diff --git a/.buildinfo b/.buildinfo
index c359bee9..3ad08594 100644
--- a/.buildinfo
+++ b/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 9819ffc6e12194ec337e2a6db159e35c
+config: 29ed7a05517d7dde50ab7eb968f02f45
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/_images/tutorials_06_examples_max_cut_and_tsp_16_1.png b/_images/tutorials_06_examples_max_cut_and_tsp_16_1.png
index dafaf7ec..e8303c9c 100644
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diff --git a/_images/tutorials_06_examples_max_cut_and_tsp_19_1.png b/_images/tutorials_06_examples_max_cut_and_tsp_19_1.png
index 9cdc15a4..87afbb33 100644
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diff --git a/_images/tutorials_06_examples_max_cut_and_tsp_5_0.png b/_images/tutorials_06_examples_max_cut_and_tsp_5_0.png
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diff --git a/_images/tutorials_06_examples_max_cut_and_tsp_8_1.png b/_images/tutorials_06_examples_max_cut_and_tsp_8_1.png
index dafaf7ec..e8303c9c 100644
Binary files a/_images/tutorials_06_examples_max_cut_and_tsp_8_1.png and b/_images/tutorials_06_examples_max_cut_and_tsp_8_1.png differ
diff --git a/_images/tutorials_12_quantum_random_access_optimizer_35_2.png b/_images/tutorials_12_quantum_random_access_optimizer_35_1.png
similarity index 100%
rename from _images/tutorials_12_quantum_random_access_optimizer_35_2.png
rename to _images/tutorials_12_quantum_random_access_optimizer_35_1.png
diff --git a/_sources/getting_started.rst.txt b/_sources/getting_started.rst.txt
index 19c6a9d2..751d3118 100644
--- a/_sources/getting_started.rst.txt
+++ b/_sources/getting_started.rst.txt
@@ -7,9 +7,9 @@ Getting started
 Installation
 ============
 
-Qiskit Optimization depends Qiskit. which has its own
-`Qiskit Getting Started <https://qiskit.org/documentation/getting_started.html>`__ detailing the
-installation options and its supported environments/platforms. You should refer to
+Qiskit Optimization depends on Qiskit, which has its own
+`installation instructions <https://docs.quantum.ibm.com/start/install>`__ detailing the
+installation options for Qiskit and its supported environments/platforms. You should refer to
 that first. Then the information here can be followed which focuses on the additional installation
 specific to Qiskit Optimization.
 
@@ -21,13 +21,9 @@ See :ref:`optional_installs` for more information.
 
     .. tab-item:: Start locally
 
-        The simplest way to get started is to first follow the `getting started 'Start locally' guide for
-        Qiskit <https://qiskit.org/documentation/getting_started.html>`__
+        The simplest way to get started is to follow the `Qiskit installation instructions <https://docs.quantum.ibm.com/start/install>`__
 
-        In your virtual environment where you installed Qiskit simply add ``optimization`` to the
-        extra list in a similar manner to how the extra ``visualization`` support is installed for
-        Qiskit, i.e:
-        In your virtual environment, where you installed Qiskit, install Qiskit Optimization as follows:
+        In your virtual environment where you installed Qiskit, also install ``qiskit-optimization``:
 
         .. code:: sh
 
@@ -48,7 +44,7 @@ See :ref:`optional_installs` for more information.
 
        Since Qiskit Optimization depends on Qiskit, and its latest changes may require new or changed
        features of Qiskit, you should first follow Qiskit's `"Install from source"` instructions
-       here `Qiskit Getting Started <https://qiskit.org/documentation/getting_started.html>`__
+       `here <https://docs.quantum.ibm.com/start/install-qiskit-source>`__
 
        .. raw:: html
 
diff --git a/_sources/migration/01_migration_guide_to_v0.5.rst.txt b/_sources/migration/01_migration_guide_to_v0.5.rst.txt
index 51f2a754..ea9db5e5 100644
--- a/_sources/migration/01_migration_guide_to_v0.5.rst.txt
+++ b/_sources/migration/01_migration_guide_to_v0.5.rst.txt
@@ -9,7 +9,7 @@ Overview
 
 Qiskit Terra v0.22 introduces new algorithm implementations that
 leverage `Qiskit
-Primitives <https://qiskit.org/documentation/apidoc/primitives.html>`__
+Primitives <https://docs.quantum.ibm.com/api/qiskit/primitives>`__
 (Estimator and Sampler). The former algorithm implementations that
 leverage opflow will be deprecated in the future release.
 
@@ -19,7 +19,7 @@ of Qiskit Terra v0.22 until the former algorithms are deprecated.
 It is not the intention to provide detailed explanations of the
 primitives in this migration guide. We suggest that you read the
 `corresponding
-resources <https://qiskit.org/documentation/apidoc/primitives.html>`__
+resources <https://docs.quantum.ibm.com/api/qiskit/primitives>`__
 of the Qiskit Terra documentation instead.
 
 We use ``qiskit.primitives.Sampler`` in this guide as an example of
diff --git a/_sources/tutorials/12_quantum_random_access_optimizer.ipynb.txt b/_sources/tutorials/12_quantum_random_access_optimizer.ipynb.txt
index 44b1fe69..da7c3031 100644
--- a/_sources/tutorials/12_quantum_random_access_optimizer.ipynb.txt
+++ b/_sources/tutorials/12_quantum_random_access_optimizer.ipynb.txt
@@ -51,7 +51,7 @@
     "To begin, we utilize the `Maxcut` class from Qiskit Optimization's application module. It allows us to generate a `QuadraticProgram` representation of the given graph.\n",
     "\n",
     "Note that once our problem has been represented as a `QuadraticProgram`, it will need to be converted to the correct type, a [quadratic unconstrained binary optimization (QUBO)](https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization) problem, so that it is compatible with QRAO.\n",
-    "A `QuadraticProgram` generated by `Maxcut` is already a QUBO, but if you define your own problem be sure you convert it to a QUBO before proceeding. Here is [a tutorial](https://qiskit.org/documentation/optimization/tutorials/02_converters_for_quadratic_programs.html) on converting `QuadraticPrograms`."
+    "A `QuadraticProgram` generated by `Maxcut` is already a QUBO, but if you define your own problem be sure you convert it to a QUBO before proceeding. Here is [a tutorial](https://qiskit-community.github.io/qiskit-optimization/tutorials/02_converters_for_quadratic_programs.html) on converting `QuadraticPrograms`."
    ]
   },
   {
@@ -113,7 +113,7 @@
     "\n",
     "Once we have appropriately configured our problem, we proceed to encode it using the `QuantumRandomAccessEncoding` class from the `qrao` module. This encoding step allows us to generate a quantum Hamiltonian operator that represents our problem. In particular, we employ a Quantum Random Access Code (QRAC) to encode multiple classical binary variables (corresponding to the nodes of our max-cut graph) into each qubit.\n",
     "\n",
-    "It's important to note that the resulting \"relaxed\" Hamiltonian, produced by this encoding, will not be diagonal. This differs from the standard workflow in `qiskit-optimization`, which typically generates a diagonal (Ising) Hamiltonian suitable for optimization using a `MinimumEigenOptimizer`. You can find a tutorial on the `MinimumEigenOptimizer` [here](https://qiskit.org/documentation/optimization/tutorials/03_minimum_eigen_optimizer.html).\n",
+    "It's important to note that the resulting \"relaxed\" Hamiltonian, produced by this encoding, will not be diagonal. This differs from the standard workflow in `qiskit-optimization`, which typically generates a diagonal (Ising) Hamiltonian suitable for optimization using a `MinimumEigenOptimizer`. You can find a tutorial on the `MinimumEigenOptimizer` [here](https://qiskit-community.github.io/qiskit-optimization/tutorials/03_minimum_eigen_optimizer.html).\n",
     "\n",
     "In our encoding process, we employ a $(3,1,p)-$QRAC, where each qubit can accommodate a maximum of 3 classical binary variables. The parameter $p$ represents the bit recovery probability achieved through measurement. Depending on the nature of the problem, some qubits may have fewer than 3 classical variables assigned to them. To evaluate the compression achieved, we can examine the `compression_ratio` attribute of the encoding, which provides the ratio between the number of original binary variables and the number of qubits used (at best, a factor of 3)."
    ]
@@ -552,7 +552,7 @@
     "\n",
     "By invoking `qrao.solve_relaxed()`, we obtain two essential outputs:\n",
     "\n",
-    "- `MinimumEigensolverResult`: This object contains the results of running the minimum eigen optimizer such as the VQE on the relaxed problem. It provides information about the eigenvalue, and other relevant details. You can refer to the Qiskit Algorithms [documentation](https://qiskit.org/documentation/stubs/qiskit.algorithms.MinimumEigensolverResult.html) for a comprehensive explanation of the entries within this object.\n",
+    "- `MinimumEigensolverResult`: This object contains the results of running the minimum eigen optimizer such as the VQE on the relaxed problem. It provides information about the eigenvalue, and other relevant details. You can refer to the Qiskit Algorithms [documentation](https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.MinimumEigensolverResult) for a comprehensive explanation of the entries within this object.\n",
     "- `RoundingContext`: This object encapsulates essential information about the encoding and the solution of the relaxed problem in a form that is ready for consumption by the rounding schemes."
