juvgqSyL0wAAAAAAKsowTdO0OkRNlpaWptDQUKWmpiokJMTqOBV2+zur9fOeJD1yWQtNuqq11XEAAAAAAOcoSw91m5F6VI7C29t9tO6A8hxOi9MAAAAAACqCUu9hrmzXUPWD7DqRnqPFvx+zOg4AAAAAoAIo9R7G19umW3tESZLmrGHCPAAAAABwZ5R6D3RrzygZhrRqd5L2nsi4+AYAAAAAgBqJUu+BIusEaFDrgvvUf7g20eI0AAAAAIDyotR7qFFnbm/36YaDys5zWJwGAAAAAFAelHoPNbB1AzUJ81dKVp7+u+WI1XEAAAAAAOVAqfdQXjZDt/UsmDBv7hpOwQcAAAAAd0Sp92C3dI+St83QhoRT2nYkzeo4AAAAAIAyotR7sAYhfrqqfUNJ0jxG6wEAAADA7VDqPdyoXtGSpAW/HlJmTr7FaQAAAAAAZUGp93B9Yusppn6gMnLy9WX8YavjAAAAAADKgFLv4QzD0O1nbm83d02CTNO0OBEAAAAAoLQo9dDwrpHy9bbpt8Np2nQw1eo4AAAAAIBSotRDdQJ9dW3HxpKkOasTLE4DAAAAACgtSj0kSaN6F0yY99Wmw0rNyrM4DQAAAACgNCj1kCR1bRqmNo2ClZPv1PyNB62OAwAAAAAoBUo9JBVMmFc4Ws+EeQAAAADgHij1cLmhS4QCfL2050Sm1uxLtjoOAAAAAOAiKPVwCfbz0Q1xTSQxYR4AAAAAuANKPYq4vWfBPesX/XZUJ9JzLE4DAAAAALgQSj2K6NAkVF2iwpTnMPXphgNWxwEAAAAAXAClHsWM6lUwWj9vTaKcTibMAwAAAICailKPYq7rHKEQP28dPHVay3adsDoOAAAAAOA8KPUoxs/HSzd3i5IkzV2daHEaAAAAAMD5UOpRotvPnIL/0/ZjOpxy2uI0AAAAAICSUOpRohYNgtQ7pq6cpvTROibMAwAAAICaiFKP8xrVK1qS9NHaROU5nBanAQAAAACci1KP8xrcvpHqB/nqeHqOftx2zOo4AAAAAIBzUOpxXr7eNt3S/cyEeWuYMA8AAAAAahpKPS7otp5NZRjSil0ntf9kptVxAAAAAABnodTjgqLqBmhAq3BJ0odrGa0HAAAAgJqEUo+LuuPMhHmfrD+g7DyHxWkAAAAAAIUo9bioQW0aKCLUT6ey8vTd1qNWxwEAAAAAnEGpx0V52Qzd2rOpJGnumgSL0wAAAAAAClHqUSoje0TJy2Zo3f5T2nE03eo4AAAAAABR6lFKDUP8dFW7hpIYrQcAAACAmoJSj1IbdWbCvM83HlJmTr7FaQAAAAAAlHqUWp/YempWL0AZOfn6atNhq+MAAAAAgMej1KPUbDZDt/cqnDCPe9YDAAAAgNUo9SiTm7tFydfbpi2HUrXpQIrVcQAAAADAo1HqUSZ1A301tGNjSUyYBwAAAABWo9SjzEadOQV/4abDSj2dZ3EaAAAAAPBclHqUWbfoOmrdMFjZeU4t2HjQ6jgAAAAA4LEo9SgzwzA0qnfBaP2cNYkyTdPiRAAAAADgmSj1KJcb45oowNdLu49naO2+ZKvjAAAAAIBHotSjXIL9fDSsS4Qkbm8HAAAAAFah1KPcbu8ZLUn6dusRnczIsTgNAAAAAHgeSj3KrWNkqDpHhirPYeqzDUyYBwAAAADVjVKPChnVu2C0ft6aRDmdTJgHAAAAANWJUo8Kua5ThIL9vJWYnKUVu09aHQcAAAAAPAqlHhXi7+ul4V0jJUlzVydYnAYAAAAAPAulHhU2qlfBPet/3H5cR1JPW5wGAAAAADwHpR4V1rJhsHo1ryuH09RHaw9YHQcAAAAAPAalHpWicMK8j9YlKt/htDgNAAAAAHgGSj0qxeD2DVUv0FfH0nL04/bjVscBAAAAAI9AqUelsHt7aUT3KEnS3DWJFqcBAAAAAM9AqUelub1nUxmGtHznCSUkZVodBwAAAABqPUo9Kk3TegG6tGW4JGneWkbrAQAAAKCqUepRqQpvb/fp+oPKyXdYnAYAAAAAajdKPSrVZW0aqFGIn5Izc/Xd1qNWxwEAAACAWs1tSv20adPUp08fBQQEKCws7KLr5+Xl6S9/+Ys6duyowMBARURE6K677tLhw4erPqwH8/ay6daeZybMW80p+AAAAABQldym1Ofm5mrEiBEaN25cqdbPysrSxo0b9dRTT2njxo36/PPPtWPHDl1//fVVnBS39mgqL5uhtfuTtfNYutVxAAAAAKDW8rY6QGk988wzkqRZs2aVav3Q0FD98MMPRZbNmDFDPXv2VGJiopo2bVrZEXFGo1A/XdG2gRb9dkzz1iTqr9e3tzoSAAAAANRKbjNSXxlSU1NlGMYFT9/PyclRWlpakQfKblSvaEnS/I0HlZWbb3EaAAAAAKidPKbUZ2dn6y9/+Ytuu+02hYSEnHe96dOnKzQ01PWIioqqxpS1R78W9RVdL0Dp2fn6etMRq+MAAAAAQK1kaamfPHmyDMO44GP79u0V3k9eXp5uueUWmaapN99884LrTpkyRampqa7HgQMHKrx/T2SzGbq9Z8ElDnPWJFicBgAAAABqJ0uvqZ80aZJGjx59wXViYmIqtI/CQp+QkKCffvrpgqP0kmS322W32yu0TxS4uVuk/vn9Tm0+mKrNB1PUKTLM6kgAAAAAUKtYWurDw8MVHh5eZe9fWOh37dqlJUuWqF69elW2LxRXL8iuqzs20pfxhzVvTSKlHgAAAAAqmdtcU5+YmKj4+HglJibK4XAoPj5e8fHxysjIcK3Tpk0bLViwQFJBob/55pu1fv16zZ07Vw6HQ0ePHtXRo0eVm5tr1cfwOIUT5n0Zf1hp2XkWpwEAAACA2sVtbmk3depUzZ492/U8Li5OkrRkyRINHDhQkrRjxw6lpqZKkg4dOqSFCxdKkrp06VLkvc7eBlWrR7M6atUwSDuPZWjBxkO6u08zqyMBAAAAQK1hmKZpWh2iJktLS1NoaKhSU1Mvej0+Sjb75/16euFvatUwSIsmXCrDMKyOBAAAAAA1Vll6qNucfg/3dWPXJvL38dLOYxlan3DK6jgAAAAAUGtQ6lHlQvx8dH3nCEnS3NXc3g4AAAAAKgulHtViVO+Ce9b/d8tRJWXkWJwGAAAAAGoHSj2qRafIMHWKDFWuw6nPNhy0Og4AAAAA1AqUelSbUb0KRuvnrU2U08n8jAAAAABQUZR6VJvrOkco2O6thKQsrdpz0uo4AAAAAOD2KPWoNgG+3rqpaxNJ0hwmzAMAAACACqPUo1qN6h0tSVq87biOpmZbnAYAAAAA3BulHtWqVcNg9WxWVw6nqY/XHbA6DgAAAAC4NUo9ql3h7e0+WpeofIfT4jQAAAAA4L4o9ah2Qzo0Ut1AXx1JzdaSHSesjgMAAAAAbotSj2pn9/bSiO6RkpgwDwAAAAAqglIPS9zes+AU/OW7TigxKcviNAAAAADgnij1sER0vUD1b1lfpil9uC7R6jgAAAAA4JYo9bDMqF4Ft7f7ZN0B5eYzYR4AAAAAlBWlHpa5om0DNQyxKykzV9/9dtTqOAAAAADgdij1sIy3l0239ii4tn4uE+YBAAAAQJlR6mGpW3tGyWZIa/Yla/fxdKvjAAAAAIBbodTDUo1D/XV524aSpLlrmDAPAAAAAMqCUg/LjepVcAr+/A0HdTrXYXEaAAAAAHAflHpY7tKW4Yqq66+07Hx9tfmw1XEAAAAAwG1Q6mE5m83Q7T0Lbm/HKfgAAAAAUHqUetQII7pHysfL0KYDKdp6KNXqOAAAAADgFij1qBHqB9k1pENjSdLcNdzeDgAAAABKg1KPGuOOMxPmfRl/WGnZeRanAQAAAICaj1KPGqNn87pq0SBIWbkOffnrIavjAAAAAECNR6lHjWEYhuv2dnPXJMo0TYsTAQAAAEDNRqlHjXJT10j5+di0/Wi6NiaesjoOAAAAANRolHrUKKH+Prq+c4Qkac5qbm8HAAAAABdCqUeNM6pXwT3rv9lyRMmZuRanAQAAAICai1KPGqdTZKg6NAlRbr5T8zcctDoOAAAAANRYlHrUOAUT5hWM1s9bmyinkwnzAAAAAKAklHrUSNd3jlCw3Vv7Tmbq5z1JVscBAAAAgBqJUo8aKdDurRu7NpEkzV2TYHEaAAAAAKiZKPWosW4/c8/6738/pmNp2RanAQAAAICah1KPGqtNoxB1j64jh9PUJ+sOWB0HAAAAAGocSj1qtFG9C0brP1ybKAcT5gEAAABAEZR61GhXd2isOgE+OpyarSXbj1sdBwAAAABqFEo9ajQ/Hy+N6B4liQnzAAAAAOBclHrUeLf1LDgFf+nOEzqQnGVxGgAAAACoOSj1qPGa1w9Uvxb1ZZoF19YDAAAAAApQ6uEW7jgzYd4n6w8oN99pcRoAAAAAqBko9XALl7dtqAbBdp3MyNX3vx+1Og4AAAAA1AiUergFHy+bbu1xZsK81ZyCDwAAAAASpR5u5NaeTWUzpF/2Jmn38Qyr4wAAAACA5Sj1cBsRYf66rE1DSdK8NYzWAwAAAAClHm5l1JkJ8z7bcEDZeQ6L0wAAAACAtSj1cCuXtgxXZB1/pWXn6+vNR6yOAwAAAACWotTDrXjZDN3Ws2C0fu6aBIvTAAAAAIC1KPVwO7d0j5KPl6FfE1P02+FUq+MAAAAAgGUo9XA74cF2DW7fSJI0lwnzAAAAAHgwSj3c0qhe0ZKkL389pIycfIvTAAAAAIA1KPVwS71j6iomPFCZuQ598eshq+MAAAAAgCUo9XBLhmG4RuvnrE6QaZoWJwIAAACA6keph9u6uWuk7N42bT+aro2JKVbHAQAAAIBqR6mH2woN8NF1nSMkcXs7AAAAAJ6JUg+3NqpXwT3rv958RClZuRanAQAAAIDqRamHW+sSFaZ2jUOUm+/UZxsOWh0HAAAAAKoVpR5uzTAM3dG7YMK8uWsSmTAPAAAAgEeh1MPtXd8lQkF2b+07malf9iRZHQcAAAAAqo3blPpp06apT58+CggIUFhYWKm2+etf/6o2bdooMDBQderU0RVXXKE1a9ZUbVBUuyC7t26IK5wwL9HiNAAAAABQfdym1Ofm5mrEiBEaN25cqbdp1aqVZsyYoS1btmjlypVq1qyZrrrqKp04caIKk8IKhfesX/TbUR1Pz7Y4DQAAAABUD8N0s4uQZ82apQkTJiglJaXM26alpSk0NFSLFy/W5ZdfXqZtUlNTFRISUuZ9ovoMf/NnbUg4pT9f1UrjL2tpdRwAAAAAKJey9FC3GamvqNzcXL399tsKDQ1V586dz7teTk6O0tLSijzgHgpvb/fh2gNyON3qWBUAAAAAlEutL/Vff/21goKC5Ofnp5dfflk//PCD6tevf971p0+frtDQUNcjKiqqGtOiIq7p2FhhAT46lHJay3YetzoOAAAAAFQ5S0v95MmTZRjGBR/bt2+v0D4GDRqk+Ph4/fzzzxoyZIhuueUWHT9+/sI3ZcoUpaamuh4HDhyo0P5Rffx8vHRz10hJ0tzVTJgHAAAAoPbztnLnkyZN0ujRoy+4TkxMTIX2ERgYqBYtWqhFixbq3bu3WrZsqXfffVdTpkwpcX273S673V6hfcI6t/dqqn+v3KefdhzXwVNZiqwTYHUkAAAAAKgylpb68PBwhYeHV+s+nU6ncnJyqnWfqD4x4UHq26KeVu1O0kdrD+jPg1tbHQkAAAAAqozbXFOfmJio+Ph4JSYmyuFwKD4+XvHx8crIyHCt06ZNGy1YsECSlJmZqSeffFKrV69WQkKCNmzYoDFjxujQoUMaMWKEVR8D1aDw9nYfrTugPIfT4jQAAAAAUHUsHakvi6lTp2r27Nmu53FxcZKkJUuWaODAgZKkHTt2KDU1VZLk5eWl7du3a/bs2Tp58qTq1aunHj16aMWKFWrfvn2150f1ubJdQ4UH23UiPUc//H5M13RsbHUkAAAAAKgSbnef+urGferd0z8W7dCMJbvVJ7ae5t3X2+o4AAAAAFBq3KceHu+2Xk1lM6Sf9yRpz4mMi28AAAAAAG6IUo9aqUmYvwa1biBJ+nANt7cDAAAAUDtR6lFrjerdVJL02caDys5zWJwGAAAAACofpR611oBWDdQkzF8pWXn6ZvMRq+MAAAAAQKWj1KPW8rIZur1XwWj93DUJFqcBAAAAgMpHqUetNqJ7pLxthjYmpuj3w2lWxwEAAACASkWpR63WINhPg9s3kiTNW8toPQAAAIDahVKPWm/UmVPwF2w8pIycfIvTAAAAAEDlodSj1rsktp5i6gcqM9ehL+MPWR0HAAAAACoNpR61nmH8MWHenNWJMk3T4kQAAAAAUDko9fAIN3eLlK+3TduOpCn+QIrVcQAAAACgUlDq4RHCAnx1bafGkqS5axItTgMAAAAAlYNSD48xqle0JOmrTYeVkpVrcRoAAAAAqDhKPTxG16Zhats4RDn5Ts3fyIR5AAAAANwfpR4ewzAM1+3t5q5JYMI8AAAAAG6PUg+PckNcEwX6emnviUyt3ptsdRwAAAAAqBBKPTxKkN1bw+KaSJLmrEmwOA0AAAAAVAylHh7njjMT5i3aelQn0nMsTgMAAAAA5Ueph8dpFxGiuKZhynea+mT9AavjAAAAAEC5UerhkQpvb/fh2kQ5nEyYBwAAAMA9Uerhka7t1Fih/j46eOq0lu88YXUcAAAAACgXSj08kp+Pl27uFimp4PZ2AAAAAOCOKPXwWLefuWf9T9uP61DKaYvTAAAAAEDZUerhsWLDg3RJTD05TenjtYlWxwEAAACAMqPUw6ON6l0wWv/RugPKczgtTgMAAAAAZUOph0e7ql0j1Q+y63h6jhb/fszqOAAAAABQJpR6eDRfb5tG9iicMI9T8AEAAAC4F0o9PN6tPZrKMKSVu09q38lMq+MAAAAAQKlR6uHxouoGaGCrcEnSh0yYBwAAAMCNUOoBSaN6RUuSPl1/QNl5DovTAAAAAEDpUOoBSYPaNFBEqJ9OZeXp261HrI4DAAAAAKVCqQckedkM3daz4PZ2c1dzCj4AAAAA90CpB84Y2SNKXjZD6xNOafvRNKvjAAAAAMBFUeqBMxqE+Omqdg0lMVoPAAAAwD1Q6oGz3NG7YMK8Bb8eUmZOvsVpAAAAAODCKPXAWS6Jqafm9QOVkZOvhZsOWx0HAAAAAC6IUg+cxWYzdPuZCfPmrE6QaZoWJwIAAACA86PUA+e4uVukfL1t+u1wmjYdTLU6DgAAAACcF6UeOEedQF9d27GxJGnu6gSL0wAAAADA+VHqgRKM6l1wCv5Xmw8rNSvP4jQAAAAAUDJKPVCCrk3rqE2jYGXnOfX5rwetjgMAAAAAJaLUAyUwDEOjehWM1s9dk8iEeQAAAABqJEo9cB43xDVRgK+Xdh/P0Jp9yVbHAQAAAIBiKPXAeQT7+WhYlyaSCkbrAQAAAKCmodQDF1B4Cv53W4/oZEaOxWkAAAAAoChKPXABHZqEqnNUmPIcpj5dz4R5AAAAAGoWSj1wEYWj9fPWJsjpZMI8AAAAADUHpR64iOs6RSjEz1sHkk9r+a4TVscBAAAAABdKPXAR/r5eGt4tUhIT5gEAAACoWSj1QCkUnoL/47ZjOpJ62uI0AAAAAFCAUg+UQosGwerVvK6cpvTh2gNWxwEAAAAASZR6oNTu6B0tSfpobaLyHE6L0wAAAAAApR4otcHtG6leoK+Op+fox23HrY4DAAAAAJR6oLR8vW26pUeUJGnumgSL0wAAAAAApR4ok9t7NpVhSCt2ndT+k5lWxwEAAADg4Sj1QBlE1Q3QgFbhkqQP13J7OwAAAADWotQDZTSqV8GEeZ+sP6CcfIfFaQAAAAB4Mko9UEaDWoercaifTmXl6butR62OAwAAAMCDUeqBMvL2sunWHk0lSXNXcwo+AAAAAOtQ6oFyuLVnlLxshtbuT9aOo+lWxwEAAADgoSj1QDk0DPHTlW0bSpLmcXs7AAAAABZxm1I/bdo09enTRwEBAQoLCyvz9g8++KAMw9Arr7xS6dngmUb1LjgF//ONh5SVm29xGgAAAACeyG1KfW5urkaMGKFx48aVedsFCxZo9erVioiIqIJk8FR9Y+srul6A0nPy9dWmw1bHAQAAAOCB3KbUP/PMM5o4caI6duxYpu0OHTqkRx55RHPnzpWPj08VpYMnstkM3d6zYLR+DhPmAQAAALCA25T68nA6nbrzzjv1+OOPq3379qXaJicnR2lpaUUewPmM6B4lXy+bthxK1eaDKVbHAQAAAOBhanWpf/755+Xt7a1HH3201NtMnz5doaGhrkdUVFQVJoS7qxvoq2s6NpLE7e0AAAAAVD9LS/3kyZNlGMYFH9u3by/Xe2/YsEGvvvqqZs2aJcMwSr3dlClTlJqa6nocOHCgXPuH5xjVO1qStHDTYaWezrM4DQAAAABP4m3lzidNmqTRo0dfcJ2YmJhyvfeKFSt0/PhxNW3a1LXM4XBo0qRJeuWVV7R///4St7Pb7bLb7eXaJzxT9+g6atUwSDuPZWjBxoMa3be51ZEAAAAAeAhLS314eLjCw8Or5L3vvPNOXXHFFUWWDR48WHfeeafuueeeKtknPJNhGLqjd7Smfvmb5q5J1N19mpXp7BAAAAAAKC+3uaY+MTFR8fHxSkxMlMPhUHx8vOLj45WRkeFap02bNlqwYIEkqV69eurQoUORh4+Pjxo1aqTWrVtb9TFQS90Q10T+Pl7adTxD6/afsjoOAAAAAA/hNqV+6tSpiouL09NPP62MjAzFxcUpLi5O69evd62zY8cOpaamWpgSnirEz0fDukRIkuauSbA4DQAAAABPYZimaVodoiZLS0tTaGioUlNTFRISYnUc1GBbDqbquhkr5etl0y9TLlO9IOZmAAAAAFB2ZemhbjNSD9R0HSND1TkyVLkOpz7dcNDqOAAAAAA8AKUeqESjehXc3m7emkQ5nZwEAwAAAKBqUeqBSnRt58YK9vNWYnKWVu4+aXUcAAAAALUcpR6oRAG+3hreNVISE+YBAAAAqHqUeqCSjerVVJK0eNtxHU3NtjgNAAAAgNqMUg9UspYNg9WzeV05nKY+WpdodRwAAAAAtRilHqgChaP1H609oHyH0+I0AAAAAGorSj1QBYZ0aKS6gb46mpatn7YftzoOAAAAgFqKUg9UAbu3l0Z0L5gwb84aTsEHAAAAUDUo9UAVGdWz4J71y3eeUGJSlsVpAAAAANRGlHqgijStF6BLW4VLkuatZbQeAAAAQOWj1ANVqHDCvE/XH1BOvsPiNAAAAABqG0o9UIUub9NAjUL8lJSZq++2HrU6DgAAAIBahlIPVCFvL5tu7RklSZrLhHkAAAAAKhmlHqhit/ZoKi+bobX7krXrWLrVcQAAAADUIpR6oIo1CvXT5W0aSGK0HgAAAEDlotQD1WBU74Lb283feFBZufkWpwEAAABQW1DqgWrQv0V9Na0boPTsfH296YjVcQAAAADUEpR6oBrYbIZuP3N7uzlrEmSapsWJAAAAANQG5Sr1mZmZlZ0DqPVGdIuUr7dNmw+mau2+ZKvjAAAAAKgFylXqGzZsqDFjxmjlypWVnQeoteoF2TWiW6QkacaS3RanAQAAAFAblKvUz5kzR8nJybrsssvUqlUrPffcczp8+HBlZwNqnQcHxMrLZmjFrpPafDDF6jgAAAAA3Fy5Sv0NN9ygL774QocOHdKDDz6oefPmKTo6Wtdee60+//xz5eczuzdQkqi6ARrWOUKS9Dqj9QAAAAAqqEIT5YWHh+uxxx7T5s2b9dJLL2nx4sW6+eabFRERoalTpyorK6uycgK1xriBsZKkRb8d065j6RanAQAAAODOKlTqjx07phdeeEHt2rXT5MmTdfPNN+vHH3/UP//5T33++ee64YYbKikmUHu0bBisIe0bSZLeWLrH4jQAAAAA3Jl3eTb6/PPP9f7772vRokVq166dHnroId1xxx0KCwtzrdOnTx+1bdu2snICtcrDg1rou9+OauGmw5p4RSs1rRdgdSQAAAAAbqhcI/X33HOPIiIitGrVKsXHx2v8+PFFCr0kRURE6H/+538qIyNQ63SMDNWlrcLlcJqauZzRegAAAADlY5imaZZ1o6ysLAUEeMbIYlpamkJDQ5WamqqQkBCr46AWWbM3SSPfXi1fL5tW/GWQGob4WR0JAAAAQA1Qlh5arpH6/Px8paWlFXukp6crNze3XKEBT9Mrpp56NKujXIdT7yzfa3UcAAAAAG6oXKU+LCxMderUKfYICwuTv7+/oqOj9fTTT8vpdFZ2XqBWeWhQC0nS3DWJOpXJATEAAAAAZVOuUj9r1ixFREToySef1BdffKEvvvhCTz75pJo0aaI333xT999/v1577TU999xzlZ0XqFUGtgpX+4gQnc5z6P1V+6yOAwAAAMDNlOua+ssvv1wPPPCAbrnlliLLP/nkE7311lv68ccf9cEHH2jatGnavn17pYW1AtfUo6r9d8sRPTR3o0L8vLVq8mUK9vOxOhIAAAAAC1X5NfU///yz4uLiii2Pi4vTL7/8Iknq16+fEhMTy/P2gEcZ0r6RYsMDlZadrzmr+Z0BAAAAUHrlKvVRUVF69913iy1/9913FRUVJUlKSkpSnTp1KpYO8AA2m6FxAwuurX935V5l5zksTgQAAADAXXiXZ6N//OMfGjFihL799lv16NFDkrR+/Xpt375dn332mSRp3bp1GjlyZOUlBWqxYV0i9PIPO3Uo5bQ+XndAd/dpZnUkAAAAAG6gXNfUS9L+/fv11ltvaceOHZKk1q1b64EHHlCzZs0qM5/luKYe1eWDX/brqS9/U0Son5Y+Pki+3uU6kQYAAACAmytLDy3zSH1eXp6GDBmimTNnavr06eUOCaCoEd2j9OqPu3U4NVtfxh/SiO5RVkcCAAAAUMOVeSjQx8dHmzdvroosgEfz8/HSff2bS5LeXLpHDme5TqIBAAAA4EHKdX7vHXfcUeJEeQAqZlTvaIX6+2jvyUx9t/Wo1XEAAAAA1HDlmigvPz9f7733nhYvXqxu3bopMDCwyOsvvfRSpYQDPE2Q3Vuj+zTTqz/u0owlu3VNx0YyDMPqWAAAAABqqHKV+q1bt6pr166SpJ07dxZ5jQICVMw9fZvpnRV7te1ImpbuOKFBbRpYHQkAAABADVWuUr9kyZLKzgHgjLAAX93RO1pvL9+rGUt2a2DrcA6WAQAAAChRhe6ZtXv3bi1atEinT5+WJJXz7ngAznFvv+by9bZpQ8IprdmXbHUcAAAAADVUuUp9UlKSLr/8crVq1UrXXHONjhw5IkkaO3asJk2aVKkBAU/UIMRPt3SPlCS9vmS3xWkAAAAA1FTlKvUTJ06Uj4+PEhMTFRAQ4Fo+cuRIfffdd5UWDvBkD1waKy+boRW7TmrTgRSr4wAAAACogcpV6r///ns9//zzioyMLLK8ZcuWSkhIqJRggKeLqhugYV0iJDFaDwAAAKBk5Sr1mZmZRUboCyUnJ8tut1c4FIACDw2MlWFI3/9+TDuPpVsdBwAAAEANU65S379/f/3nP/9xPTcMQ06nUy+88IIGDRpUaeEAT9eiQbCGtG8kSXqD0XoAAAAA5yjXLe1eeOEFXX755Vq/fr1yc3P1xBNP6LffflNycrJWrVpV2RkBj/bQwBb6dutRLdx0WI9d2VpN6xU/SwYAAACAZyrXSH2HDh20c+dO9evXT8OGDVNmZqZuuukm/frrr4qNja3sjIBH6xgZqgGtwuU0pTeX7bE6DgAAAIAaxDC5ufwFpaWlKTQ0VKmpqQoJCbE6DjzU2n3JuuWtX+TrZdPyJwapUaif1ZEAAAAAVJGy9NBynX4vSSkpKVq7dq2OHz8up9NZ5LW77rqrvG8LoAQ9m9dVz2Z1tXZ/st5ZsVdPXdvO6kgAAAAAaoByjdR/9dVXGjVqlDIyMhQSEiLDMP54Q8NQcnJypYa0EiP1qCmW7jiu0e+vk7+Pl1ZNvkx1A32tjgQAAACgCpSlh5brmvpJkyZpzJgxysjIUEpKik6dOuV61KZCD9QkA1qFq0OTEJ3Oc+j9VfusjgMAAACgBihXqT906JAeffTREu9VD6BqGIahhwe2kCTN+nm/0rPzLE4EAAAAwGrlKvWDBw/W+vXrKzsLgIsY3L6RYsMDlZ6drw9WJ1gdBwAAAIDFyjVR3tChQ/X444/r999/V8eOHeXj41Pk9euvv75SwgEoymYz9NDAFpr06Sa9u2Kf7unTXP6+XlbHAgAAAGCRck2UZ7Odf4DfMAw5HI4KhapJmCgPNU2ew6lB/1iqg6dO66/XtdPovs2tjgQAAACgElX5RHlOp/O8j9pU6IGayMfLpgcGxEqS3l6+V7n5zotsAQAAAKC2KlOpv+aaa5Samup6/txzzyklJcX1PCkpSe3acf9soKqN6Bap8GC7Dqdm64tfD1kdBwAAAIBFylTqFy1apJycHNfzv//970VuYZefn68dO3ZUXrqzTJs2TX369FFAQIDCwsJKtc3o0aNlGEaRx5AhQ6okH1Cd/Hy8dF//gtPu31y2Rw5nma+iAQAAAFALlKnUn3v5fTkuxy+33NxcjRgxQuPGjSvTdkOGDNGRI0dcjw8//LCKEgLVa1SvaIX6+2jfyUz9d8sRq+MAAAAAsEC5Zr+3wjPPPCNJmjVrVpm2s9vtatSoURUkAqwVaPfWPX2b6ZXFu/T6kt26tlNjGYZhdSwAAAAA1ahMI/WFp7Cfu6wmW7p0qRo0aKDWrVtr3LhxSkpKuuD6OTk5SktLK/IAaqrRfZop0NdL24+m66ftx62OAwAAAKCalWmk3jRNjR49Wna7XZKUnZ2tBx98UIGBgZJU5Hr7mmDIkCG66aab1Lx5c+3Zs0dPPvmkrr76av3yyy/y8ir53t7Tp093nRUA1HRhAb66o3e03lq+VzOW7NZlbRrU+ANtAAAAACpPme5Tf88995Rqvffff79U602ePFnPP//8BdfZtm2b2rRp43o+a9YsTZgwocis+6W1d+9excbGavHixbr88stLXCcnJ6fIwYm0tDRFRUVxn3rUWMfTs9Xv+SXKzXdq3n291Ce2vtWRAAAAAFRAWe5TX6aR+tKW9dKaNGmSRo8efcF1YmJiKm1/MTExql+/vnbv3n3eUm+3211nIgDuoEGwn0Z2j9IHqxP0xpI9lHoAAADAg1g6UV54eLjCw8OrbX8HDx5UUlKSGjduXG37BKrDAwNi9OHaRK3cfVLxB1LUJSrM6kgAAAAAqkGZJsqzUmJiouLj45WYmCiHw6H4+HjFx8crIyPDtU6bNm20YMECSVJGRoYef/xxrV69Wvv379ePP/6oYcOGqUWLFho8eLBVHwOoEpF1AjSsSxNJ0utLdlucBgAAAEB1cZtSP3XqVMXFxenpp59WRkaG4uLiFBcXp/Xr17vW2bFjh1JTUyVJXl5e2rx5s66//nq1atVKY8eOVbdu3bRixQpOr0etNG5grAxD+uH3Y9p+lLs2AAAAAJ6gTBPleaKyTFAAWO2huRv03y1HNaxLhF69Nc7qOAAAAADKoSw91G1G6gFc3EMDW0iSvtp0WPtPZlqcBgAAAEBVo9QDtUiHJqEa2DpcTlN6a/keq+MAAAAAqGKUeqCWGT+oYLT+sw0HdST1tMVpAAAAAFQlSj1Qy3RvVlc9m9dVnsPUO8v3WR0HAAAAQBWi1AO1UOFo/by1CUrKyLE4DQAAAICqQqkHaqH+LeurY5NQZec59f6q/VbHAQAAAFBFKPVALWQYhh4+M1o/+5f9SsvOszgRAAAAgKpAqQdqqavaNVTLBkFKz87XB78kWB0HAAAAQBWg1AO1lM1m6KFBsZKk91bu0+lch8WJAAAAAFQ2Sj1Qi13XKUJRdf2VlJmrj9YlWh0HAAAAQCWj1AO1mLeXTQ8OKBitf3v5XuXmOy1OBAAAAKAyUeqBWm5410g1CLbrSGq2Fvx60Oo4AAAAACoRpR6o5fx8vHT/pTGSpDeX7pHDaVqcCAAAAEBlodQDHuC2nk0VFuCj/UlZ+mbLEavjAAAAAKgklHrAAwTavTWmb3NJ0htLdss0Ga0HAAAAagNKPeAh7r6kmYLs3tp+NF0/bjtudRwAAAAAlYBSD3iI0AAf3dE7WpI0g9F6AAAAoFag1AMeZGy/5rJ72xR/IEW/7EmyOg4AAACACqLUAx4kPNiuW3tESZJeX7rb4jQAAAAAKopSD3iY+wfEyttmaNXuJP2aeMrqOAAAAAAqgFIPeJgmYf66Ma6JJOn1JXssTgMAAACgIij1gAd6cGCsDENavO2Yth9NszoOAAAAgHKi1AMeKDY8SNd0aCxJeoPRegAAAMBtUeoBD/XQoFhJ0tebD2v/yUyL0wAAAAAoD0o94KHaR4RqUOtwOU1p5jJG6wEAAAB3RKkHPNj4y1pIkuZvPKjDKactTgMAAACgrCj1gAfrFl1XvZrXVZ7D1Dsr9lodBwAAAEAZUeoBD1c4Wv/h2kSdzMixOA0AAACAsqDUAx6uX4v66hQZquw8p95ftc/qOAAAAADKgFIPeDjDMPTwoILR+v/8nKDU03kWJwIAAABQWpR6ALqybUO1ahik9Jx8zVmdYHUcAAAAAKVEqQcgm83QQwMLRuvfXblPWbn5FicCAAAAUBqUegCSpGs7NVbTugFKzszVR2sPWB0HAAAAQClQ6gFIkry9bHpwQKwk6e3le5WT77A4EQAAAICLodQDcBnerYkahth1NC1bCzYesjoOAAAAgIug1ANwsXt76b7+MZKkN5ftUb7DaXEiAAAAABdCqQdQxO29mqpOgI8SkrL0zZYjVscBAAAAcAGUegBFBPh6a0zf5pKkN5bskdNpWpwIAAAAwPlQ6gEUc9clzRRk99aOY+n6cftxq+MAAAAAOA9KPYBiQgN8dOcl0ZKkGUt2yzQZrQcAAABqIko9gBKN6dtcdm+bNh1I0c97kqyOAwAAAKAElHoAJQoPtuu2nk0lSTN+2m1xGgAAAAAlodQDOK/7Lo2Rt83QL3uTtCHhlNVxAAAAAJyDUg/gvJqE+eumrk0kSW8sYbQeAAAAqGko9QAu6MEBsbIZ0o/bj+v3w2lWxwEAAABwFko9gAuKCQ/SNR0bS5LeWMpoPQAAAFCTUOoBXNRDA1tIkr7ZckR7T2RYnAYAAABAIUo9gItqFxGiy9s0kGlKM5ftsToOAAAAgDMo9QBK5aFBBaP1n288pEMppy1OAwAAAECi1AMopW7RdXRJTD3lO029s3yv1XEAAAAAiFIPoAwePjNa/+HaRJ3MyLE4DQAAAABKPYBS69uinjpHhSkn36l3V+6zOg4AAADg8Sj1AErNMAw9PDBWkvTBLwlKPZ1ncSIAAADAs1HqAZTJFW0bqnXDYGXk5Os/P++3Og4AAADg0Sj1AMrEZjP00KCC0fr3Vu1TVm6+xYkAAAAAz0WpB1BmQzs2VnS9AJ3KytO8NYlWxwEAAAA8FqUeQJl5e9n04ICC0fp3VuxVTr7D4kQAAACAZ6LUAyiXm7o2UaMQPx1Ly9HnGw9ZHQcAAADwSJR6AOVi9/bSfZfGSJLeXLpH+Q6nxYkAAAAAz0OpB1But/WMUt1AXyUmZ+mbLUesjgMAAAB4HEo9gHIL8PXWmL7NJEmvL9ktp9O0NhAAAADgYdym1E+bNk19+vRRQECAwsLCSr3dtm3bdP311ys0NFSBgYHq0aOHEhOZrRuoLHde0kzBdm/tPJahxduOWR0HAAAA8ChuU+pzc3M1YsQIjRs3rtTb7NmzR/369VObNm20dOlSbd68WU899ZT8/PyqMCngWUL9fXTnJdGSCkbrTZPRegAAAKC6GKab/Rf4rFmzNGHCBKWkpFx03VtvvVU+Pj764IMPyr2/tLQ0hYaGKjU1VSEhIeV+H6A2O5mRo37P/6TsPKfmjO2lfi3rWx0JAAAAcFtl6aFuM1JfVk6nU998841atWqlwYMHq0GDBurVq5e++OKLC26Xk5OjtLS0Ig8AF1Y/yK5bezSVJM1YssviNAAAAIDnqLWl/vjx48rIyNBzzz2nIUOG6Pvvv9eNN96om266ScuWLTvvdtOnT1doaKjrERUVVY2pAfd1/6Ux8vEytHpvsjYkJFsdBwAAAPAIlpb6yZMnyzCMCz62b99ervd2OgvumT1s2DBNnDhRXbp00eTJk3Xttddq5syZ591uypQpSk1NdT0OHDhQrv0DniYizF83xUVKkl5fssfiNAAAAIBn8LZy55MmTdLo0aMvuE5MTEy53rt+/fry9vZWu3btiixv27atVq5ced7t7Ha77HZ7ufYJeLoHB8bq0w0H9NP24/rtcKraR4RaHQkAAACo1Swt9eHh4QoPD6+S9/b19VWPHj20Y8eOIst37typ6OjoKtkn4Oma1w/U0E4R+mrTYb2xdI9ev72r1ZEAAACAWs1trqlPTExUfHy8EhMT5XA4FB8fr/j4eGVkZLjWadOmjRYsWOB6/vjjj+vjjz/WO++8o927d2vGjBn66quv9NBDD1nxEQCP8NDAWEnSf7cc0d4TGRdZGwAAAEBFuE2pnzp1quLi4vT0008rIyNDcXFxiouL0/r1613r7NixQ6mpqa7nN954o2bOnKkXXnhBHTt21L///W/Nnz9f/fr1s+IjAB6hbeMQXdG2gUxTenMp19YDAAAAVcnt7lNf3bhPPVB2GxNP6aY3fpa3zdCyJwapSZi/1ZEAAAAAt8F96gFYqmvTOuoTW0/5TlNvL2O0HgAAAKgqlHoAVeLhQS0kSR+tO6AT6TkWpwEAAABqJ0o9gCrRJ7aeukSFKSffqXdX7rM6DgAAAFArUeoBVAnDMFyj9XNWJyg1K8/iRAAAAEDtQ6kHUGUub9NAbRoFKyMnX7N/2W91HAAAAKDWodQDqDI2m6FxZ+5b/96qfcrMybc4EQAAAFC7UOoBVKlrO0WoWb0ApWTl6cO1iVbHAQAAAGoVSj2AKuVlM/TggILR+reX71VOvsPiRAAAAEDtQakHUOVu6hqpxqF+Op6eo882HLQ6DgAAAFBrUOoBVDlfb5vu6x8jSZq5bI/yHU6LEwEAAAC1A6UeQLW4rWdT1Q301YHk0/pq82Gr4wAAAAC1AqUeQLXw9/XS2H7NJUlvLNkjp9O0OBEAAADg/ij1AKrNnZdEK9jurV3HM/T978esjgMAAAC4PUo9gGoT4ueju/pES5LeWLpbpsloPQAAAFARlHoA1WpM3+by87Fp88FUrdh10uo4AAAAgFuj1AOoVvWC7LqtZ1NJ0utLdlucBgAAAHBvlHoA1e7+S2Pk42Vozb5krd+fbHUcAAAAwG1R6gFUu8ah/hreNVISo/UAAABARVDqAVjiwQGxshnSkh0ntPVQqtVxAAAAALdEqQdgiWb1A3VtpwhJ0ptL91icBgAAAHBPlHoAlnloUKwk6b9bj2j38QyL0wAAAADuh1IPwDJtGoXoirYNZZrSzGWM1gMAAABlRakHYKmHz4zWf/HrIR08lWVxGgAAAMC9UOoBWCquaR31bVFP+U5Tby/fa3UcAAAAwK1Q6gFY7uFBLSRJH607oOPp2RanAQAAANwHpR6A5S6Jqae4pmHKzXfq3ZX7rI4DAAAAuA1KPQDLGYah8WdG6+f8kqCUrFyLEwEAAADugVIPoEa4rE0DtWkUrMxch2b/nGB1HAAAAMAtUOoB1AiGYbiurX//533KzMm3OBEAAABQ81HqAdQY13RsrOb1A5WSlad5axKtjgMAAADUeJR6ADWGl83QuAEF961/Z8VeZec5LE4EAAAA1GyUegA1yg1xTRQR6qfj6Tn6bMNBq+MAAAAANRqlHkCN4utt0/2XxkiSZi7bo3yH0+JEAAAAQM1FqQdQ44zs0VT1An118NRpLdx02Oo4AAAAQI1FqQdQ4/j7emls/+aSpDeW7pHTaVqcCAAAAKiZKPUAaqQ7ekcr2M9bu49n6Pvfj1odBwAAAKiRKPUAaqQQPx/dfUkzSdLrS/bINBmtBwAAAM5FqQdQY43p11z+Pl7acihVy3edtDoOAAAAUONQ6gHUWHUDfXVbz6aSpNeX7LY4DQAAAFDzUOoB1Gj3XxojHy9Da/cla93+ZKvjAAAAADUKpR5AjdYo1E83d4uUxGg9AAAAcC5KPYAa78EBsbIZ0tIdJ7T1UKrVcQAAAIAag1IPoMaLrheo6zpHSJLeWMpoPQAAAFCIUg/ALTw0sIUk6dutR7X7eLrFaQAAAICagVIPwC20bhSsK9s1lGlKby7da3UcAAAAoEag1ANwGw8PKhit/yL+kA4kZ1mcBgAAALAepR6A2+gSFaZ+LerL4TT19nJG6wEAAABKPQC3Ujha//H6Azqelm1xGgAAAMBalHoAbqV3TF11bRqm3Hyn3l25z+o4AAAAgKUo9QDcimEYGn9ZwWj9nNUJSsnKtTgRAAAAYB1KPQC3M6h1A7VtHKLMXIdm/bzf6jgAAACAZSj1ANyOYRh6eFCsJOn9VfuVkZNvcSIAAADAGpR6AG7p6g6NFVM/UKmn8zRvTYLVcQAAAABLUOoBuCUvm6EHBxaM1r+zYp+y8xwWJwIAAACqH6UegNu6oUsTRYT66UR6jj7dcNDqOAAAAEC1o9QDcFu+3jY9MKBgtH7m0j3KczgtTgQAAABUL0o9ALc2skeU6gf56lDKaS2MP2x1HAAAAKBaUeoBuDU/Hy+N7RcjSXpj6W45nabFiQAAAIDqQ6kH4Pbu6N1UIX7e2nMiU4t+O2p1HAAAAKDaUOoBuL1gPx+N7tNMkjRjyW6ZJqP1AAAA8AxuU+qnTZumPn36KCAgQGFhYaXaxjCMEh8vvvhi1YYFUO1G920ufx8v/XY4Tct2nrA6DgAAAFAt3KbU5+bmasSIERo3blyptzly5EiRx3vvvSfDMDR8+PAqTArACnUDfTWqV1NJ0utLdlucBgAAAKge3lYHKK1nnnlGkjRr1qxSb9OoUaMiz7/88ksNGjRIMTExlRkNQA1x36Ux+s8vCVq3/5TW7ktWz+Z1rY4EAAAAVCm3GamvqGPHjumbb77R2LFjL7heTk6O0tLSijwAuIeGIX66uXukpIJr6wEAAIDazmNK/ezZsxUcHKybbrrpgutNnz5doaGhrkdUVFQ1JQRQGR68NFZeNkPLd57QloOpVscBAAAAqpSlpX7y5Mnnncyu8LF9+/ZK2dd7772nUaNGyc/P74LrTZkyRampqa7HgQMHKmX/AKpH03oBur5zhCSurQcAAEDtZ+k19ZMmTdLo0aMvuE5lXP++YsUK7dixQx9//PFF17Xb7bLb7RXeJwDrjBsYqwW/HtJ3vx3VrmPpatkw2OpIAAAAQJWwtNSHh4crPDy8yvfz7rvvqlu3burcuXOV7wuA9Vo1DNbg9g216LdjenPpHr00sovVkQAAAIAq4TbX1CcmJio+Pl6JiYlyOByKj49XfHy8MjIyXOu0adNGCxYsKLJdWlqaPv30U917773VHRmAhR4e1EKS9OWmwzqQnGVxGgAAAKBquE2pnzp1quLi4vT0008rIyNDcXFxiouL0/r1613r7NixQ6mpRSfG+uijj2Sapm677bbqjgzAQp0iw9S/ZX05nKZmLttjdRwAAACgShimaZpWh6jJ0tLSFBoaqtTUVIWEhFgdB0AZrN6bpFvfXi1fL5tW/mWQGoRceKJMAAAAoCYoSw91m5F6ACirXs3rqnt0HeU6nHpnxV6r4wAAAACVjlIPoNYyDMN1bf3cNYk6lZlrcSIAAACgclHqAdRqA1uHq13jEGXlOvT+z/utjgMAAABUKko9gFrt7NH6Wav2KSMn3+JEAAAAQOWh1AOo9YZ0aKSY8EClZedr7uoEq+MAAAAAlYZSD6DW87IZGjcgVpL0zop9ys5zWJwIAAAAqByUegAe4Ya4JmoS5q+TGTn6dP0Bq+MAAAAAlYJSD8Aj+HjZ9MCAGEnSzGV7ledwWpwIAAAAqDhKPQCPcUv3KNUPsutQyml9GX/Y6jgAAABAhVHqAXgMPx8v3du/uSTpjaW75XCaFicCAAAAKoZSD8CjjOrVVCF+3tp7IlOLfjtqdRwAAACgQij1ADxKsJ+PRvctGK1/fclumSaj9QAAAHBflHoAHueePs0U4Oul3w6naenOE1bHAQAAAMqNUg/A49QJ9NWoXk0lSa//xGg9AAAA3BelHoBHurd/jHy9bFqfcEpr9yVbHQcAAAAoF0o9AI/UMMRPI7pHSpJmLNltcRoAAACgfCj1ADzWgwNi5WUztGLXSW0+mGJ1HAAAAKDMKPUAPFZU3QAN6xwhqWAmfAAAAMDdUOoBeLRxA2MlSYt+O6Zdx9ItTgMAAACUDaUegEdr2TBYQ9o3kiS9sXSPxWkAAACAsqHUA/B4Dw9qIUlauOmwEpOyLE4DAAAAlB6lHoDH6xgZqktbhcvhNDVzOaP1AAAAcB+UegCQ9PCZa+s/W39Qx9KyLU4DAAAAlA6lHgAk9Yqppx7N6ijX4dQ7y/daHQcAAAAoFUo9AJzx0Jlr6+euSdSpzFyL0wAAAAAX5211AACoKQa2Clf7iBD9djhNV7+6QuHBdgXZvRVo91awn7eC7N4KOvOn6/mZZcF2HwXavVx/9/OxyTAMqz8SAAAAajlKPQCcYRiGHruylcbOXq+jadk6WoFr671shqv0n31AINDurWB7SQcIfIo8D7T/cdDAy8bBAQAAAJTMME3TtDpETZaWlqbQ0FClpqYqJCTE6jgAqkFiUpaOpWcrIztf6Tn5ysjOV0ZOnjJyHGf9PV/p2fnKcL1+5s/cfFX2/6oG+HqddUbAHwcDXAcIzjooUNIBg8IDBHZvzh4AAABwB2XpoYzUA8A5mtYLUNN6AeXa1uk0dTrPUULpzyt+ECDnj+fp5yxPz85TnqPg6EBWrkNZuQ4dT8+p0Ofy8TLOKvw+RQ4QBJ19OUEJBxAKzyYItHsp0NdbNs4eAAAAqBEo9QBQiWw2Q4FnRtEbVvDknpz8gjMDMnMcSs/JO6f0Fz1AkJ6dr8zC13LylZGd53o9M9chScpzmDqVladTWXmSTlcoW1CJlxCUcIDg3EsMzlo30O4tX2/mawUAAKgISj0A1FB2by/Zg7xUL6hi7+NwmsrMPVPwc845K6DYJQbnHCA450CCw1lw9kDhMqVVLJuvt63oGQPFJiX0UZC98PKDogcFGob4qVGoX8UCAAAAuDlKPQDUcl42QyF+Pgrx86nQ+5imqZx853kuI8grfhnBWc8LDyoUPj+dV3D2QG6+U0n5uUoq5y0ER3aP0pND2yrUv2KfDQAAwF1R6gEApWIYhvx8vOTn46XwYHuF3ivf4fzjsoKzDgBknnNJQZFLDM66rCAzx6FDKaf18foDWrLjuJ69oYMGt29USZ8UAADAfVDqAQDVztvLptAAm0IDyj/CvnZfsibP36y9JzP1wAcbNLRjY/31+vYVPuAAAADgTpihCADglno2r6v//qm/HhoYKy+boW+2HNEVLy3T/A0Hxd1aAQCAp6DUAwDclp+Pl54Y0kZfPtxX7RqHKPV0niZ9ukl3v79OB09lWR0PAACgylHqAQBur0OTUH05vq+eGNJavt42Ld95QoNfXq7//LJfTiej9gAAoPai1AMAagUfL5seGthC3/6pv3o0q6PMXIemfvmbbnnrF+0+nmF1PAAAgCpBqQcA1Cqx4UH6+P5L9Oyw9gr09dL6hFO65tUVen3JbuU5nFbHAwAAqFSUegBArWOzGbrzkmb6/rEBGtAqXLkOp15ctEPDZqzS1kOpVscDAACoNJR6AECt1STMX7Pu6aGXR3ZWWICPfj+SpmGvr9Lz321Xdp7D6ngAAAAVRqkHANRqhmHoxrhILX5sgIZ2aiyH09SbS/fomldXaO2+ZKvjAQAAVAilHgDgEeoH2fX67V311p3d1CDYrr0nM3XLW7/oqS+2Kj07z+p4AAAA5UKpBwB4lMHtG+mHxwbotp5RkqQPVido8MvLtWT7cYuTAQAAlB2lHgDgcUL9fTT9pk6ad28vNa0boMOp2bpn1jpN/DheyZm5VscDAAAoNUo9AMBj9WlRX4smXKp7+zWXzZAW/HpIV760TF9tOizTNK2OBwAAcFGUegCAR/P39dL/XttO88f1UauGQUrKzNUjH/6q+/6zQUdTs62OBwAAcEGUegAAJMU1raOvH+mvCVe0lI+XocXbjunKl5bpw7WJjNoDAIAai1IPAMAZvt42Tbiilb5+pL86R4UpPSdfUz7fotvfWaOEpEyr4wEAABRDqQcA4BytGwXr83F99L9D28rPx6Zf9iZp8CvL9c7yvXI4GbUHAAA1B6UeAIASeNkM3ds/Rt9PGKA+sfWUnefUtP9u001vrNL2o2lWxwMAAJBEqQcA4IKa1gvQ3Ht76fnhHRXs561NB1N17Wsr9dIPO5WT77A6HgAA8HCUegAALsIwDI3s0VSLHxugK9s1VL7T1Gs/7tK1r63UxsRTVscDAAAejFIPAEApNQzx09t3dtPrt3dV/SBf7TqeoeFv/qxnv/5dWbn5VscDAAAeiFIPAEAZGIahoZ0a64eJA3RT1yYyTendlfs0+JXlWrnrpNXxAACAh6HUAwBQDnUCffXSLV00654eahLmrwPJp3XHu2v0xGeblHo6z+p4AADAQ1DqAQCogIGtG2jRxEt19yXRkqRP1h/UlS8t06LfjlqcDAAAeAJKPQAAFRRk99Yzwzro0wcvUUx4oI6n5+iBDzbo4bkbdSI9x+p4AACgFqPUAwBQSXo0q6v/PtpfDw+KlZfN0DdbjuiKl5Zp/oaDMk3T6ngAAKAWotQDAFCJ/Hy89PjgNlo4vq/aR4Qo9XSeJn26SXe/v04HT2VZHQ8AANQylHoAAKpA+4hQffFwX/1lSBv5etu0fOcJXfXycs3+eb+cTkbtAQBA5XCbUj9t2jT16dNHAQEBCgsLK9U2GRkZGj9+vCIjI+Xv76927dpp5syZVRsUAIAzfLxsGjcwVt/+qb96NqurrFyHnl74m2556xftPp5hdTwAAFALuE2pz83N1YgRIzRu3LhSb/PYY4/pu+++05w5c7Rt2zZNmDBB48eP18KFC6swKQAARcWGB+mj+3vr2WHtFejrpfUJp3TNqyv0+pLdynM4rY4HAADcmNuU+meeeUYTJ05Ux44dS73Nzz//rLvvvlsDBw5Us2bNdP/996tz585au3ZtFSYFAKA4m83QnZc00/ePDdDA1uHKdTj14qIdun7GKm09lGp1PAAA4KbcptSXR58+fbRw4UIdOnRIpmlqyZIl2rlzp6666qrzbpOTk6O0tLQiDwAAKkuTMH+9P7qHXh7ZWXUCfLTtSJqGvb5Kz327Xdl5DqvjAQAAN1OrS/2//vUvtWvXTpGRkfL19dWQIUP0+uuv69JLLz3vNtOnT1doaKjrERUVVY2JAQCewDAM3RgXqR8eG6DrOkfI4TQ1c9keXf3qCq3Zm2R1PAAA4EYsLfWTJ0+WYRgXfGzfvr3c7/+vf/1Lq1ev1sKFC7Vhwwb985//1MMPP6zFixefd5spU6YoNTXV9Thw4EC59w8AwIXUD7LrX7fF6Z27uqthiF37TmZq5Nur9b9fbFF6dp7V8QAAgBswTNO07L46J06cUFLShUckYmJi5Ovr63o+a9YsTZgwQSkpKRfc7vTp0woNDdWCBQs0dOhQ1/J7771XBw8e1HfffVeqjGlpaQoNDVVqaqpCQkJKtQ0AAGWVejpPz327TR+uLTiY3DjUT3+/saMGtWlgcTIAAFDdytJDvaspU4nCw8MVHh5eJe+dl5envLw82WxFT0bw8vKS08lMwwCAmiXU30fTb+qk6zpFaPLnW5SYnKV7Zq3TDV0iNPW69qob6HvxNwEAAB7Hba6pT0xMVHx8vBITE+VwOBQfH6/4+HhlZPxxn982bdpowYIFkqSQkBANGDBAjz/+uJYuXap9+/Zp1qxZ+s9//qMbb7zRqo8BAMAF9WlRX4smXKr7+jeXzZC+iD+sK19apq82HZaFJ9cBAIAaytLT78ti9OjRmj17drHlS5Ys0cCBAyUVTDz0/vvva/To0ZKko0ePasqUKfr++++VnJys6Oho3X///Zo4caIMwyjVfjn9HgBglfgDKfrLZ5u141i6JOmKtg31txs6qFGon8XJAABAVSpLD3WbUm8VSj0AwEq5+U69uXSPZizZpTyHqWC7t54c2la39ogq9QFqAADgXsrSQ93m9HsAADyRr7dNf7qipb55tL+6RIUpPSdfUz7fotvfWaP9JzOtjgcAACxGqQcAwA20ahis+eP66Klr28nfx0u/7E3SkFeX653le5XvYAJYAAA8FaUeAAA34WUzNLZfcy2acKn6tqin7Dynpv13m4a/+bO2H02zOh4AALAApR4AADfTtF6A5oztpReGd1Kwn7c2HUzVta+t1Es/7FROvsPqeAAAoBpR6gEAcEOGYeiWHlFa/NgAXdWuofKdpl77cZeufW2lNiaesjoeAACoJpR6AADcWMMQP711Zze9Maqr6gf5atfxDA1/82f931e/Kys33+p4AACgilHqAQBwc4Zh6JqOjfXDxAEa3jVSpim9t2qfrnp5uVbuOml1PAAAUIUo9QAA1BJ1An31z1s6a/aYnmoS5q+Dp07rjnfX6InPNik1K8/qeAAAoApQ6gEAqGUGtArXoomX6u5LomUY0ifrD+qKl5fpu61HrY4GAAAqGaUeAIBaKMjurWeGddCnD1yimPBAnUjP0YNzNuihuRt0PD3b6ngAAKCSUOoBAKjFujerq/8+2l/jB7WQl83Qf7cc1ZUvLddnGw7KNE2r4wEAgAqi1AMAUMv5+Xjpz4Nba+H4vurQJESpp/P050836a731upAcpbV8QAAQAVQ6gEA8BDtI0L1xUN99ZchbeTrbdOKXSc1+JXlmrVqn5xORu0BAHBHlHoAADyIt5dN4wbG6rs/9VfPZnWVlevQX7/6XSPe+kW7j6dbHQ8AAJQRpR4AAA8UEx6kj+7vrWdv6KAgu7c2JJzSNa+u1OtLdivP4bQ6HgAAKCVKPQAAHspmM3Rn72h9P/FSDWwdrlyHUy8u2qHrZ6zSloOpVscDAAClQKkHAMDDRYT56/3RPfTKyC6qE+CjbUfSdMMbq/Tct9uVneewOh4AALgASj0AAJBhGLohrol+eGyAruscIYfT1Mxle3T1qyu0Zm+S1fEAAMB5UOoBAIBL/SC7/nVbnN65q7sahti172SmRr69Wv/7xRalZ+dZHQ8AAJyDUg8AAIq5sl1D/fDYAN3Ws6kkac7qRF318nIt2X7c4mQAAOBslHoAAFCiED8fTb+po+bd10vR9QJ0JDVb98xapwkf/arkzFyr4wEAAFHqAQDARfSJra/v/nSp7r80RjZD+iL+sK54aZkWbjos0zStjgcAgEej1AMAgIvy9/XSk9e01YKH+qpNo2AlZ+bq0Q9/1X3/Wa8jqaetjgcAgMei1AMAgFLrHBWmheP76bErW8nHy9Dibcd11UvLNW9NopxORu0BAKhulHoAAFAmvt42PXp5S33zaH/FNQ1Tek6+nlywRbf/e7X2n8y0Oh4AAB6FUg8AAMqlVcNgffZgH029tp38fby0em+yBr+yXG8v36N8h9PqeAAAeARKPQAAKDcvm6Ex/Zrr+4mXql+L+srJd+rv/92um978WduOpFkdDwCAWs8wmbb2gtLS0hQaGqrU1FSFhIRYHQcAgBrLNE19uuGg/vb170rLzpe3zdA1HRsr0O4lm2HIy2a4/vzj75KXYchmM/748+y/GwUHDoq8Xvherr9LhlF8ue3Mexdd9+wMukCus97jzLI/1i3YHwAAVaUsPZRSfxGUegAAyuZ4Wrae+nKrFv12zOooVcZmqIQDAOc5WHHOAQmb7TwHM84cRCj2nkUOTOg8Bzb+2G/JBzDOev3sAxQ2Q942Q75eNvn5eMnP548/7d5e5yzzkp+3Td5enOgJAFWtLD3Uu5oyAQAAD9EgxE9v3dldy3ae0NZDqXI6TTlM0/Wnwyk5TVMOZ8Gj8O9/LDvr9cLtzn7dlGuZwzRllrC82PamKadTxZY5nOe8fmbZxThNyekwJXne2Ii3zShS/P19zxT/sw4C2H28zjw/62DBua+fOUjgOmBQuK732evY5Otl48wIALgASj0AAKgSA1qFa0CrcKtjlEuR0u/6UyUcoCh6MOD8BytU8sGGs18/Z9n5lhfdXuccuCh+MMPhPE/uMwcy8p2mch1OZec5lJPnUHaeU6fzHMoufOQ7lZv/x8SH+U5TGTn5ysipnu/CZujMWQM2+Z85AGAvcqCg6IGBYmcYnHPgoPgBh6IHHezeNtlsHEQA4D4o9QAAAOew2QzZZMjHy+okNYPTaSon33mm5BcUf1fpz3MqO/+PAwJnHwzIPmtZznm2O53r+OO9z1peeIGo05RO5zl0Os+hU8qrls/r6114