    ]
   },
diff --git a/getting_started.html b/getting_started.html
index 9f2a90d9..93ffca80 100644
--- a/getting_started.html
+++ b/getting_started.html
@@ -432,9 +432,9 @@
 <h1>Getting started<a class="headerlink" href="#getting-started" title="Permalink to this heading">#</a></h1>
 <section id="installation">
 <h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">#</a></h2>
-<p>Qiskit Optimization depends Qiskit. which has its own
-<a class="reference external" href="https://qiskit.org/documentation/getting_started.html">Qiskit Getting Started</a> detailing the
-installation options and its supported environments/platforms. You should refer to
+<p>Qiskit Optimization depends on Qiskit, which has its own
+<a class="reference external" href="https://docs.quantum.ibm.com/start/install">installation instructions</a> detailing the
+installation options for Qiskit and its supported environments/platforms. You should refer to
 that first. Then the information here can be followed which focuses on the additional installation
 specific to Qiskit Optimization.</p>
 <p>Qiskit Optimization has some functions that have been made optional where the dependent code and/or
@@ -444,12 +444,8 @@ <h2>Installation<a class="headerlink" href="#installation" title="Permalink to t
 <input checked="checked" id="sd-tab-item-0" name="sd-tab-set-0" type="radio">
 </input><label class="sd-tab-label" for="sd-tab-item-0">
 Start locally</label><div class="sd-tab-content docutils">
-<p>The simplest way to get started is to first follow the <a class="reference external" href="https://qiskit.org/documentation/getting_started.html">getting started ‘Start locally’ guide for
-Qiskit</a></p>
-<p>In your virtual environment where you installed Qiskit simply add <code class="docutils literal notranslate"><span class="pre">optimization</span></code> to the
-extra list in a similar manner to how the extra <code class="docutils literal notranslate"><span class="pre">visualization</span></code> support is installed for
-Qiskit, i.e:
-In your virtual environment, where you installed Qiskit, install Qiskit Optimization as follows:</p>
+<p>The simplest way to get started is to follow the <a class="reference external" href="https://docs.quantum.ibm.com/start/install">Qiskit installation instructions</a></p>
+<p>In your virtual environment where you installed Qiskit, also install <code class="docutils literal notranslate"><span class="pre">qiskit-optimization</span></code>:</p>
 <div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>qiskit-optimization
 </pre></div>
 </div>
@@ -469,7 +465,7 @@ <h2>Installation<a class="headerlink" href="#installation" title="Permalink to t
 the latest version of the Qiskit Optimization code more efficiently.</p>
 <p>Since Qiskit Optimization depends on Qiskit, and its latest changes may require new or changed
 features of Qiskit, you should first follow Qiskit’s <cite>“Install from source”</cite> instructions
-here <a class="reference external" href="https://qiskit.org/documentation/getting_started.html">Qiskit Getting Started</a></p>
+<a class="reference external" href="https://docs.quantum.ibm.com/start/install-qiskit-source">here</a></p>
 <h2>Installing Qiskit Optimization from Source</h2><p>Using the same development environment that you installed Qiskit in you are ready to install
 Qiskit Optimization.</p>
 <ol class="arabic">
diff --git a/migration/01_migration_guide_to_v0.5.html b/migration/01_migration_guide_to_v0.5.html
index 1faaa2eb..c36f88c6 100644
--- a/migration/01_migration_guide_to_v0.5.html
+++ b/migration/01_migration_guide_to_v0.5.html
@@ -435,7 +435,7 @@ <h1>Qiskit Optimization v0.5 Migration Guide<a class="headerlink" href="#qiskit-
 <section id="overview">
 <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this heading">#</a></h2>
 <p>Qiskit Terra v0.22 introduces new algorithm implementations that
-leverage <a class="reference external" href="https://qiskit.org/documentation/apidoc/primitives.html">Qiskit
+leverage <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/primitives">Qiskit
 Primitives</a>
 (Estimator and Sampler). The former algorithm implementations that
 leverage opflow will be deprecated in the future release.</p>
@@ -443,7 +443,7 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 of Qiskit Terra v0.22 until the former algorithms are deprecated.</p>
 <p>It is not the intention to provide detailed explanations of the
 primitives in this migration guide. We suggest that you read the
-<a class="reference external" href="https://qiskit.org/documentation/apidoc/primitives.html">corresponding
+<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/primitives">corresponding
 resources</a>
 of the Qiskit Terra documentation instead.</p>
 <p>We use <code class="docutils literal notranslate"><span class="pre">qiskit.primitives.Sampler</span></code> in this guide as an example of
diff --git a/searchindex.js b/searchindex.js
index 4e3c9817..06832710 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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"stubs/qiskit_optimization.converters.LinearInequalityToPenalty.rst", "stubs/qiskit_optimization.converters.MaximizeToMinimize.rst", "stubs/qiskit_optimization.converters.MinimizeToMaximize.rst", "stubs/qiskit_optimization.converters.QuadraticProgramConverter.rst", "stubs/qiskit_optimization.converters.QuadraticProgramToQubo.rst", "stubs/qiskit_optimization.infinity.INFINITY.rst", "stubs/qiskit_optimization.problems.Constraint.rst", "stubs/qiskit_optimization.problems.LinearConstraint.rst", "stubs/qiskit_optimization.problems.LinearExpression.rst", "stubs/qiskit_optimization.problems.QuadraticConstraint.rst", "stubs/qiskit_optimization.problems.QuadraticExpression.rst", "stubs/qiskit_optimization.problems.QuadraticObjective.rst", "stubs/qiskit_optimization.problems.QuadraticProgramElement.rst", "stubs/qiskit_optimization.problems.Variable.rst", "stubs/qiskit_optimization.translators.from_docplex_mp.rst", "stubs/qiskit_optimization.translators.from_gurobipy.rst", "stubs/qiskit_optimization.translators.from_ising.rst", "stubs/qiskit_optimization.translators.to_docplex_mp.rst", "stubs/qiskit_optimization.translators.to_gurobipy.rst", "stubs/qiskit_optimization.translators.to_ising.rst", "tutorials/01_quadratic_program.ipynb", "tutorials/02_converters_for_quadratic_programs.ipynb", "tutorials/03_minimum_eigen_optimizer.ipynb", "tutorials/04_grover_optimizer.ipynb", "tutorials/05_admm_optimizer.ipynb", "tutorials/06_examples_max_cut_and_tsp.ipynb", "tutorials/07_examples_vehicle_routing.ipynb", "tutorials/08_cvar_optimization.ipynb", "tutorials/09_application_classes.ipynb", "tutorials/10_warm_start_qaoa.ipynb", "tutorials/11_using_classical_optimization_solvers_and_models.ipynb", "tutorials/12_quantum_random_access_optimizer.ipynb", "tutorials/index.rst"], "titles": ["Qiskit Optimization API Reference", "Optimization algorithms (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.algorithms</span></code>)", "Quantum Random Access Optimization (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.algorithms.qrao</span></code>)", "Optimization applications (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.applications</span></code>)", "Optimization converters (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.converters</span></code>)", "Optimization problems (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.problems</span></code>)", "Quadratic program translators (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.translators</span></code>)", "Qiskit Optimization Explanations", "Background on Quantum Random Access Optimization: <em>Quantum relaxations, quantum random access codes, rounding schemes</em>", "Getting started", "Qiskit Optimization overview", "Qiskit Optimization v0.5 Migration Guide", "Qiskit Optimization v0.6 Migration Guide", "Qiskit Optimization Migration Guide", "Release Notes", "QiskitOptimizationError", "QuadraticProgram", "ADMMOptimizationResult", "ADMMOptimizer", "ADMMParameters", "ADMMState", "BaseAggregator", "CobylaOptimizer", "CplexOptimizer", "GoemansWilliamsonOptimizationResult", "GoemansWilliamsonOptimizer", "GroverOptimizationResult", "GroverOptimizer", "GurobiOptimizer", "IntermediateResult", "MeanAggregator", "MinimumEigenOptimizationResult", "MinimumEigenOptimizer", "MultiStartOptimizer", "OptimizationAlgorithm", "OptimizationResult", "OptimizationResultStatus", "RecursiveMinimumEigenOptimizationResult", "RecursiveMinimumEigenOptimizer", "ScipyMilpOptimizer", "SlsqpOptimizationResult", "SlsqpOptimizer", "SolutionSample", "WarmStartQAOAFactory", "WarmStartQAOAOptimizer", "EncodingCommutationVerifier", "MagicRounding", 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83, 86, 87, 88, 93, 94, 96, 97, 98, 100, 101, 