AKGkswfOPlBQ9NIEewkHCbikAUBVo9QDAADggmw2Q/6+BafaVwfTLDx7wPnHwYJ8R5GDBGcfOChyQKHIwYPiBxxOn3Xw4OwDDflnXXaRe+bshNTT1fJx5eNlyM/7rDMQSrpkwdtLMiRDf0zUWPD34ssK1jNUeNXCH+udtezMioXb65x1/lhmFHutyHsahmtdlbh98WU6s03ha3+s98d7qoTtz/dZCnOe/fNwLSt8XuxnVvJ7qsiyP96zpCzF9/vHZ6ltattHCvX3UZ8W9a2OUWko9QAAAKhRDMOQ3duroMj6+1TLPvMdzrPOLjjfGQbFDxzkFDkr4ex1zl5W/GDE2Zc05DlM5TnylZ6TXy2fFfB0HZuE6qtH+lkdo9JQ6gEAAODxvL1sCvKyKchePf95XNZLGnLynTJN0zU1o2kWTNNYeCOrwssVTJlnvVZ0mc5Z33T9edbrZ14saftzl+msDGdvf+57Fu737NfOzaCzl5WQybWd+cf0lEXfs+gynbufs97z7J+hii0r+T3/yHv2z7L4e9bGuTPNWvihYuoHWR2hUlHqAQAAgGpW3Zc0AKi9mJUDAAAAAAA3RakHAAAAAMBNUeoBAAAAAHBTlHoAAAAAANwUpR4AAAAAADdFqQcAAAAAwE1R6gEAAAAAcFOUegAAAAAA3BSlHgAAAAAAN0WpBwAAAADATVHqAQAAAABwU5R6AAAAAADcFKUeAAAAAAA3RakHAAAAAMBNUeoBAAAAAHBTlHoAAAAAANwUpR4AAAAAADdFqQcAAAAAwE1R6gEAAAAAcFOUegAAAAAA3BSlHgAAAAAAN0WpBwAAAADATVHqAQAAAABwU5R6AAAAAADcFKUeAAAAAAA35W11gJrONE1JUlpamsVJAAAAAACeoLB/FvbRC6HUX0R6erokKSoqyuIkAAAAAABPkp6ertDQ0AuuY5ilqf4ezOl06vDhwwoODpZhGFbHOa+0tDRFRUXpwIEDCgkJsToOzoPvyT3wPdV8fEfuge/JPfA91Xx8R+6B78k9uMv3ZJqm0tPTFRERIZvtwlfNM1J/ETabTZGRkVbHKLWQkJAa/Y8TBfie3APfU83Hd+Qe+J7cA99Tzcd35B74ntyDO3xPFxuhL8REeQAAAAAAuClKPQAAAAAAbopSX0vY7XY9/fTTstvtVkfBBfA9uQe+p5qP78g98D25B76nmo/vyD3wPbmH2vg9MVEeAAAAAABuipF6AAAAAADcFKUeAAAAAAA3RakHAAAAAMBNUeoBAAAAAHBTlHo3t3z5cl133XWKiIiQYRj64osvrI6EEkyfPl09evRQcHCwGjRooBtuuEE7duywOhbO8uabb6pTp04KCQlRSEiILrnkEn377bdWx8JFPPfcczIMQxMmTLA6Cs7y17/+VYZhFHm0adPG6lg4x6FDh3THHXeoXr168vf3V8eOHbV+/XqrY+EszZo1K/a7ZBiGHn74Yauj4SwOh0NPPfWUmjdvLn9/f8XGxurZZ58V85HXLOnp6ZowYYKio6Pl7++vPn36aN26dVbHqhTeVgdAxWRmZqpz584aM2aMbrrpJqvj4DyWLVumhx9+WD169FB+fr6efPJJXXXVVfr9998VGBhodTxIioyM1HPPPaeWLVvKNE3Nnj1bw4YN06+//qr27dtbHQ8lWLdund566y116tTJ6igoQfv27bV48WLXc29v/pOjJjl16pT69u2rQYMG6dtvv1V4eLh27dqlOnXqWB0NZ1m3bp0cDofr+datW3XllVdqxIgRFqbCuZ5//nm9+eabmj17ttq3b6/169frnnvuUWhoqB599FGr4+GMe++9V1u3btUHH3ygiIgIzZkzR1dccYV+//13NWnSxOp4FcIt7WoRwzC0YMEC3XDDDVZHwUWcOHFCDRo00LJly3TppZdaHQfnUbduXb344osaO3as1VFwjoyMDHXt2lVvvPGG/va3v6lLly565ZVXrI6FM/7617/qiy++UHx8vNVRcB6TJ0/WqlWrtGLFCqujoAwmTJigr7/+Wrt27ZJhGFbHwRnXXnutGjZsqHfffde1bPjw4fL399ecOXMsTIZCp0+fVnBwsL788ksNHTrUtbxbt266+uqr9be//c3CdBXH6feABVJTUyUVlEbUPA6HQx999JEyMzN1ySWXWB0HJXj44Yc1dOhQXXHFFVZHwXns2rVLERERiomJ0ahRo5SYmGh1JJxl4cKF6t69u0aMGKEGDRooLi5O77zzjtWxcAG5ubmaM2eOxowZQ6GvYfr06aMff/xRO3fulCRt2rRJK1eu1NVXX21xMhTKz8+Xw+GQn59fkeX+/v5auXKlRakqD+fCAdXM6XRqwoQJ6tu3rzp06GB1HJxly5YtuuSSS5Sdna2goCAtWLBA7dq1szoWzvHRRx9p48aNteY6uNqoV69emjVrllq3bq0jR47omWeeUf/+/bV161YFBwdbHQ+S9u7dqzfffFOPPfaYnnzySa1bt06PPvqofH19dffdd1sdDyX44osvlJKSotGjR1sdBeeYPHmy0tLS1KZNG3l5ecnhcGjatGkaNWqU1dFwRnBwsC655BI9++yzatu2rRo2bKgPP/xQv/zyi1q0aGF1vAqj1APV7OGHH9bWrVtrxVHB2qZ169aKj49XamqqPvvsM919991atmwZxb4GOXDggP70pz/phx9+KHa0HTXH2aNTnTp1Uq9evRQdHa1PPvmEy1lqCKfTqe7du+vvf/+7JCkuLk5bt27VzJkzKfU11Lvvvqurr75aERERVkfBOT755BPNnTtX8+bNU/v27RUfH68JEyYoIiKC36ca5IMPPtCYMWPUpEkTeXl5qWvXrrrtttu0YcMGq6NVGKUeqEbjx4/X119/reXLlysyMtLqODiHr6+v62htt27dtG7dOr366qt66623LE6GQhs2bNDx48fVtWtX1zKHw6Hly5drxowZysnJkZeXl4UJUZKwsDC1atVKu3fvtjoKzmjcuHGxA5Zt27bV/PnzLUqEC0lISNDixYv1+eefWx0FJXj88cc1efJk3XrrrZKkjh07KiEhQdOnT6fU1yCxsbFatmyZMjMzlZaWpsaNG2vkyJGKiYmxOlqFcU09UA1M09T48eO1YMEC/fTTT2revLnVkVAKTqdTOTk5VsfAWS6//HJt2bJF8fHxrkf37t01atQoxcfHU+hrqIyMDO3Zs0eNGze2OgrO6Nu3b7Fbq+7cuVPR0dEWJcKFvP/++2rQoEGRCb5Qc2RlZclmK1qrvLy85HQ6LUqECwkMDFTjxo116tQpLVq0SMOGDbM6UoUxUu/mMjIyiox87Nu3T/Hx8apbt66aNm1qYTKc7eGHH/7/9u4tpKptj+P4b2ruhZmVmpWaaWZkZJeXgkwwu5BZRmIZukjNiETqISlSsaxIX4Iwiyy7GEV2gTBLQrFCiSjEyAq6GoaZht2v0E3Pw95n0ap9OC06+yynfT8wYc0x5pzjP14W/BhrjqXy8nJVVlbK09NTT548kSQNGDBA7u7uTq4OkpSTk6M5c+Zo+PDhevv2rcrLy1VXV6eamhpnl4ZveHp6/rAXhYeHh3x8fNijogdZs2aN4uLiFBQUpPb2duXn58vV1VVJSUnOLg1/Wb16tSIiIlRYWKjExEQ1NDSotLRUpaWlzi4N3+nq6lJZWZlSU1P5a8geKi4uTgUFBRo+fLjGjh2ra9euadu2bUpPT3d2afhGTU2Nuru7NXr0aDU3N2vt2rUKCwvT0qVLnV3aL+ObweQaGxsVHR1tO8/KypIkpaam6uDBg06qCt8rKSmRJE2bNs2uvaysjA1veojOzk6lpKSoo6NDAwYM0Pjx41VTU6NZs2Y5uzTAdNra2pSUlKTnz5/L19dXkZGRunLlinx9fZ1dGv4yadIkVVRUKCcnR5s3b9aIESNUVFTExl490Llz59Ta2kpA7MF27Nih9evXKzMzU52dnfL399eKFSu0YcMGZ5eGb7x+/Vo5OTlqa2uTt7e3EhISVFBQIDc3N2eX9sv4n3oAAAAAAEyKd+oBAAAAADApQj0AAAAAACZFqAcAAAAAwKQI9QAAAAAAmBShHgAAAAAAkyLUAwAAAABgUoR6AAAAAABMilAPAAAAAIBJEeoBAIDDHj58KMMw1NTU5OxSAAD4rRHqAQDoZdLS0mQYxg9HTEyMs0v7v6urq5NhGHr16pWzSwEA4B/Rx9kFAACA/72YmBiVlZXZtVksFidVAwAA/ims1AMA0AtZLBYNHTrU7vDy8pIkJScna/HixXbXf/78WYMGDdKhQ4ckSdXV1YqMjNTAgQPl4+OjefPm6cGDBw7V8PHjR61bt06BgYGyWCwKDQ3V/v37bf319fWaPHmyLBaL/Pz8lJ2drS9fvtj6g4ODVVRUZPfMiRMnauPGjbZzwzC0b98+xcfHq2/fvho1apROnz4t6c9XBKKjoyVJXl5eMgxDaWlpDs0BAICejlAPAMBvxmq16syZM3r37p2traamRh8+fFB8fLwk6f3798rKylJjY6POnz8vFxcXxcfHq6ur66fHSUlJ0dGjR1VcXKzbt29rz5496tevnyTp8ePHio2N1aRJk3T9+nWVlJRo//792rJli8Pz2bRpkxITE3Xjxg3FxsbKarXqxYsXCgwM1MmTJyVJd+/eVUdHh7Zv3+7w8wEA6Mn4+T0AAL1QVVWVLUD/W25urnJzczV79mx5eHiooqJCS5YskSSVl5dr/vz58vT0lCQlJCTY3XvgwAH5+vrq1q1bCg8P/6/j37t3TydOnFBtba1mzpwpSQoJCbH179q1S4GBgdq5c6cMw1BYWJja29u1bt06bdiwQS4uP7/ukJaWpqSkJElSYWGhiouL1dDQoJiYGHl7e0uSBg8erIEDB/70MwEAMAtW6gEA6IWio6PV1NRkd2RkZEiS+vTpo8TERB05ckTSn6vylZWVslqttvvv37+vpKQkhYSEqH///goODpYktba2/tT4TU1NcnV1VVRU1N/23759W1OmTJFhGLa2qVOn6t27d2pra3NoruPHj7d99vDwUP/+/dXZ2enQMwAAMCtW6gEA6IU8PDwUGhr6H/utVquioqLU2dmp2tpaubu72+2OHxcXp6CgIO3du1f+/v7q6upSeHi4Pn369FPju7u7//IcXFxc1N3dbdf2+fPnH65zc3OzOzcMw6HXBAAAMDNW6gEA+A1FREQoMDBQx48f15EjR7Ro0SJbOH7+/Lnu3r2rvLw8zZgxQ2PGjNHLly8dev64cePU1dWl+vr6v+0fM2aMLl++bBfaL126JE9PTw0bNkyS5Ovrq46ODlv/mzdv1NLS4lAdf/zxhyTp69evDt0HAIBZEOoBAOiFPn78qCdPntgdz549s7smOTlZu3fvVm1trd1P7728vOTj46PS0lI1NzfrwoULysrKcmj84OBgpaamKj09XadOnVJLS4vq6up04sQJSVJmZqYePXqkVatW6c6dO6qsrFR+fr6ysrJs79NPnz5dhw8f1sWLF3Xz5k2lpqbK1dXVoTqCgoJkGIaqqqr09OlTu80BAQDoDQj1AAD0QtXV1fLz87M7IiMj7a6xWq26deuWAgICNHXqVFu7i4uLjh07pqtXryo8PFyrV6/W1q1bHa6hpKRECxcuVGZmpsLCwrR8+XK9f/9ekhQQEKCzZ8+qoaFBEyZMUEZGhpYtW6a8vDzb/Tk5OYqKitK8efM0d+5cLViwQCNHjnSohoCAAG3atEnZ2dkaMmSIVq5c6fA8AADoyYzu719WAwAAAAAApsBKPQAAAAAAJkWoBwAAAADApAj1AAAAAACYFKEeAAAAAACTItQDAAAAAGBShHoAAAAAAEyKUA8AAAAAgEkR6gEAAAAAMClCPQAAAAAAJkWoBwAAAADApAj1AAAAAACY1L8AgAlXsrgkNTIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pylab.rcParams[\"figure.figsize\"] = (12, 8)\n",
+    "pylab.plot(counts, values, label=type(optimizer).__name__)\n",
+    "pylab.xlabel(\"Eval count\")\n",
+    "pylab.ylabel(\"Energy\")\n",
+    "pylab.title(\"Energy convergence using Gradient\")\n",
+    "pylab.legend(loc=\"upper right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial point\n",
+    "\n",
+    "By default, the optimization begins at a random point within the bounds defined by the ansatz. The [initial_point](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE.initial_point) option allows to override this point with a custom list of values that match the number of ansatz parameters.\n",
+    "\n",
+    "You might wonder... _Why set a custom initial point?_ Well, this option can come in handy if you have a guess for a reasonable starting point for the problem, or perhaps know information from a prior experiment.\n",
+    "\n",
+    "To demonstrate this feature, let's look at the results from our previous VQE run:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "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 0x13fe401d0>,\n",
+      "    'optimal_parameters': {   ParameterVectorElement(θ[0]): 4.296519450348804,\n",
+      "                              ParameterVectorElement(θ[1]): 4.426962358395529,\n",
+      "                              ParameterVectorElement(θ[2]): 0.547077760766061,\n",
+      "                              ParameterVectorElement(θ[3]): 6.0929478327669955,\n",
+      "                              ParameterVectorElement(θ[4]): -2.598326651673345,\n",
+      "                              ParameterVectorElement(θ[5]): 1.5683250498282117,\n",
+      "                              ParameterVectorElement(θ[6]): -4.717616147449735,\n",
+      "                              ParameterVectorElement(θ[7]): 0.3602101747090559},\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",
+      "    'optimizer_evals': None,\n",
+      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x1400832d0>,\n",
+      "    'optimizer_time': 0.25897884368896484}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(result)\n",
+    "cost_function_evals = result.cost_function_evals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, you can take the [optimal_point](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQEResult.html#qiskit_algorithms.minimum_eigensolvers.VQEResult.optimal_point) from the above result and use it as the [initial_point](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE.initial_point) for a follow-up computation.\n",
+    "\n",
+    "**Note:** [initial_point](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#qiskit_algorithms.minimum_eigensolvers.VQE.initial_point) is now a keyword-only argument of the [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#vqe) class (i.e, it must be set following the `keyword=value` syntax).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "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 0x140086250>,\n",