102], "2": [2, 11, 16, 19, 22, 29, 35, 36, 41, 47, 49, 67, 72, 82, 88, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 103], "ansatz": [2, 11, 12, 14, 97, 98, 99, 103], "min_eigen_solv": [2, 11, 12, 14, 32, 44, 49, 98, 100, 103], "result": [2, 4, 8, 11, 12, 14, 16, 17, 18, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 44, 46, 48, 49, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102], "onli": [2, 8, 11, 12, 14, 22, 25, 29, 32, 39, 41, 44, 46, 69, 71, 73, 74, 87, 93, 94, 95, 97, 98, 102], "must": [2, 8, 11, 14, 44, 46, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103], "done": [2, 14, 97], "manual": [2, 14, 97], "max_vars_per_qubit": [2, 14, 47, 49, 103], "relaxed_result": [2, 14, 48, 49, 103], "rounding_context": [2, 14, 46, 52, 53, 103], "bryce": [2, 8, 103], "fuller": [2, 8, 103], "et": [2, 8, 38, 44, 72, 98, 99, 103], 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Optimizer": [[94, "Minimum-Eigen-Optimizer"]], "Converting a QUBO to a SparsePauliOp": [[94, "Converting-a-QUBO-to-a-SparsePauliOp"]], "Solving a QUBO with the MinimumEigenOptimizer": [[94, "Solving-a-QUBO-with-the-MinimumEigenOptimizer"]], "Analysis of Samples": [[94, "Analysis-of-Samples"]], "Grover Optimizer": [[95, "Grover-Optimizer"]], "Grover Adaptive Search": [[95, "Grover-Adaptive-Search"]], "Find the Minimum of a QUBO Problem using GroverOptimizer": [[95, "Find-the-Minimum-of-a-QUBO-Problem-using-GroverOptimizer"]], "Check that GroverOptimizer finds the correct value": [[95, "Check-that-GroverOptimizer-finds-the-correct-value"]], "ADMM Optimizer": [[96, "ADMM-Optimizer"]], "Initialization": [[96, "Initialization"], [98, "Initialization"]], "Example": [[96, "Example"]], "Classical Solution": [[96, "Classical-Solution"]], "Parameters": [[96, "Parameters"]], "Calling 3-ADMM-H algorithm": [[96, "Calling-3-ADMM-H-algorithm"]], "Classical Solver Result": [[96, 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"Vehicle-Routing"]], "The Introduction": [[98, "The-Introduction"]], "The Model": [[98, "The-Model"]], "Classical solution": [[98, "Classical-solution"]], "Quantum solution": [[98, "Quantum-solution"]], "Construct the Ising Hamiltonian": [[98, "Construct-the-Ising-Hamiltonian"]], "From Hamiltonian to QP formulation": [[98, "From-Hamiltonian-to-QP-formulation"]], "Classical solution using IBM ILOG CPLEX": [[98, "Classical-solution-using-IBM-ILOG-CPLEX"]], "Quantum solution from the ground up": [[98, "Quantum-solution-from-the-ground-up"]], "Step 1": [[98, "Step-1"]], "Step 2": [[98, "Step-2"]], "Step 3": [[98, "Step-3"]], "Step 4": [[98, "Step-4"]], "Step 5": [[98, "Step-5"]], "Improving Variational Quantum Optimization using CVaR": [[99, "Improving-Variational-Quantum-Optimization-using-CVaR"]], "Portfolio Optimization": [[99, "Portfolio-Optimization"]], "Minimum Eigen Optimizer using SamplingVQE": [[99, "Minimum-Eigen-Optimizer-using-SamplingVQE"]], "Application Classes for Optimization Problems": [[100, "Application-Classes-for-Optimization-Problems"]], "Vertex cover problem": [[100, "Vertex-cover-problem"]], "Knapsack problem": [[100, "Knapsack-problem"]], "How to check the Hamiltonian": [[100, "How-to-check-the-Hamiltonian"]], "Warm-starting quantum optimization": [[101, "Warm-starting-quantum-optimization"]], "Preliminaries: relaxing QUBOs": [[101, "Preliminaries:-relaxing-QUBOs"]], "Standard QAOA": [[101, "Standard-QAOA"]], "Warm-start QAOA": [[101, "Warm-start-QAOA"], [101, "id2"]], "Analysis": [[101, "Analysis"]], "Using Classical Optimization Solvers and Models with Qiskit Optimization": [[102, "Using-Classical-Optimization-Solvers-and-Models-with-Qiskit-Optimization"]], "CplexSolver and GurobiSolver": [[102, "CplexSolver-and-GurobiSolver"]], "Translators between QuadraticProgram and Docplex/Gurobipy": [[102, "Translators-between-QuadraticProgram-and-Docplex/Gurobipy"]], "Indicator constraints of Docplex": [[102, "Indicator-constraints-of-Docplex"]], "Quantum Random Access Optimization": [[103, "Quantum-Random-Access-Optimization"]], "Set up a combinatorial optimization problem": [[103, "Set-up-a-combinatorial-optimization-problem"]], "Encode the problem into a quantum Hamiltonian": [[103, "Encode-the-problem-into-a-quantum-Hamiltonian"]], "Solve the problem using the QuantumRandomAccessOptimizer": [[103, "Solve-the-problem-using-the-QuantumRandomAccessOptimizer"]], "Interpret the solution": [[103, "Interpret-the-solution"]], "Inspect the results of subroutines": [[103, "Inspect-the-results-of-subroutines"]], "Exact Problem Solution with the NumpyMinimumEigensolver": [[103, "Exact-Problem-Solution-with-the-NumpyMinimumEigensolver"]], "Solve the problem using the QuantumRandomAccessOptimizer with MagicRounding": [[103, "Solve-the-problem-using-the-QuantumRandomAccessOptimizer-with-MagicRounding"]], "Alternative: Solve the Problem in Two Explicit Steps": [[103, "Alternative:-Solve-the-Problem-in-Two-Explicit-Steps"]], "Manually solve the relaxed problem.": [[103, "Manually-solve-the-relaxed-problem."]], "Manually perform rounding on the relaxed problem results": [[103, "Manually-perform-rounding-on-the-relaxed-problem-results"]], "Appendix": [[103, "Appendix"]], "How to verify correctness of your encoding": [[103, "How-to-verify-correctness-of-your-encoding"]], "Optimization Tutorials": [[104, "optimization-tutorials"]]}, "indexentries": {"module": [[0, "module-qiskit_optimization"], [1, "module-qiskit_optimization.algorithms"], [2, "module-qiskit_optimization.algorithms.qrao"], [3, "module-qiskit_optimization.applications"], [4, "module-qiskit_optimization.converters"], [5, "module-qiskit_optimization.problems"], [6, "module-qiskit_optimization.translators"]], "qiskit_optimization": [[0, "module-qiskit_optimization"]], "qiskit_optimization.algorithms": [[1, "module-qiskit_optimization.algorithms"]], "qiskit_optimization.algorithms.qrao": 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\ No newline at end of file
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"stubs/qiskit_optimization.algorithms.GroverOptimizer.rst", "stubs/qiskit_optimization.algorithms.GurobiOptimizer.rst", "stubs/qiskit_optimization.algorithms.IntermediateResult.rst", "stubs/qiskit_optimization.algorithms.MeanAggregator.rst", "stubs/qiskit_optimization.algorithms.MinimumEigenOptimizationResult.rst", "stubs/qiskit_optimization.algorithms.MinimumEigenOptimizer.rst", "stubs/qiskit_optimization.algorithms.MultiStartOptimizer.rst", "stubs/qiskit_optimization.algorithms.OptimizationAlgorithm.rst", "stubs/qiskit_optimization.algorithms.OptimizationResult.rst", "stubs/qiskit_optimization.algorithms.OptimizationResultStatus.rst", "stubs/qiskit_optimization.algorithms.RecursiveMinimumEigenOptimizationResult.rst", "stubs/qiskit_optimization.algorithms.RecursiveMinimumEigenOptimizer.rst", "stubs/qiskit_optimization.algorithms.ScipyMilpOptimizer.rst", "stubs/qiskit_optimization.algorithms.SlsqpOptimizationResult.rst", "stubs/qiskit_optimization.algorithms.SlsqpOptimizer.rst", "stubs/qiskit_optimization.algorithms.SolutionSample.rst", "stubs/qiskit_optimization.algorithms.WarmStartQAOAFactory.rst", "stubs/qiskit_optimization.algorithms.WarmStartQAOAOptimizer.rst", "stubs/qiskit_optimization.algorithms.qrao.EncodingCommutationVerifier.rst", "stubs/qiskit_optimization.algorithms.qrao.MagicRounding.rst", "stubs/qiskit_optimization.algorithms.qrao.QuantumRandomAccessEncoding.rst", "stubs/qiskit_optimization.algorithms.qrao.QuantumRandomAccessOptimizationResult.rst", "stubs/qiskit_optimization.algorithms.qrao.QuantumRandomAccessOptimizer.rst", "stubs/qiskit_optimization.algorithms.qrao.RoundingContext.rst", "stubs/qiskit_optimization.algorithms.qrao.RoundingResult.rst", "stubs/qiskit_optimization.algorithms.qrao.RoundingScheme.rst", "stubs/qiskit_optimization.algorithms.qrao.SemideterministicRounding.rst", "stubs/qiskit_optimization.applications.BinPacking.rst", "stubs/qiskit_optimization.applications.Clique.rst", "stubs/qiskit_optimization.applications.ExactCover.rst", "stubs/qiskit_optimization.applications.GraphOptimizationApplication.rst", "stubs/qiskit_optimization.applications.GraphPartition.rst", "stubs/qiskit_optimization.applications.Knapsack.rst", "stubs/qiskit_optimization.applications.Maxcut.rst", "stubs/qiskit_optimization.applications.NumberPartition.rst", "stubs/qiskit_optimization.applications.OptimizationApplication.rst", "stubs/qiskit_optimization.applications.SKModel.rst", "stubs/qiskit_optimization.applications.SetPacking.rst", "stubs/qiskit_optimization.applications.StableSet.rst", "stubs/qiskit_optimization.applications.Tsp.rst", "stubs/qiskit_optimization.applications.VehicleRouting.rst", "stubs/qiskit_optimization.applications.VertexCover.rst", "stubs/qiskit_optimization.converters.InequalityToEquality.rst", "stubs/qiskit_optimization.converters.IntegerToBinary.rst", "stubs/qiskit_optimization.converters.LinearEqualityToPenalty.rst", "stubs/qiskit_optimization.converters.LinearInequalityToPenalty.rst", "stubs/qiskit_optimization.converters.MaximizeToMinimize.rst", "stubs/qiskit_optimization.converters.MinimizeToMaximize.rst", "stubs/qiskit_optimization.converters.QuadraticProgramConverter.rst", "stubs/qiskit_optimization.converters.QuadraticProgramToQubo.rst", "stubs/qiskit_optimization.infinity.INFINITY.rst", "stubs/qiskit_optimization.problems.Constraint.rst", "stubs/qiskit_optimization.problems.LinearConstraint.rst", "stubs/qiskit_optimization.problems.LinearExpression.rst", "stubs/qiskit_optimization.problems.QuadraticConstraint.rst", "stubs/qiskit_optimization.problems.QuadraticExpression.rst", "stubs/qiskit_optimization.problems.QuadraticObjective.rst", "stubs/qiskit_optimization.problems.QuadraticProgramElement.rst", "stubs/qiskit_optimization.problems.Variable.rst", "stubs/qiskit_optimization.translators.from_docplex_mp.rst", "stubs/qiskit_optimization.translators.from_gurobipy.rst", "stubs/qiskit_optimization.translators.from_ising.rst", "stubs/qiskit_optimization.translators.to_docplex_mp.rst", "stubs/qiskit_optimization.translators.to_gurobipy.rst", "stubs/qiskit_optimization.translators.to_ising.rst", "tutorials/01_quadratic_program.ipynb", "tutorials/02_converters_for_quadratic_programs.ipynb", "tutorials/03_minimum_eigen_optimizer.ipynb", "tutorials/04_grover_optimizer.ipynb", "tutorials/05_admm_optimizer.ipynb", "tutorials/06_examples_max_cut_and_tsp.ipynb", "tutorials/07_examples_vehicle_routing.ipynb", "tutorials/08_cvar_optimization.ipynb", "tutorials/09_application_classes.ipynb", "tutorials/10_warm_start_qaoa.ipynb", "tutorials/11_using_classical_optimization_solvers_and_models.ipynb", "tutorials/12_quantum_random_access_optimizer.ipynb", "tutorials/index.rst"], "titles": ["Qiskit Optimization API Reference", "Optimization algorithms (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.algorithms</span></code>)", "Quantum Random Access Optimization (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.algorithms.qrao</span></code>)", "Optimization applications (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.applications</span></code>)", "Optimization converters (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.converters</span></code>)", "Optimization problems (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.problems</span></code>)", "Quadratic program translators (<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">qiskit_optimization.translators</span></code>)", "Qiskit Optimization Explanations", "Background on Quantum Random Access Optimization: <em>Quantum relaxations, quantum random access codes, rounding schemes</em>", "Getting started", "Qiskit Optimization overview", "Qiskit Optimization v0.5 Migration Guide", "Qiskit Optimization v0.6 Migration Guide", "Qiskit Optimization Migration Guide", "Release Notes", "QiskitOptimizationError", "QuadraticProgram", "ADMMOptimizationResult", "ADMMOptimizer", "ADMMParameters", "ADMMState", "BaseAggregator", "CobylaOptimizer", "CplexOptimizer", "GoemansWilliamsonOptimizationResult", "GoemansWilliamsonOptimizer", "GroverOptimizationResult", "GroverOptimizer", "GurobiOptimizer", "IntermediateResult", "MeanAggregator", "MinimumEigenOptimizationResult", "MinimumEigenOptimizer", "MultiStartOptimizer", "OptimizationAlgorithm", "OptimizationResult", "OptimizationResultStatus", "RecursiveMinimumEigenOptimizationResult", "RecursiveMinimumEigenOptimizer", "ScipyMilpOptimizer", "SlsqpOptimizationResult", "SlsqpOptimizer", "SolutionSample", "WarmStartQAOAFactory", "WarmStartQAOAOptimizer", "EncodingCommutationVerifier", "MagicRounding", 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83, 86, 87, 88, 93, 94, 96, 97, 98, 100, 101, 102], "2": [2, 11, 16, 19, 22, 29, 35, 36, 41, 47, 49, 67, 72, 82, 88, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 103], "ansatz": [2, 11, 12, 14, 97, 98, 99, 103], "min_eigen_solv": [2, 11, 12, 14, 32, 44, 49, 98, 100, 103], "result": [2, 4, 8, 11, 12, 14, 16, 17, 18, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 44, 46, 48, 49, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102], "onli": [2, 8, 11, 12, 14, 22, 25, 29, 32, 39, 41, 44, 46, 69, 71, 73, 74, 87, 93, 94, 95, 97, 98, 102], "must": [2, 8, 11, 14, 44, 46, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103], "done": [2, 14, 97], "manual": [2, 14, 97], "max_vars_per_qubit": [2, 14, 47, 49, 103], "relaxed_result": [2, 14, 48, 49, 103], "rounding_context": [2, 14, 46, 52, 53, 103], "bryce": [2, 8, 103], "fuller": [2, 8, 103], "et": [2, 8, 38, 44, 72, 98, 99, 103], 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57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103], "an": [4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 22, 23, 24, 25, 27, 28, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 47, 49, 52, 53, 55, 56, 57, 58, 60, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 75, 76, 80, 82, 88, 91, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103], "appropri": [4, 8, 14, 97, 103], "optimizationresult": [4, 14, 17, 22, 23, 24, 26, 27, 28, 31, 33, 34, 37, 38, 39, 40, 41, 48, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 94, 96], "structur": 5, "defin": [5, 8, 14, 19, 26, 29, 40, 69, 76, 80, 82, 92, 93, 95, 96, 97, 98, 99, 101, 102, 103], "follow": [5, 8, 9, 11, 12, 13, 14, 16, 35, 47, 72, 86, 87, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103], "intend": [5, 8], "instanti": [5, 16, 98], "directli": [5, 8, 14, 55, 57, 58, 60, 65, 66, 67, 68, 94, 97, 102, 103], "object": [5, 6, 8, 14, 16, 17, 19, 20, 21, 22, 24, 26, 30, 31, 32, 33, 35, 37, 39, 40, 41, 42, 43, 45, 47, 48, 50, 51, 71, 72, 76, 83, 84, 86, 87, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103], "type": [5, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 43, 44, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 98, 103], "avail": [5, 9, 53, 96, 97, 98, 100, 101, 103], "within": [5, 8, 22, 23, 28, 32, 39, 41, 98, 99, 100, 103], "optim": [6, 9, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 47, 48, 49, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 75, 76, 83, 92, 93, 98], "section": [7, 8, 13, 92, 95, 100, 102, 103], "document": [7, 8, 11, 12, 14, 40, 92, 103], "background": 7, "around": [7, 14, 95, 97, 98], "techniqu": [7, 95, 103], "etc": 7, "both": [7, 8, 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"inequalitytoequality"], [93, "InequalityToEquality"]], "IntegerToBinary": [[70, "integertobinary"], [93, "IntegerToBinary"]], "LinearEqualityToPenalty": [[71, "linearequalitytopenalty"], [93, "LinearEqualityToPenalty"]], "LinearInequalityToPenalty": [[72, "linearinequalitytopenalty"]], "MaximizeToMinimize": [[73, "maximizetominimize"]], "MinimizeToMaximize": [[74, "minimizetomaximize"]], "QuadraticProgramConverter": [[75, "quadraticprogramconverter"]], "QuadraticProgramToQubo": [[76, "quadraticprogramtoqubo"]], "INFINITY": [[77, "infinity"]], "Constraint": [[78, "constraint"]], "LinearConstraint": [[79, "linearconstraint"]], "LinearExpression": [[80, "linearexpression"]], "QuadraticConstraint": [[81, "quadraticconstraint"]], "QuadraticExpression": [[82, "quadraticexpression"]], "QuadraticObjective": [[83, "quadraticobjective"]], "QuadraticProgramElement": [[84, "quadraticprogramelement"]], "Variable": [[85, "variable"]], "from_docplex_mp": [[86, "from-docplex-mp"]], "from_gurobipy": [[87, "from-gurobipy"]], "from_ising": [[88, "from-ising"]], "to_docplex_mp": [[89, "to-docplex-mp"]], "to_gurobipy": [[90, "to-gurobipy"]], "to_ising": [[91, "to-ising"]], "Quadratic Programs": [[92, "Quadratic-Programs"]], "Introduction": [[92, "Introduction"], [94, "Introduction"], [95, "Introduction"], [96, "Introduction"], [97, "Introduction"], [99, "Introduction"], [100, "Introduction"], [101, "Introduction"]], "Loading a QuadraticProgram from an LP file": [[92, "Loading-a-QuadraticProgram-from-an-LP-file"]], "Loading a QuadraticProgram from a docplex model": [[92, "Loading-a-QuadraticProgram-from-a-docplex-model"]], "Directly constructing a QuadraticProgram": [[92, "Directly-constructing-a-QuadraticProgram"]], "Adding/removing linear and quadratic constraints": [[92, "Adding/removing-linear-and-quadratic-constraints"]], "Substituting Variables": [[92, "Substituting-Variables"]], "Converters for Quadratic Programs": [[93, "Converters-for-Quadratic-Programs"]], "Minimum Eigen Optimizer": [[94, "Minimum-Eigen-Optimizer"]], "Converting a QUBO to a SparsePauliOp": [[94, "Converting-a-QUBO-to-a-SparsePauliOp"]], "Solving a QUBO with the MinimumEigenOptimizer": [[94, "Solving-a-QUBO-with-the-MinimumEigenOptimizer"]], "Analysis of Samples": [[94, "Analysis-of-Samples"]], "Grover