+      "    'optimal_parameters': {   ParameterVectorElement(θ[4]): -2.598326651673345,\n",
+      "                              ParameterVectorElement(θ[6]): -4.717616147449735,\n",
+      "                              ParameterVectorElement(θ[7]): 0.3602101747090559,\n",
+      "                              ParameterVectorElement(θ[1]): 4.426962358395529,\n",
+      "                              ParameterVectorElement(θ[5]): 1.5683250498282117,\n",
+      "                              ParameterVectorElement(θ[2]): 0.547077760766061,\n",
+      "                              ParameterVectorElement(θ[3]): 6.0929478327669955,\n",
+      "                              ParameterVectorElement(θ[0]): 4.296519450348804},\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",
+      "    'optimizer_evals': None,\n",
+      "    'optimizer_result': <qiskit_algorithms.optimizers.optimizer.OptimizerResult object at 0x13fdfb450>,\n",
+      "    'optimizer_time': 0.03934192657470703}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "initial_pt = result.optimal_point\n",
+    "\n",
+    "estimator1 = Estimator()\n",
+    "gradient1 = FiniteDiffEstimatorGradient(estimator, epsilon=0.01)\n",
+    "ansatz1 = TwoLocal(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()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cost_function_evals is 1 with initial point versus 9 without it.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    f\"cost_function_evals is {cost_function_evals1} with initial point versus {cost_function_evals} without it.\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "By looking at the [cost_function_evals](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#vqe) you can notice how the initial point helped the algorithm converge faster (in just 1 iteration, as we already provided the optimal solution).\n",
+    "\n",
+    "This can be particularly useful in cases where we have two closely related problems, and the solution to one problem can be used to guess the other's. A good example might be plotting dissociation profiles in chemistry, where we change the inter-atomic distances of a molecule and compute its minimum eigenvalue for each distance. When the distance changes are small, we expect the solution to still be close to the prior one. Thus, a popular technique is to simply use the optimal point from one solution as the starting point for the next step. There also exist more complex techniques, where we can apply extrapolation to compute an initial position based on prior solution(s) rather than directly use the prior solution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>None</td></tr><tr><td><code>qiskit-terra</code></td><td>0.25.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.11.4</td></tr><tr><td>Python compiler</td><td>Clang 14.0.3 (clang-1403.0.22.14.1)</td></tr><tr><td>Python build</td><td>main, Jul 25 2023 17:07:07</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>6</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Sun Aug 06 22:26:33 2023 BST</td></tr></table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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 Qiskit</h3><p>&copy; Copyright IBM 2017, 2023.</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>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import qiskit.tools.jupyter\n",
+    "\n",
+    "%qiskit_version_table\n",
+    "%qiskit_copyright"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/tutorials/03_vqe_simulation_with_noise.ipynb b/docs/tutorials/03_vqe_simulation_with_noise.ipynb
new file mode 100644
index 00000000..94b1ebeb
--- /dev/null
+++ b/docs/tutorials/03_vqe_simulation_with_noise.ipynb
@@ -0,0 +1,457 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# VQE with Qiskit Aer Primitives\n",
+    "\n",
+    "This notebook demonstrates how to leverage the [Qiskit Aer Primitives](https://qiskit.org/ecosystem/aer/apidocs/aer_primitives.html) to run both noiseless and noisy simulations locally. Qiskit Aer not only allows you to define your own custom noise model, but also to easily create a noise model based on the properties of a real quantum device. This notebook will show an example of the latter, to illustrate the general workflow of running algorithms with local noisy simulators.\n",
+    "\n",
+    "For further information on the Qiskit Aer noise model, you can consult the [Qiskit Aer documentation](https://qiskit.org/ecosystem/aer/apidocs/aer_noise.html), as well the tutorial for [building noise models](https://qiskit.org/ecosystem/aer/tutorials/3_building_noise_models.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The algorithm of choice is once again VQE, where the task consists on finding the minimum (ground state) energy of a Hamiltonian. As shown in previous tutorials, VQE takes in a qubit operator as input. Here, you will take a set of Pauli operators that were originally computed by Qiskit Nature for the H2 molecule, using the [SparsePauliOp](https://qiskit.org/documentation/stubs/qiskit.quantum_info.SparsePauliOp.html#sparsepauliop) class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of qubits: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "\n",
+    "H2_op = SparsePauliOp.from_list(\n",
+    "    [\n",
+    "        (\"II\", -1.052373245772859),\n",
+    "        (\"IZ\", 0.39793742484318045),\n",
+    "        (\"ZI\", -0.39793742484318045),\n",
+    "        (\"ZZ\", -0.01128010425623538),\n",
+    "        (\"XX\", 0.18093119978423156),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "print(f\"Number of qubits: {H2_op.num_qubits}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the above problem is still easily tractable classically, you can use [NumPyMinimumEigensolver](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.NumPyMinimumEigensolver.html#numpyminimumeigensolver) to compute a reference value to compare the results later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reference value: -1.85728\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_algorithms.minimum_eigensolvers import NumPyMinimumEigensolver\n",
+    "\n",
+    "numpy_solver = NumPyMinimumEigensolver()\n",
+    "result = numpy_solver.compute_minimum_eigenvalue(operator=H2_op)\n",
+    "ref_value = result.eigenvalue.real\n",
+    "print(f\"Reference value: {ref_value:.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following examples will all use the same ansatz and optimizer, defined as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define ansatz and optimizer\n",
+    "from qiskit.circuit.library import TwoLocal\n",
+    "from qiskit_algorithms.optimizers import SPSA\n",
+    "\n",
+    "iterations = 125\n",
+    "ansatz = TwoLocal(rotation_blocks=\"ry\", entanglement_blocks=\"cz\")\n",
+    "spsa = SPSA(maxiter=iterations)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Performance *without* noise\n",
+    "\n",
+    "Let's first run the [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.NumPyMinimumEigensolver.html#numpyminimumeigensolver) 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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define callback\n",
+    "# note: Re-run this cell to restart lists before training\n",
+    "counts = []\n",
+    "values = []\n",
+    "\n",
+    "\n",
+    "def store_intermediate_result(eval_count, parameters, mean, std):\n",
+    "    counts.append(eval_count)\n",
+    "    values.append(mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define Aer Estimator for noiseless statevector simulation\n",
+    "from qiskit.utils import algorithm_globals\n",
+    "from qiskit_aer.primitives import Estimator 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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "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"
+     ]
+    }
+   ],
+   "source": [
+    "# instantiate and run VQE\n",
+    "from qiskit_algorithms.minimum_eigensolvers import VQE\n",
+    "\n",
+    "vqe = VQE(noiseless_estimator, ansatz, optimizer=spsa, callback=store_intermediate_result)\n",
+    "result = vqe.compute_minimum_eigenvalue(operator=H2_op)\n",
+    "\n",
+    "print(f\"VQE on Aer qasm simulator (no noise): {result.eigenvalue.real:.5f}\")\n",
+    "print(f\"Delta from reference energy value is {(result.eigenvalue.real - ref_value):.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You captured the energy values above during the convergence, so you can track the process in the graph below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Convergence with no noise')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pylab\n",
+    "\n",
+    "pylab.rcParams[\"figure.figsize\"] = (12, 4)\n",
+    "pylab.plot(counts, values)\n",
+    "pylab.xlabel(\"Eval count\")\n",
+    "pylab.ylabel(\"Energy\")\n",
+    "pylab.title(\"Convergence with no noise\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Performance *with* noise\n",
+    "\n",
+    "Now, let's add noise to our simulation. In particular, you will extract a noise model from a (fake) device. 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",
+    "First, you need to get an actual device backend and from its [configuration](https://qiskit.org/documentation/stubs/qiskit.providers.fake_provider.FakeVigo.html#qiskit.providers.fake_provider.FakeVigo.configuration) and [properties](https://qiskit.org/documentation/stubs/qiskit.providers.fake_provider.FakeVigo.html#qiskit.providers.fake_provider.FakeVigo.properties) you can setup a coupling map and a noise model to match the device. Note: You can also use this coupling map as the entanglement map for the variational form if you choose to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NoiseModel:\n",
+      "  Basis gates: ['cx', 'id', 'rz', 'sx', 'x']\n",
+      "  Instructions with noise: ['sx', 'id', 'x', 'measure', 'cx']\n",
+      "  Qubits with noise: [0, 1, 2, 3, 4]\n",
+      "  Specific qubit errors: [('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,)), ('cx', (3, 4)), ('cx', (4, 3)), ('cx', (3, 1)), ('cx', (1, 3)), ('cx', (1, 2)), ('cx', (2, 1)), ('cx', (0, 1)), ('cx', (1, 0)), ('measure', (0,)), ('measure', (1,)), ('measure', (2,)), ('measure', (3,)), ('measure', (4,))]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_aer.noise import NoiseModel\n",
+    "from qiskit.providers.fake_provider import FakeVigo\n",
+    "\n",
+    "# fake providers contain data from real IBM Quantum devices stored in Qiskit Terra,\n",
+    "# and are useful for extracting realistic noise models.\n",
+    "device = FakeVigo()\n",
+    "\n",
+    "coupling_map = device.configuration().coupling_map\n",
+    "noise_model = NoiseModel.from_backend(device)\n",
+    "\n",
+    "print(noise_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the noise model is defined, you can run [VQE](https://qiskit.org/documentation/stubs/qiskit.providers.fake_provider.FakeVigo.html#qiskit.providers.fake_provider.FakeVigo.properties) using an Aer [Estimator](https://qiskit.org/ecosystem/aer/stubs/qiskit_aer.primitives.Estimator.html#qiskit_aer.primitives.Estimator), where you can pass the noise model to the underlying simulator using the [backend_options](https://qiskit.org/ecosystem/aer/stubs/qiskit_aer.primitives.Estimator.html) dictionary. Please note that this simulation will take longer than the noiseless one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of defining a new instance of the [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#vqe) class, you can now simply assign a new estimator to our previous [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#vqe) instance. As the callback method will be re-used, you will also need to re-start the `counts` and `values` variables to be able to plot the convergence graph later on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# re-start callback variables\n",
+    "counts = []\n",
+    "values = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VQE on Aer qasm simulator (with noise): -1.75227\n",
+      "Delta from reference energy value is 0.10501\n"
+     ]
+    }
+   ],
+   "source": [
+    "vqe.estimator = noisy_estimator\n",
+    "\n",
+    "result1 = vqe.compute_minimum_eigenvalue(operator=H2_op)\n",
+    "\n",
+    "print(f\"VQE on Aer qasm simulator (with noise): {result1.eigenvalue.real:.5f}\")\n",
+    "print(f\"Delta from reference energy value is {(result1.eigenvalue.real - ref_value):.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if counts or values:\n",
+    "    pylab.rcParams[\"figure.figsize\"] = (12, 4)\n",
+    "    pylab.plot(counts, values)\n",
+    "    pylab.xlabel(\"Eval count\")\n",
+    "    pylab.ylabel(\"Energy\")\n",
+    "    pylab.title(\"Convergence with noise\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n"
+   ]
+  },
+  {
+   "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](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.html#vqe) using the Qiskit Aer [Estimator](https://qiskit.org/ecosystem/aer/stubs/qiskit_aer.primitives.Estimator.html#estimator) with 1024 shots. Finally, you extracted a noise model from a backend and used it to define a new [Estimator](https://qiskit.org/ecosystem/aer/stubs/qiskit_aer.primitives.Estimator.html#estimator) for noisy simulations. The results are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "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): -1.75227\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Reference value: {ref_value:.5f}\")\n",
+    "print(f\"VQE on Aer qasm simulator (no noise): {result.eigenvalue.real:.5f}\")\n",
+    "print(f\"VQE on Aer qasm simulator (with noise): {result1.eigenvalue.real:.5f}\")"
+   ]
+  },
+  {
+   "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",
+    "\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://qiskit.org/documentation/partners/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 tutorial](https://qiskit.org/documentation/partners/qiskit_ibm_runtime/tutorials/Error-Suppression-and-Error-Mitigation.html) for further information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>None</td></tr><tr><td><code>qiskit-terra</code></td><td>0.25.0</td></tr><tr><td><code>qiskit_aer</code></td><td>0.12.2</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.11.4</td></tr><tr><td>Python compiler</td><td>Clang 14.0.3 (clang-1403.0.22.14.1)</td></tr><tr><td>Python build</td><td>main, Jul 25 2023 17:07:07</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>6</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Sun Aug 06 23:10:08 2023 BST</td></tr></table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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 Qiskit</h3><p>&copy; Copyright IBM 2017, 2023.</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>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import qiskit.tools.jupyter\n",
+    "\n",
+    "%qiskit_version_table\n",
+    "%qiskit_copyright"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/tutorials/04_vqd.ipynb b/docs/tutorials/04_vqd.ipynb
new file mode 100644
index 00000000..053561ab
--- /dev/null
+++ b/docs/tutorials/04_vqd.ipynb
@@ -0,0 +1,370 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Variational Quantum Deflation (VQD) Algorithm\n",
+    "\n",
+    "This notebook demostrates how to use Qiskit's implementation of the [Variational Quantum Deflation (VQD)](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.eigensolvers.VQD.html#vqd) algorithm for computing higher energy states of a Hamiltonian, as introduced in this [reference paper](https://arxiv.org/abs/1805.08138).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "VQD is a quantum algorithm that uses a variational technique to find the _k_ eigenvalues of the Hamiltonian _H_ of a given system.\n",
+    "\n",
+    "The algorithm computes excited state energies of generalized hamiltonians by optimizing over a modified cost function. Each successive eigenvalue is calculated iteratively by introducing an overlap term with all the previously computed eigenstates that must be minimized. This ensures that higher energy eigenstates are found.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Complete working example for VQD\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first step of the VQD workflow is to create a qubit operator, ansatz and optimizer. For this example, you can use the H2 molecule, which should already look familiar if you have completed the previous VQE tutorials:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "\n",
+    "H2_op = SparsePauliOp.from_list(\n",
+    "    [\n",
+    "        (\"II\", -1.052373245772859),\n",
+    "        (\"IZ\", 0.39793742484318045),\n",
+    "        (\"ZI\", -0.39793742484318045),\n",
+    "        (\"ZZ\", -0.01128010425623538),\n",
+    "        (\"XX\", 0.18093119978423156),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can set up, for example, a [TwoLocal](https://qiskit.org/documentation/stubs/qiskit.circuit.library.TwoLocal.html#twolocal) ansatz with two qubits, and choose [SLSQP](https://qiskit.org/documentation/stubs/qiskit.algorithms.optimizers.SLSQP.html#slsqp) as the optimization method.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 538.128x200.667 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit.library import TwoLocal\n",
+    "from qiskit_algorithms.optimizers import SLSQP\n",