Optimizer": [[95, "Grover-Optimizer"]], "Grover Adaptive Search": [[95, "Grover-Adaptive-Search"]], "Find the Minimum of a QUBO Problem using GroverOptimizer": [[95, "Find-the-Minimum-of-a-QUBO-Problem-using-GroverOptimizer"]], "Check that GroverOptimizer finds the correct value": [[95, "Check-that-GroverOptimizer-finds-the-correct-value"]], "ADMM Optimizer": [[96, "ADMM-Optimizer"]], "Initialization": [[96, "Initialization"], [98, "Initialization"]], "Example": [[96, "Example"]], "Classical Solution": [[96, "Classical-Solution"]], "Parameters": [[96, "Parameters"]], "Calling 3-ADMM-H algorithm": [[96, "Calling-3-ADMM-H-algorithm"]], "Classical Solver Result": [[96, "Classical-Solver-Result"]], "Quantum Solution": [[96, "Quantum-Solution"]], "Quantum Solver Results": [[96, "Quantum-Solver-Results"]], "Max-Cut and Traveling Salesman Problem": [[97, "Max-Cut-and-Traveling-Salesman-Problem"]], "Weighted Max-Cut": [[97, "Weighted-Max-Cut"]], "Approximate Universal Quantum Computing for Optimization Problems": [[97, "Approximate-Universal-Quantum-Computing-for-Optimization-Problems"]], "Application classes": [[97, "Application-classes"]], "Max-Cut problem": [[97, "Max-Cut-problem"]], "Brute force approach": [[97, "Brute-force-approach"], [97, "id2"]], "Mapping to the Ising problem": [[97, "Mapping-to-the-Ising-problem"], [97, "id3"]], "Checking that the full Hamiltonian gives the right cost": [[97, "Checking-that-the-full-Hamiltonian-gives-the-right-cost"], [97, "id4"]], "Running it on quantum computer": [[97, "Running-it-on-quantum-computer"], [97, "id5"]], "Traveling Salesman Problem": [[97, "Traveling-Salesman-Problem"]], "Vehicle Routing": [[98, "Vehicle-Routing"]], "The Introduction": [[98, "The-Introduction"]], "The Model": [[98, "The-Model"]], "Classical solution": [[98, "Classical-solution"]], "Quantum solution": [[98, "Quantum-solution"]], "Construct the Ising Hamiltonian": [[98, "Construct-the-Ising-Hamiltonian"]], "From Hamiltonian to QP formulation": [[98, "From-Hamiltonian-to-QP-formulation"]], "Classical solution using IBM ILOG CPLEX": [[98, "Classical-solution-using-IBM-ILOG-CPLEX"]], "Quantum solution from the ground up": [[98, "Quantum-solution-from-the-ground-up"]], "Step 1": [[98, "Step-1"]], "Step 2": [[98, "Step-2"]], "Step 3": [[98, "Step-3"]], "Step 4": [[98, "Step-4"]], "Step 5": [[98, "Step-5"]], "Improving Variational Quantum Optimization using CVaR": [[99, "Improving-Variational-Quantum-Optimization-using-CVaR"]], "Portfolio Optimization": [[99, "Portfolio-Optimization"]], "Minimum Eigen Optimizer using SamplingVQE": [[99, "Minimum-Eigen-Optimizer-using-SamplingVQE"]], "Application Classes for Optimization Problems": [[100, "Application-Classes-for-Optimization-Problems"]], "Vertex cover problem": [[100, "Vertex-cover-problem"]], "Knapsack problem": [[100, "Knapsack-problem"]], "How to check the Hamiltonian": [[100, "How-to-check-the-Hamiltonian"]], "Warm-starting quantum optimization": [[101, "Warm-starting-quantum-optimization"]], "Preliminaries: relaxing QUBOs": [[101, "Preliminaries:-relaxing-QUBOs"]], "Standard QAOA": [[101, "Standard-QAOA"]], "Warm-start QAOA": [[101, "Warm-start-QAOA"], [101, "id2"]], "Analysis": [[101, "Analysis"]], "Using Classical Optimization Solvers and Models with Qiskit Optimization": [[102, "Using-Classical-Optimization-Solvers-and-Models-with-Qiskit-Optimization"]], "CplexSolver and GurobiSolver": [[102, "CplexSolver-and-GurobiSolver"]], "Translators between QuadraticProgram and Docplex/Gurobipy": [[102, "Translators-between-QuadraticProgram-and-Docplex/Gurobipy"]], "Indicator constraints of Docplex": [[102, "Indicator-constraints-of-Docplex"]], "Quantum Random Access Optimization": [[103, "Quantum-Random-Access-Optimization"]], "Set up a combinatorial optimization problem": [[103, "Set-up-a-combinatorial-optimization-problem"]], "Encode the problem into a quantum Hamiltonian": [[103, "Encode-the-problem-into-a-quantum-Hamiltonian"]], "Solve the problem using the QuantumRandomAccessOptimizer": [[103, "Solve-the-problem-using-the-QuantumRandomAccessOptimizer"]], "Interpret the solution": [[103, "Interpret-the-solution"]], "Inspect the results of subroutines": [[103, "Inspect-the-results-of-subroutines"]], "Exact Problem Solution with the NumpyMinimumEigensolver": [[103, "Exact-Problem-Solution-with-the-NumpyMinimumEigensolver"]], "Solve the problem using the QuantumRandomAccessOptimizer with MagicRounding": [[103, "Solve-the-problem-using-the-QuantumRandomAccessOptimizer-with-MagicRounding"]], "Alternative: Solve the Problem in Two Explicit Steps": [[103, "Alternative:-Solve-the-Problem-in-Two-Explicit-Steps"]], "Manually solve the relaxed problem.": [[103, "Manually-solve-the-relaxed-problem."]], "Manually perform rounding on the relaxed problem results": [[103, "Manually-perform-rounding-on-the-relaxed-problem-results"]], "Appendix": [[103, "Appendix"]], "How to verify correctness of your encoding": [[103, "How-to-verify-correctness-of-your-encoding"]], "Optimization Tutorials": [[104, "optimization-tutorials"]]}, "indexentries": {"module": [[0, "module-qiskit_optimization"], [1, "module-qiskit_optimization.algorithms"], [2, "module-qiskit_optimization.algorithms.qrao"], [3, "module-qiskit_optimization.applications"], [4, "module-qiskit_optimization.converters"], [5, "module-qiskit_optimization.problems"], [6, "module-qiskit_optimization.translators"]], "qiskit_optimization": [[0, "module-qiskit_optimization"]], "qiskit_optimization.algorithms": [[1, "module-qiskit_optimization.algorithms"]], "qiskit_optimization.algorithms.qrao": 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diff --git a/tutorials/01_quadratic_program.ipynb b/tutorials/01_quadratic_program.ipynb
index bde44054..4b2ba9c7 100644
--- a/tutorials/01_quadratic_program.ipynb
+++ b/tutorials/01_quadratic_program.ipynb
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@@ -155,10 +155,10 @@
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@@ -217,10 +217,10 @@
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@@ -630,10 +630,10 @@
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@@ -671,10 +671,10 @@
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@@ -738,10 +738,10 @@
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@@ -787,10 +787,10 @@
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@@ -827,10 +827,10 @@
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@@ -867,10 +867,10 @@
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diff --git a/tutorials/02_converters_for_quadratic_programs.html b/tutorials/02_converters_for_quadratic_programs.html
index 22014725..a112780b 100644
--- a/tutorials/02_converters_for_quadratic_programs.html
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diff --git a/tutorials/02_converters_for_quadratic_programs.ipynb b/tutorials/02_converters_for_quadratic_programs.ipynb
index b6437a44..34046a6d 100644
--- a/tutorials/02_converters_for_quadratic_programs.ipynb
+++ b/tutorials/02_converters_for_quadratic_programs.ipynb
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diff --git a/tutorials/03_minimum_eigen_optimizer.html b/tutorials/03_minimum_eigen_optimizer.html
index 7f71f9b8..cb7c12cd 100644
--- a/tutorials/03_minimum_eigen_optimizer.html
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 <div class="output_area stderr docutils container">
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 <div class="highlight"><pre>
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diff --git a/tutorials/03_minimum_eigen_optimizer.ipynb b/tutorials/03_minimum_eigen_optimizer.ipynb
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+     "shell.execute_reply": "2024-02-14T16:06:31.182152Z"
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    "outputs": [
@@ -178,10 +178,10 @@
    "execution_count": 4,
    "metadata": {
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-     "iopub.execute_input": "2024-02-09T16:51:38.690987Z",
-     "iopub.status.busy": "2024-02-09T16:51:38.690545Z",
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-     "shell.execute_reply": "2024-02-09T16:51:38.696418Z"
+     "iopub.execute_input": "2024-02-14T16:06:31.185397Z",
+     "iopub.status.busy": "2024-02-14T16:06:31.184993Z",
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+     "shell.execute_reply": "2024-02-14T16:06:31.191095Z"
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@@ -238,10 +238,10 @@
    "execution_count": 5,
    "metadata": {
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-     "iopub.execute_input": "2024-02-09T16:51:38.699435Z",
-     "iopub.status.busy": "2024-02-09T16:51:38.699236Z",
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-     "shell.execute_reply": "2024-02-09T16:51:38.702070Z"