+    "\n",
+    "ansatz = TwoLocal(2, rotation_blocks=[\"ry\", \"rz\"], entanglement_blocks=\"cz\", reps=1)\n",
+    "\n",
+    "optimizer = SLSQP()\n",
+    "ansatz.decompose().draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step of the workflow is to define the required primitives for running [VQD](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.eigensolvers.VQD.html#vqd). This algorithm requires two different primitive instances: one [Estimator](https://qiskit.org/documentation/stubs/qiskit.primitives.Estimator.html#qiskit.primitives.Estimator) for computing the expectation values for the \"VQE part\" of the algorithm, and one [Sampler](https://qiskit.org/documentation/stubs/qiskit.primitives.Sampler.html#qiskit.primitives.Sampler). The sampler will be passed along to the [StateFidelity](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.state_fidelities.BaseStateFidelity.html#basestatefidelity) subroutine that will be used to compute the cost for higher energy states. There are several methods that you can use to compute state fidelities, but to keep things simple, you can use the [ComputeUncompute](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.state_fidelities.ComputeUncompute.html#qiskit_algorithms.state_fidelities.ComputeUncompute) method already available in [qiskit_algorithm.state_fidelities](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.state_fidelities.html#module-qiskit_algorithms.state_fidelities).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.primitives import Sampler, Estimator\n",
+    "from qiskit_algorithms.state_fidelities import ComputeUncompute\n",
+    "\n",
+    "estimator = Estimator()\n",
+    "sampler = Sampler()\n",
+    "fidelity = ComputeUncompute(sampler)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to set up the VQD algorithm, it is important to define two additional inputs: the number of energy states to compute (`k`) and the `betas` defined in the original VQD paper. In this example, the number of states (`k`) will be set to three, which indicates that two excited states will be computed in addition to the ground state.\n",
+    "\n",
+    "The `betas` balance the contribution of each overlap term to the cost function, and they are an optional argument in the [VQD](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.eigensolvers.VQD.html#vqd) construction. If not set by the user, they can be autoevaluated for input operators of type [SparsePauliOp](https://qiskit.org/documentation/stubs/qiskit.quantum_info.SparsePauliOp.html). Please note that if you want to set your own `betas`, you should provide a list of values of length `k`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k = 3\n",
+    "betas = [33, 33, 33]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are almost ready to run the VQD algorithm, but let's define a callback first to store intermediate values:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "counts = []\n",
+    "values = []\n",
+    "steps = []\n",
+    "\n",
+    "\n",
+    "def callback(eval_count, params, value, meta, step):\n",
+    "    counts.append(eval_count)\n",
+    "    values.append(value)\n",
+    "    steps.append(step)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can finally instantiate [VQD](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.eigensolvers.VQD.html#vqd) and compute the eigenvalues for the chosen operator.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_algorithms.eigensolvers import VQD\n",
+    "\n",
+    "\n",
+    "vqd = VQD(estimator, fidelity, ansatz, optimizer, k=k, betas=betas, callback=callback)\n",
+    "result = vqd.compute_eigenvalues(operator=H2_op)\n",
+    "vqd_values = result.eigenvalues"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see the three state energies as part of the [VQD](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.eigensolvers.VQD.html#vqd) result:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.857275   -1.24458441 -0.882722  ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(vqd_values.real)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can use the values stored by the callback to plot the energy convergence for each state:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pylab\n",
+    "\n",
+    "pylab.rcParams[\"figure.figsize\"] = (12, 8)\n",
+    "\n",
+    "steps = np.asarray(steps)\n",
+    "counts = np.asarray(counts)\n",
+    "values = np.asarray(values)\n",
+    "\n",
+    "for i in range(1, 4):\n",
+    "    _counts = counts[np.where(steps == i)]\n",
+    "    _values = values[np.where(steps == i)]\n",
+    "    pylab.plot(_counts, _values, label=f\"State {i-1}\")\n",
+    "\n",
+    "pylab.xlabel(\"Eval count\")\n",
+    "pylab.ylabel(\"Energy\")\n",
+    "pylab.title(\"Energy convergence for each computed state\")\n",
+    "pylab.legend(loc=\"upper right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This molecule can be solved exactly using the [NumPyEigensolver](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.eigensolvers.NumPyEigensolver.html#numpyeigensolver) class, which will give a reference value that you can compare with the [VQD](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.eigensolvers.VQD.html#vqd) result:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit_algorithms.eigensolvers import NumPyEigensolver\n",
+    "\n",
+    "\n",
+    "exact_solver = NumPyEigensolver(k=3)\n",
+    "exact_result = exact_solver.compute_eigenvalues(H2_op)\n",
+    "ref_values = exact_result.eigenvalues"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see a comparison of the exact result with the previously computed [VQD](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.eigensolvers.VQD.html#vqd) eigenvalues:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reference values: [-1.85727503 -1.24458455 -0.88272215]\n",
+      "VQD values: [-1.857275   -1.24458441 -0.882722  ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Reference values: {ref_values}\")\n",
+    "print(f\"VQD values: {vqd_values.real}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, the result from VQD matches the values from the exact solution, and extends VQE to also compute excited states.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>None</td></tr><tr><td><code>qiskit-terra</code></td><td>0.25.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.11.4</td></tr><tr><td>Python compiler</td><td>Clang 14.0.3 (clang-1403.0.22.14.1)</td></tr><tr><td>Python build</td><td>main, Jul 25 2023 17:07:07</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>6</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Mon Aug 07 00:28:41 2023 BST</td></tr></table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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 Qiskit</h3><p>&copy; Copyright IBM 2017, 2023.</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>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import qiskit.tools.jupyter\n",
+    "%qiskit_version_table\n",
+    "%qiskit_copyright"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/tutorials/05_qaoa.ipynb b/docs/tutorials/05_qaoa.ipynb
new file mode 100644
index 00000000..4e46d7f2
--- /dev/null
+++ b/docs/tutorials/05_qaoa.ipynb
@@ -0,0 +1,401 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quantum Approximate Optimization Algorithm\n",
+    "\n",
+    "The Algorithms library for Qiskit has an implementation of the Quantum Approximate Optimization Algorithm [QAOA](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.QAOA.html#qaoa) and this notebook demonstrates using it for a graph partition problem.\n",
+    "\n",
+    "Before we begin, let's import the [annotations](https://docs.python.org/3/howto/annotations.html) module from [**future**](https://docs.python.org/3/library/__future__.html) to allow postponed evaluation of annotations. This enables us to use simpler type hints throughout the notebook.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import annotations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we create a graph and draw it so it can be seen.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "\n",
+    "num_nodes = 4\n",
+    "w = np.array(\n",
+    "    [[0.0, 1.0, 1.0, 0.0], [1.0, 0.0, 1.0, 1.0], [1.0, 1.0, 0.0, 1.0], [0.0, 1.0, 1.0, 0.0]]\n",
+    ")\n",
+    "G = nx.from_numpy_array(w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true,
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "layout = nx.random_layout(G, seed=10)\n",
+    "colors = [\"r\", \"g\", \"b\", \"y\"]\n",
+    "nx.draw(G, layout, node_color=colors)\n",
+    "labels = nx.get_edge_attributes(G, \"weight\")\n",