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+     "shell.execute_reply": "2024-02-14T16:06:31.197046Z"
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@@ -264,10 +264,10 @@
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-     "iopub.execute_input": "2024-02-09T16:51:38.704803Z",
-     "iopub.status.busy": "2024-02-09T16:51:38.704609Z",
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-     "shell.execute_reply": "2024-02-09T16:51:38.706936Z"
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@@ -289,10 +289,10 @@
    "execution_count": 7,
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-     "iopub.execute_input": "2024-02-09T16:51:38.710042Z",
-     "iopub.status.busy": "2024-02-09T16:51:38.709685Z",
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-     "shell.execute_reply": "2024-02-09T16:51:38.724926Z"
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@@ -324,10 +324,10 @@
    "execution_count": 8,
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-     "iopub.execute_input": "2024-02-09T16:51:38.727861Z",
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-     "shell.execute_reply": "2024-02-09T16:51:39.140015Z"
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@@ -362,10 +362,10 @@
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-     "shell.execute_reply": "2024-02-09T16:51:39.146816Z"
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@@ -404,10 +404,10 @@
    "execution_count": 10,
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-     "iopub.execute_input": "2024-02-09T16:51:39.150166Z",
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@@ -430,10 +430,10 @@
    "execution_count": 11,
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-     "iopub.execute_input": "2024-02-09T16:51:39.157011Z",
-     "iopub.status.busy": "2024-02-09T16:51:39.156664Z",
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-     "shell.execute_reply": "2024-02-09T16:51:39.160544Z"
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@@ -474,10 +474,10 @@
    "execution_count": 12,
    "metadata": {
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-     "iopub.execute_input": "2024-02-09T16:51:39.163756Z",
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@@ -491,10 +491,10 @@
    "execution_count": 13,
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-     "iopub.execute_input": "2024-02-09T16:51:39.169926Z",
-     "iopub.status.busy": "2024-02-09T16:51:39.169548Z",
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-     "shell.execute_reply": "2024-02-09T16:51:39.175387Z"
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@@ -518,10 +518,10 @@
    "execution_count": 14,
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-     "iopub.execute_input": "2024-02-09T16:51:39.178448Z",
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-     "shell.execute_reply": "2024-02-09T16:51:39.181588Z"
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@@ -553,10 +553,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:51:39.184759Z",
-     "iopub.status.busy": "2024-02-09T16:51:39.184384Z",
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-     "shell.execute_reply": "2024-02-09T16:51:39.188610Z"
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@@ -590,10 +590,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:51:39.191554Z",
-     "iopub.status.busy": "2024-02-09T16:51:39.191188Z",
-     "iopub.status.idle": "2024-02-09T16:51:39.643630Z",
-     "shell.execute_reply": "2024-02-09T16:51:39.642915Z"
+     "iopub.execute_input": "2024-02-14T16:06:31.687971Z",
+     "iopub.status.busy": "2024-02-14T16:06:31.687479Z",
+     "iopub.status.idle": "2024-02-14T16:06:32.163791Z",
+     "shell.execute_reply": "2024-02-14T16:06:32.163026Z"
     }
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    "outputs": [
@@ -601,7 +601,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_2809/365850440.py:1: DeprecationWarning: Using plot_histogram() ``data`` argument with QuasiDistribution, ProbDistribution, or a distribution dictionary is deprecated as of qiskit-terra 0.22.0. It will be removed no earlier than 3 months after the release date. Instead, use ``plot_distribution()``.\n",
+      "/tmp/ipykernel_2824/365850440.py:1: DeprecationWarning: Using plot_histogram() ``data`` argument with QuasiDistribution, ProbDistribution, or a distribution dictionary is deprecated as of qiskit-terra 0.22.0. It will be removed no earlier than 3 months after the release date. Instead, use ``plot_distribution()``.\n",
       "  plot_histogram(samples_for_plot)\n"
      ]
     },
@@ -653,10 +653,10 @@
    "execution_count": 17,
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-     "iopub.execute_input": "2024-02-09T16:51:39.647480Z",
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-     "shell.execute_reply": "2024-02-09T16:51:39.649883Z"
+     "iopub.execute_input": "2024-02-14T16:06:32.168085Z",
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+     "shell.execute_reply": "2024-02-14T16:06:32.170721Z"
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    "outputs": [],
@@ -669,10 +669,10 @@
    "execution_count": 18,
    "metadata": {
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-     "iopub.execute_input": "2024-02-09T16:51:39.653496Z",
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-     "iopub.status.idle": "2024-02-09T16:51:40.238444Z",
-     "shell.execute_reply": "2024-02-09T16:51:40.237728Z"
+     "iopub.execute_input": "2024-02-14T16:06:32.174249Z",
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+     "shell.execute_reply": "2024-02-14T16:06:32.763592Z"
     }
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    "outputs": [
@@ -696,10 +696,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:51:40.241053Z",
-     "iopub.status.busy": "2024-02-09T16:51:40.240689Z",
-     "iopub.status.idle": "2024-02-09T16:51:40.243907Z",
-     "shell.execute_reply": "2024-02-09T16:51:40.243246Z"
+     "iopub.execute_input": "2024-02-14T16:06:32.766975Z",
+     "iopub.status.busy": "2024-02-14T16:06:32.766559Z",
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+     "shell.execute_reply": "2024-02-14T16:06:32.769137Z"
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@@ -714,10 +714,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:51:40.246552Z",
-     "iopub.status.busy": "2024-02-09T16:51:40.246043Z",
-     "iopub.status.idle": "2024-02-09T16:51:40.251176Z",
-     "shell.execute_reply": "2024-02-09T16:51:40.250628Z"
+     "iopub.execute_input": "2024-02-14T16:06:32.772145Z",
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+     "iopub.status.idle": "2024-02-14T16:06:32.776514Z",
+     "shell.execute_reply": "2024-02-14T16:06:32.775865Z"
     }
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    "outputs": [
@@ -745,10 +745,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:51:40.253655Z",
-     "iopub.status.busy": "2024-02-09T16:51:40.253280Z",
-     "iopub.status.idle": "2024-02-09T16:51:40.344922Z",
-     "shell.execute_reply": "2024-02-09T16:51:40.344178Z"
+     "iopub.execute_input": "2024-02-14T16:06:32.779007Z",
+     "iopub.status.busy": "2024-02-14T16:06:32.778631Z",
+     "iopub.status.idle": "2024-02-14T16:06:32.871050Z",
+     "shell.execute_reply": "2024-02-14T16:06:32.870277Z"
     }
    },
    "outputs": [
@@ -756,7 +756,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_2809/365850440.py:1: DeprecationWarning: Using plot_histogram() ``data`` argument with QuasiDistribution, ProbDistribution, or a distribution dictionary is deprecated as of qiskit-terra 0.22.0. It will be removed no earlier than 3 months after the release date. Instead, use ``plot_distribution()``.\n",
+      "/tmp/ipykernel_2824/365850440.py:1: DeprecationWarning: Using plot_histogram() ``data`` argument with QuasiDistribution, ProbDistribution, or a distribution dictionary is deprecated as of qiskit-terra 0.22.0. It will be removed no earlier than 3 months after the release date. Instead, use ``plot_distribution()``.\n",
       "  plot_histogram(samples_for_plot)\n"
      ]
     },
@@ -781,10 +781,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:51:40.348737Z",
-     "iopub.status.busy": "2024-02-09T16:51:40.348339Z",
-     "iopub.status.idle": "2024-02-09T16:51:40.453673Z",
-     "shell.execute_reply": "2024-02-09T16:51:40.453003Z"
+     "iopub.execute_input": "2024-02-14T16:06:32.875562Z",
+     "iopub.status.busy": "2024-02-14T16:06:32.875309Z",
+     "iopub.status.idle": "2024-02-14T16:06:32.984588Z",
+     "shell.execute_reply": "2024-02-14T16:06:32.983866Z"
     }
    },
    "outputs": [
@@ -792,14 +792,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_2809/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
+      "/tmp/ipykernel_2824/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
       "  import qiskit.tools.jupyter\n"
      ]
     },
     {
      "data": {
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+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:06:32 2024 UTC</td></tr></table>"
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@@ -852,7 +852,25 @@
   "widgets": {
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     "state": {
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+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
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@@ -905,7 +923,7 @@
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@@ -920,31 +938,13 @@
        "_view_name": "HTMLView",
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        "description_allow_html": false,
-       "layout": "IPY_MODEL_061bf5c8619e499d983085ce8f341bb1",
+       "layout": "IPY_MODEL_4b3bc656947f4fd7a3510c9d4fc61c1c",
        "placeholder": "​",
-       "style": "IPY_MODEL_fdef2d007834471fbcd8dd059b0c577c",
+       "style": "IPY_MODEL_0362caef9918471ca4e937e0615b932c",
        "tabbable": null,
        "tooltip": null,
        "value": "<p style='font-family: IBM Plex Sans, Arial, Helvetica, sans-serif; font-size: 20px; font-weight: medium;'>Circuit Properties</p>"
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-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
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diff --git a/tutorials/04_grover_optimizer.html b/tutorials/04_grover_optimizer.html
index 26b2daa6..d42c89dc 100644
--- a/tutorials/04_grover_optimizer.html
+++ b/tutorials/04_grover_optimizer.html
@@ -454,11 +454,11 @@ <h2>Grover Adaptive Search<a class="headerlink" href="#Grover-Adaptive-Search" t
 <li><p>The Grover diffusion operator <span class="math notranslate nohighlight">\(D\)</span>, that multiplies the amplitude of the <span class="math notranslate nohighlight">\(|0\rangle_n\)</span> state by -1.</p></li>
 </ol>
 <p>While implementations of GAS vary around the specific use case, the general framework still loosely follows the steps described below.</p>
-<div><p><img alt="97d377647d7348eb8f3ced0b7f106af3" class="no-scaled-link" src="../_images/GAS-Algorithm.png" style="width: 400px;" /></p>
+<div><p><img alt="90fffba927f94028af44145ae4c503c6" class="no-scaled-link" src="../_images/GAS-Algorithm.png" style="width: 400px;" /></p>
 </div><p><code class="docutils literal notranslate"><span class="pre">GroverOptimizer</span></code> uses <code class="docutils literal notranslate"><span class="pre">QuadraticProgramToNegativeValueOracle</span></code> to construct <span class="math notranslate nohighlight">\(A_y\)</span> such that it prepares a <span class="math notranslate nohighlight">\(n\)</span>-qubit register to represent the equal superposition of all <span class="math notranslate nohighlight">\(|x\rangle_n\)</span> and a <span class="math notranslate nohighlight">\(m\)</span>-qubit register to (approximately) represent the corresponding <span class="math notranslate nohighlight">\(|Q(x)-y\rangle_m\)</span>. Then, all states with <span class="math notranslate nohighlight">\((Q(x) - y)\)</span> negative should be flagged by <span class="math notranslate nohighlight">\(O_y\)</span>. Note that in the implementation discussed, the oracle operator is actually independent of <span class="math notranslate nohighlight">\(y\)</span>, but
 this is not a requirement. For clarity, we will refer to the oracle as <span class="math notranslate nohighlight">\(O\)</span> when the oracle is independent of <span class="math notranslate nohighlight">\(y\)</span>.</p>
 <p>Put formally, <code class="docutils literal notranslate"><span class="pre">QuadraticProgramToNegativeValueOracle</span></code> constructs an <span class="math notranslate nohighlight">\(A_y\)</span> and <span class="math notranslate nohighlight">\(O\)</span> such that:</p>
-<div><p><img alt="cb35a1d614014418a39b3c754ec9a806" class="no-scaled-link" src="../_images/a-o-dfn.png" style="width: 400px;" /></p>
+<div><p><img alt="679d5a362a1541d19a30a2e235386083" class="no-scaled-link" src="../_images/a-o-dfn.png" style="width: 400px;" /></p>
 </div><p>where <span class="math notranslate nohighlight">\(|x\rangle\)</span> is the binary encoding of the integer <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>At each iteration in which the threshold <span class="math notranslate nohighlight">\(y\)</span> is updated, we adapt <span class="math notranslate nohighlight">\(A_y\)</span> such that the function values are shifted up or down (for minimum and maximum respectively) by <span class="math notranslate nohighlight">\(y\)</span>. For example, in the context of finding the minimum, as the value of <span class="math notranslate nohighlight">\(y\)</span> decreases, the search space (negative values) also decreases, until only the minimum value remains. A concrete example will be explored in the next section.</p>
 </section>
@@ -535,10 +535,10 @@ <h2>Find the Minimum of a QUBO Problem using GroverOptimizer<a class="headerlink
 </pre></div></div>
 </div>
 <p>This results in the optimal solution <span class="math notranslate nohighlight">\(x_0=1\)</span>, <span class="math notranslate nohighlight">\(x_1=0\)</span>, <span class="math notranslate nohighlight">\(x_2=1\)</span> and the optimal objective value of <span class="math notranslate nohighlight">\(-6\)</span> (most of the time, since it is a randomized algorithm). In the following, a custom visualization of the quantum state shows a possible run of <code class="docutils literal notranslate"><span class="pre">GroverOptimizer</span></code> applied to this QUBO.</p>
-<div><p><img alt="c848ffc3b51a4045a959fac6a57611c7" class="no-scaled-link" src="../_images/GAS-QUBO-Example.png" style="width: 800px;" /></p>
+<div><p><img alt="7d4741c185524357a6725871a38aae0a" class="no-scaled-link" src="../_images/GAS-QUBO-Example.png" style="width: 800px;" /></p>
 </div><p>Each graph shows a single iteration of GAS, with the current values of <span class="math notranslate nohighlight">\(r\)</span> (= iteration counter) and <span class="math notranslate nohighlight">\(y\)</span> (= threshold/offset) shown in the title. The X-axis displays the integer equivalent of the input (e.g. ‘101’ <span class="math notranslate nohighlight">\(\rightarrow\)</span> 5), and the Y-axis shows the possible function values. As there are 3 binary variables, there are <span class="math notranslate nohighlight">\(2^3=8\)</span> possible solutions, which are shown in each graph. The color intensity indicates the probability of measuring a certain result (with bright
 intensity being the highest), while the actual color indicates the corresponding phase (see phase color-wheel below). Note that as <span class="math notranslate nohighlight">\(y\)</span> decreases, we shift all of the values up by that amount, meaning there are fewer and fewer negative values in the distribution, until only one remains (the minimum).</p>
-<div><p><img alt="6f74f18299b444228732c78989bd6ce4" class="no-scaled-link" src="../_images/color-wheel-phase.png" style="width: 200px;" /></p>
+<div><p><img alt="f09898c1f08c40deb3d107d9ad78b33c" class="no-scaled-link" src="../_images/color-wheel-phase.png" style="width: 200px;" /></p>
 </div></section>
 <section id="Check-that-GroverOptimizer-finds-the-correct-value">
 <h2>Check that GroverOptimizer finds the correct value<a class="headerlink" href="#Check-that-GroverOptimizer-finds-the-correct-value" title="Permalink to this heading">#</a></h2>
@@ -579,7 +579,7 @@ <h2>Check that GroverOptimizer finds the correct value<a class="headerlink" href
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diff --git a/tutorials/05_admm_optimizer.ipynb b/tutorials/05_admm_optimizer.ipynb
index d767400f..d513c8c7 100644
--- a/tutorials/05_admm_optimizer.ipynb
+++ b/tutorials/05_admm_optimizer.ipynb
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diff --git a/tutorials/06_examples_max_cut_and_tsp.html b/tutorials/06_examples_max_cut_and_tsp.html
index f300ea8a..0547e63d 100644
--- a/tutorials/06_examples_max_cut_and_tsp.html
+++ b/tutorials/06_examples_max_cut_and_tsp.html
@@ -815,7 +815,7 @@ <h3>Running it on quantum computer<a class="headerlink" href="#Running-it-on-qua
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 energy: -1.4996861455587294
-time: 3.03641676902771
+time: 3.004215717315674
 max-cut objective: -3.999686145558729
 solution: [0 1 0 1]
 solution objective: 4.0
@@ -1177,7 +1177,7 @@ <h3>Running it on quantum computer<a class="headerlink" href="#id5" title="Perma
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 energy: -7326.02469952184
-time: 14.17830228805542
+time: 14.182559490203857
 feasible: True
 solution: [1, 2, 0]
 solution objective: 202.0