+    "nx.draw_networkx_edge_labels(G, pos=layout, edge_labels=labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The brute-force method is as follows. Basically, we exhaustively try all the binary assignments. In each binary assignment, the entry of a vertex is either 0 (meaning the vertex is in the first partition) or 1 (meaning the vertex is in the second partition). We print the binary assignment that satisfies the definition of the graph partition and corresponds to the minimal number of crossing edges.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Objective value computed by the brute-force method is 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "def objective_value(x: np.ndarray, w: np.ndarray) -> float:\n",
+    "    \"\"\"Compute the value of a cut.\n",
+    "    Args:\n",
+    "        x: Binary string as numpy array.\n",
+    "        w: Adjacency matrix.\n",
+    "    Returns:\n",
+    "        Value of the cut.\n",
+    "    \"\"\"\n",
+    "    X = np.outer(x, (1 - x))\n",
+    "    w_01 = np.where(w != 0, 1, 0)\n",
+    "    return np.sum(w_01 * X)\n",
+    "\n",
+    "\n",
+    "def bitfield(n: int, L: int) -> list[int]:\n",
+    "    result = np.binary_repr(n, L)\n",
+    "    return [int(digit) for digit in result]  # [2:] to chop off the \"0b\" part\n",
+    "\n",
+    "\n",
+    "# use the brute-force way to generate the oracle\n",
+    "L = num_nodes\n",
+    "max = 2**L\n",
+    "sol = np.inf\n",
+    "for i in range(max):\n",
+    "    cur = bitfield(i, L)\n",
+    "\n",
+    "    how_many_nonzero = np.count_nonzero(cur)\n",
+    "    if how_many_nonzero * 2 != L:  # not balanced\n",
+    "        continue\n",
+    "\n",
+    "    cur_v = objective_value(np.array(cur), w)\n",
+    "    if cur_v < sol:\n",
+    "        sol = cur_v\n",
+    "\n",
+    "print(f\"Objective value computed by the brute-force method is {sol}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The graph partition problem can be converted to an Ising Hamiltonian. Qiskit has different capabilities in the Optimization module to do this. Here, since the goal is to show QAOA, the module is used without further explanation to create the operator. The paper [Ising formulations of many NP problems](https://arxiv.org/abs/1302.5843) may be of interest if you would like to understand the technique further.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.quantum_info import Pauli, SparsePauliOp\n",
+    "\n",
+    "\n",
+    "def get_operator(weight_matrix: np.ndarray) -> tuple[SparsePauliOp, float]:\n",
+    "    r\"\"\"Generate Hamiltonian for the graph partitioning\n",
+    "    Notes:\n",
+    "        Goals:\n",
+    "            1 Separate the vertices into two set of the same size.\n",
+    "            2 Make sure the number of edges between the two set is minimized.\n",
+    "        Hamiltonian:\n",
+    "            H = H_A + H_B\n",
+    "            H_A = sum\\_{(i,j)\\in E}{(1-ZiZj)/2}\n",
+    "            H_B = (sum_{i}{Zi})^2 = sum_{i}{Zi^2}+sum_{i!=j}{ZiZj}\n",
+    "            H_A is for achieving goal 2 and H_B is for achieving goal 1.\n",
+    "    Args:\n",
+    "        weight_matrix: Adjacency matrix.\n",
+    "    Returns:\n",
+    "        Operator for the Hamiltonian\n",
+    "        A constant shift for the obj function.\n",
+    "    \"\"\"\n",
+    "    num_nodes = len(weight_matrix)\n",
+    "    pauli_list = []\n",
+    "    coeffs = []\n",
+    "    shift = 0\n",
+    "\n",
+    "    for i in range(num_nodes):\n",
+    "        for j in range(i):\n",
+    "            if weight_matrix[i, j] != 0:\n",
+    "                x_p = np.zeros(num_nodes, dtype=bool)\n",
+    "                z_p = np.zeros(num_nodes, dtype=bool)\n",
+    "                z_p[i] = True\n",
+    "                z_p[j] = True\n",
+    "                pauli_list.append(Pauli((z_p, x_p)))\n",
+    "                coeffs.append(-0.5)\n",
+    "                shift += 0.5\n",
+    "\n",
+    "    for i in range(num_nodes):\n",
+    "        for j in range(num_nodes):\n",
+    "            if i != j:\n",
+    "                x_p = np.zeros(num_nodes, dtype=bool)\n",
+    "                z_p = np.zeros(num_nodes, dtype=bool)\n",
+    "                z_p[i] = True\n",
+    "                z_p[j] = True\n",
+    "                pauli_list.append(Pauli((z_p, x_p)))\n",
+    "                coeffs.append(1.0)\n",
+    "            else:\n",
+    "                shift += 1\n",
+    "\n",
+    "    return SparsePauliOp(pauli_list, coeffs=coeffs), shift\n",
+    "\n",
+    "\n",
+    "qubit_op, offset = get_operator(w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So lets use the QAOA algorithm to find the solution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 1 0 0]\n",
+      "Objective value computed by QAOA is 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_algorithms.minimum_eigensolvers import QAOA\n",
+    "from qiskit_algorithms.optimizers import COBYLA\n",
+    "from qiskit.circuit.library import TwoLocal\n",
+    "from qiskit.primitives import Sampler\n",
+    "from qiskit.quantum_info import Pauli, Statevector\n",
+    "from qiskit.result import QuasiDistribution\n",
+    "from qiskit.utils import algorithm_globals\n",
+    "\n",
+    "sampler = Sampler()\n",
+    "\n",
+    "\n",
+    "def sample_most_likely(state_vector: QuasiDistribution | Statevector) -> np.ndarray:\n",
+    "    \"\"\"Compute the most likely binary string from state vector.\n",
+    "    Args:\n",
+    "        state_vector: State vector or quasi-distribution.\n",
+    "\n",
+    "    Returns:\n",
+    "        Binary string as an array of ints.\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",
+    "\n",
+    "\n",
+    "algorithm_globals.random_seed = 10598\n",
+    "\n",
+    "optimizer = COBYLA()\n",
+    "qaoa = QAOA(sampler, optimizer, reps=2)\n",
+    "\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": "markdown",
+   "metadata": {},
+   "source": [
+    "The outcome can be seen to match to the value computed above by brute force. But we can also use the classical [NumPyMinimumEigensolver](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.minimum_eigensolvers.NumPyMinimumEigensolver.html#numpyminimumeigensolver) to do the computation, which may be useful as a reference without doing things by brute force.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 1 0 0]\n",
+      "Objective value computed by the NumPyMinimumEigensolver is 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_algorithms.minimum_eigensolvers import NumPyMinimumEigensolver\n",
+    "from qiskit.quantum_info import Operator\n",
+    "\n",
+    "npme = NumPyMinimumEigensolver()\n",
+    "result = npme.compute_minimum_eigenvalue(Operator(qubit_op))\n",
+    "\n",
+    "x = sample_most_likely(result.eigenstate)\n",
+    "\n",
+    "print(x)\n",
+    "print(f\"Objective value computed by the NumPyMinimumEigensolver is {objective_value(x, w)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to use VQE as is shown below\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 0 1]\n",
+      "Objective value computed by VQE is 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_algorithms.minimum_eigensolvers import SamplingVQE\n",
+    "from qiskit.circuit.library import TwoLocal\n",
+    "from qiskit.utils import algorithm_globals\n",
+    "\n",
+    "algorithm_globals.random_seed = 10598\n",
+    "\n",
+    "optimizer = COBYLA()\n",
+    "ansatz = TwoLocal(qubit_op.num_qubits, \"ry\", \"cz\", reps=2, entanglement=\"linear\")\n",
+    "sampling_vqe = SamplingVQE(sampler, ansatz, optimizer)\n",
+    "\n",
+    "result = sampling_vqe.compute_minimum_eigenvalue(qubit_op)\n",
+    "\n",
+    "x = sample_most_likely(result.eigenstate)\n",
+    "\n",
+    "print(x)\n",
+    "print(f\"Objective value computed by VQE is {objective_value(x, w)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>None</td></tr><tr><td><code>qiskit-terra</code></td><td>0.25.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.11.4</td></tr><tr><td>Python compiler</td><td>Clang 14.0.3 (clang-1403.0.22.14.1)</td></tr><tr><td>Python build</td><td>main, Jul 25 2023 17:07:07</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>6</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Mon Aug 07 00:40:19 2023 BST</td></tr></table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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 Qiskit</h3><p>&copy; Copyright IBM 2017, 2023.</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>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import qiskit.tools.jupyter\n",
+    "%qiskit_version_table\n",
+    "%qiskit_copyright"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "0becea4cb9c4294abbba7f0b15d5de98241be600556705f5379b48b9de7cb1f9"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}