@@ -1252,7 +1252,7 @@ <h3>Running it on quantum computer<a class="headerlink" href="#id5" title="Perma
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/tmp/ipykernel_10116/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
+/tmp/ipykernel_10107/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
   import qiskit.tools.jupyter
 </pre></div></div>
 </div>
@@ -1260,7 +1260,7 @@ <h3>Running it on quantum computer<a class="headerlink" href="#id5" title="Perma
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area rendered_html docutils container">
-<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Fri Feb 09 16:53:21 2024 UTC</td></tr></table></div>
+<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:08:15 2024 UTC</td></tr></table></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
@@ -1277,7 +1277,7 @@ <h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr
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diff --git a/tutorials/06_examples_max_cut_and_tsp.ipynb b/tutorials/06_examples_max_cut_and_tsp.ipynb
index b79337b6..f5a2e3a2 100644
--- a/tutorials/06_examples_max_cut_and_tsp.ipynb
+++ b/tutorials/06_examples_max_cut_and_tsp.ipynb
@@ -109,10 +109,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:43.809274Z",
-     "iopub.status.busy": "2024-02-09T16:52:43.808729Z",
-     "iopub.status.idle": "2024-02-09T16:52:44.990531Z",
-     "shell.execute_reply": "2024-02-09T16:52:44.989694Z"
+     "iopub.execute_input": "2024-02-14T16:07:37.501312Z",
+     "iopub.status.busy": "2024-02-14T16:07:37.501115Z",
+     "iopub.status.idle": "2024-02-14T16:07:38.711870Z",
+     "shell.execute_reply": "2024-02-14T16:07:38.711192Z"
     }
    },
    "outputs": [],
@@ -145,16 +145,16 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:44.993821Z",
-     "iopub.status.busy": "2024-02-09T16:52:44.993311Z",
-     "iopub.status.idle": "2024-02-09T16:52:45.182626Z",
-     "shell.execute_reply": "2024-02-09T16:52:45.181932Z"
+     "iopub.execute_input": "2024-02-14T16:07:38.715222Z",
+     "iopub.status.busy": "2024-02-14T16:07:38.714657Z",
+     "iopub.status.idle": "2024-02-14T16:07:38.902307Z",
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",
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6oDAgImGvvr6eVatW4Xa7WbduHV6vl3HjxnHLLbfgdDopLCwM9RIljAWOFJaVlSkMdEFhQETCUm1tbXAS4HvvvYfP52PSpEl89atfxeFwkJ+fH+olygCRm5uL1WqltLSU2bNnh3o5YUlhQETCxvHjx1mxYgXLly9vNwnw61//OsXFxeTk5IR6iTIARUdHk5eXp14DZ6EwICIhFZgE6Ha7ef/994OTAL/5zW9SVFTEkCFDQr1EGQR0vPDsFAZEpN+Vl5cH2wAHJgHOnj2bRx55hEWLFpGWlhbqJcogY7fb2bJlS6iXEbYUBkSkXxw+fDg4CCgwCfCSSy7RJEDpFwUFBfz1r3/V8cIuKAyISJ/obBJgXFycJgFKSNjtdlpaWqiuriY7OzvUywk7CgMi0msMw2DPnj3BSwCHDh0iMTGRRYsW8ZWvfIVLLrmEuLi4UC9TItDpxwsVBjpSGBCRC+L3+9m5c2cwAJw+CfDee+9l9uzZmgQoIZeXl4fFYqG0tJTp06eHejlhR2FARHrM7/ezbds2XC4Xbrc7OAmwqKgIp9PJjBkzNAlQworNZiM3N1cnCrqgP60i0i1tbW1s2bIlOAr4+PHjZGVl4XA4cDqdmgQoYU/HC7umMCAiXWptbW03CbC2tpahQ4dy+eWX43Q6ufjiixUAZMAoKChgx44doV5GWFIYEBnEDMPgpZdeYtu2bTidThwOxznfvFtaWtiwYQMul4tVq1ZRX1/PsGHDuPbaa3E6nYwbN06DgGRAstvtvP322xiGof+Hz6AwIDJIVVRU8K//+q+Ul5czffp0nnnmGe644w4eeOABsrOzO/yFaBgGTz31FH//+99pampi5MiR3HjjjTidTi666CL95SkDnt1up7GxkRMnTpCRkRHq5YQVhQGRQepHP/oRra2tbNiwgdzcXJ577jl++tOfEhUVxVNPPdVpGMjNzeWWW27B4XAwfPjw0C1epA8EjheWlpYqDJxBYUBkEPL7/Xz88cdMnDiR3NxcAL7whS9w9OhRfvKTn/DUU091uFxgsVi49dZbQ7FckX4RmHRZWlrKlClTQrya8KLKH5EB5MSJEzzzzDM8+eSTVFVVdXm/+vp6mpqa2vX4t9lsXH311dTX1/OPf/wDMHcDAnQZQAa7uLg4srOzdaKgEwoDIgOA3+/ntdde47LLLuPxxx/nySef7HIcq2EYpKSkEB8fT1VVFZWVlcHbCgsLmTVrFn/605+CjysSSQoKChQGOqEwIDIAWK1WmpubueKKK9i0aRNxcXGsX7++0zfzwPeKi4vZsWMHe/bsCd4WFxfHjBkz2L17NwBRUVH98wJEwoTdbu8ySEcyhQGRAeIzn/kM3/rWtxgxYgQOh4M33niDurq6DvcL1AJce+21+P1+3n333eBtcXFxHDx4kAkTJuD1evtt7SLhQjsDnVMYEBkgkpKSgj3+b7nlFtasWcOhQ4fa3ccwjGAdgN1u5+qrr+all14KXhY4dOgQO3bsYMqUKdhstnY1AyKRwG63U1dXh8fjCfVSworCgMgAEijyu+aaa4LHBk+/VGCxWNqdEvjGN77BpZdeyr333suiRYuYNm0aEydO5IYbbmj3eCKR4vTjhfJPCgMiA4zP5wPA4XDw+uuv09DQELytqamJ3/zmNzzzzDMApKWl8fTTT/Pqq69y+eWX88Ybb/DXv/6VrKyskKxdJNQKCgoAhYEzKQyIhAm/388HH3zAM888E3zD70zg0/ytt97K6tWrOXLkCAAnT54kJiaGl19+meeff56WlhbAPFI4a9YsHn74YebPn9/3L0QkjCUmJpKRkaEwcAY1HRIJocAkQLfbTUlJCTU1NWRlZXHFFVcwcuTITrfxAycA/uVf/oWlS5fyyCOPcOjQIdLS0nj11Vd54oknyMjIIDY2tr9fjsiAoCLCjhQGRPpZZ5MAc3Nz+fSnP92tSYA+n4/nn3+e5557Dp/Px+7du7nsssu44447SExMZMaMGf34akQGHo0y7khhQKQfeL1eNmzYwPLly4OTAO12O9deey0Oh4Px48d3u5jPMAxKS0tZsGABr7zyCoWFhX28epHBxW63s27dulAvI6woDIj0kaamJtatW4fL5WLNmjU0NjYGJwE6HA5GjRp1XtX8MTExfPe73+2DFYtEBrvdzokTJ6ivrycpKSnUywkLCgMivaihoYHVq1fjdrtZu3YtLS0tjBkzhmXLluFwOBgxYkSolygS8QLHC8vKyhg3blyIVxMeFAZELpDH42HlypW43W42bNhAa2srEydO5Pbbb8fhcASPMolIeDj9eKHCgElhQOQ8HD9+nBUrVuB2u9m0aRN+v58pU6Zw991343A4gmODRST8pKSkkJKSoiLC0ygMiHTTsWPHKCkpweVy8f777wMwY8YMHnjgAYqLi8nMzAzxCkWkuyZNmnTWUzuRxmJ0ozm5x+MhNTWV2tpaUlJS+mNdImHh6NGjuN1uXC4X27dvJyoqitmzZ+N0Olm8eDHp6emhXqKISJe6+/6tnQGRM5SWlgYDwK5du7DZbMydO5fHH3+chQsXKhCLyKCjMCAC7N+/PxgA9u3bR1xcHPPnz+emm25iwYIFJCQkhHqJIiJ9RmFAIpJhGOzbtw+Xy4XL5eLgwYMkJCSwcOFCvvzlLzNv3jzi4uJCvUwRkX6hMCARwzAMdu3aFQwAR44cITk5mcWLF3PPPfcwZ84cbDZbqJcpItLvFAZkUPP7/Wzfvj14CaCyspL09HSKiopwOp3MnDmT6Gj9MRCR9rxeL36/P2J2CPW3oAw6bW1tbN26NTgJsLq6mszMTBwOBw6Hg2nTpgUn/4mIdOY3v/kNNTU1PPjggxHx94XCgAwKra2tbN68OTgJ8OTJk+Tm5nLZZZfhdDp1plhE2vnVr37FggULGDVqFH6/H4vFgtVqDc4Lyc7O5plnnuH6669n5MiRIV5t31MYkAErMAnQ5XKxatUq6urqKCgo4JprrsHpdPZoEqCIRJYHH3yQH/7wh4wZM6bdBwWv10tpaSlVVVXs2LGD7du3KwyIhJumpibWr1/P8uXLg5MAR4wYwQ033IDD4WD06NEKACJyTpdccgkvvvgiZWVlbNu2jT179nDkyBE8Hg8Wi4Xs7GzGjx8fMceKFQYk7DU0NLBmzRpcLle7SYBf+MIXcDgcEZHaRaR3zZkzh8cee4wTJ05QUFDApz71KUaNGsWwYcPIyckhIyODuLi4iOkyqjAgYcnj8bBq1Srcbjfr16+ntbWVCRMmcNttt+FwOIIjSEVEzofdbmfo0KG8/vrrpKWlER0dTUxMTMTuLCoMSNg4ceJEcBLgxo0b8fv9TJ48mbvvvpvi4mKGDh0a6iWKyCBx8cUXA5CcnBwxlwLORmFAQiowCdDtdrN161YApk+fzv33309xcTFZWVkhXqGIDEaTJk3iySefJCkpKdRLCQuaWij9rqKiot0kQKvVyuzZs3E4HBQVFUXMNToRCa3jx49z+PBhJk2aNGh7CWhqoYSVMycBxsTEcMkll/DYY4+xaNEihUwR6Xf33nsvO3fu5C9/+QvDhg0L9XJCSmFA+syBAwdwuVy43W727t1LbGws8+fPZ+nSpSxYsIDExMRQL1FEItiiRYsYNWpUcCaJ3+/v0JzMMIyIKCrUZQLpNYFJgIEdgAMHDpCQkMCCBQtYsmQJl1xyCfHx8aFepojIWdXX13P8+HHS0tIG/HueLhNIvzAMg927dwcnAZaVlZGcnMyiRYu4++67mTt3riYBikhYq6+v5/e//z3vvvsue/fuxev1kpaWRn5+PsXFxXzlK18hJiYm1MvsUwoD0mN+v58PP/wweAmgoqKCtLQ0ioqK+OY3v8nMmTMH/R8cERkcysrKuOeee/joo4+YMmUK11xzDVlZWbS0tLB161b+8z//k8rKSr773e+Geql9SmFAuqWtrY33338ft9uN2+2murqaIUOGBCcBTp8+fdBW44rI4OT3+3nuuefYvn07P//5z5kyZQrp6entxpq/+eab3HjjjTz55JODunZAYUC65PP52LRpE263mxUrVnDixAlycnK47LLLcDgcTJ48WZMARWTAslqtPPfcc/zkJz9hyZIlnd5n1qxZREVFcfjwYQoLC/t5hf1HYUDa8Xq9vPfee8FJgB6Ph4KCAq666iqcTicTJkwY1OlYRCJLVFQUDQ0NHb7f2tpKdXU19913HzNmzBj0tU8KA0JzczPr1q3D5XKxevVqGhsbGT58OJ/97GdxOp2aBCgig9att97KD3/4QzZv3syVV15JcnIyx48fZ9euXaxcuZL9+/fz1FNPDfp26AoDEaqxsZE1a9awfPly1q1bR3NzM6NHj9YkQBGJKA888ABxcXG8+eabvPbaaxw9ehS/3092djYLFy7kF7/4BYsXLw71Mvuc+gxEEI/Hw+rVq3G5XGzYsAGv18uECRNwOp2aBCgiEa20tJSqqiqysrLIzs4mLi4u1EvqFeozIIA5CXDlypW4XC42btxIW1sbkydP5q677tIkQBGRU+x2e7sPRIZhBLsPRsJlUoWBQai6ujo4CXDLli0ATJs2jfvvv5+ioiKys7NDvEIRkfAWKSEgQGFgkAhMAnS73Wzbtg2r1cqsWbN46KGHKCoqIiMjI9RLFBGRMKUwMICVlZUF5wDs3LmTmJgY5s6dy6OPPsrixYtV3yEiIt2iMDDAHDx4MDgH4PRJgDfeeCMLFy7UJEAREemxwRUGfD7wesEwIDYWogf+yzMMg48//ji4A7B///7gJMAvfvGLzJs3T5MARUQu0N69e/nBD37A97//fbKyskK9nH43sN8tP/4Y1q2D3bvh/ffhwAHw+83brFaw22HaNJgwAebMMX8dAAKTAAMBoLS0lKSkJBYtWsRdd92lSYAiIr0sNjaWbdu2cfjwYYWBAaG1Fdxu+OMfYfVqaGw0vx8dbe4GBHrlt7XBnj2wc6f5+7g4MxD827/Bpz5l3jeM+P1+duzYEZwEePToUVJTUykqKuLBBx9k1qxZmgQoItJHhg4ditVqpbS0lBkzZoR6Of1uYIWBbdvgW98yf21rg+RkyMuDcx3/MAyor4eVK80AMW4cfO97MHdu/6y7C36/n/fffx+Xy0VJSQnHjh1jyJAhFBcX43Q6NQlQRKSf2Gw2cnNzKS0tDfVSQmJghIGWFvjZz8yvujrIzDQ/6XeXxWIGh+Rks6Zg1y5zh+CWW+C++6Afi+58Ph+bN2/G7XZTUlLCiRMnyM7OZsmSJTidTk0CFBEJkYKCAoWBsFVfD3fdBe+8Y27t5+efeyfgbGw2czfh5EkzXGzbBr/8JfThOfzAJEC3283KlSvxeDzk5+dz1VVX4XA4mDBhggKAiEiI2e12Pvzww1AvIyTCOww0NsKXvwwlJTBkCPRW1bzFAunpkJAAa9bAsmXw29+a3+slgUmAbreb1atX09DQQGFhIddddx1Op5MxY8ZEVHcrEZFwZ7fb+dvf/hZsQxxJwjcM+P3mFn5JSc8vC3RXbCxkZ8OmTXDHHfC7313QccTAJECXy8XatWtpbm5m1KhR3HTTTTidTkaMGBFx/4OJiAwUdrudpqYmjh8/zpAhQ0K9nH4VvmHglVfgzTchLa1vgkCAzWbuOqxcCb/6Fdx2W49+vK6ujtWrV7N8+fLgJMDx48fzpS99CYfDwbBhw/po4SIi0psCg4pKS0sVBsLCkSPw1FPmPycl9f3zxcebtQlPPw1FRTBmzFnvfvLkyXaTAH0+H5MnT+bOO++kuLiYvLy8vl+ziIj0qvz8fMAMA1OnTg3tYvpZeIaBH/0Ijh41C/36S2amGUK++12zfuAMNTU1wUmAmzdvBmDq1Knce++9FBcXaxKgiMgAFxsbS3Z2dkSeKAi/MHD0KLz1lnncr5sV9ruamniztpbNjY2Ue72kRkUxKT6eO7KzGdbdTn0WC6SmmgWFe/bA2LFUVlYGJwF+8MEHWCwWTQIUERnECgoKKCsrC/Uy+l34hYG//AVqa3u0K/B/NTVsa2piSXIyozMyqPH5eOn4cZYeOMD/DR/ORd3tNpiURNvhw2z/1rf4SXIyO3bsICYmhjlz5vDII4+wePFiUlNTz/OFiYhIuLPb7ezbty/Uy+h34RUGDMNsMxwd3e1dAYCbMjL4Xnw8MadV6l+aksLn9u/n/6qrefLUdaCutHi91Hk8eOrqiKuvJ+ntt8m97TZuePJJFi5cSFJ/1C2IiEjI2e12XC5XxB0vDK8wcOSIeZmgh2++kxMSOnxvmM3GyNhYDni9HW4zgJaWlmAAaGlpwWq1kpSUREpuLrmGwfe/+EW4+OLzfSUiIjIA2e126uvr8Xg8EbUTHF5hYPduaGoy2wZfIMMwOO7zMfLUJQIDsxFQIAB4vV6irFaSkpPJzsoiMSkJq8Vi9jc4etRci8KAiEhEOf14ocJAqHz0kfnrBTT+Cfi7x0OVz8ctqalUVlbiqaujtbWVqKgokpOTyc3JISEx0QwApwtcnti9+4LXICIiA0tBQQFghoGLI+gDYXiFgepqs27gAhjA7pMnebK0lOGGwfjqamqjo0lOSSElOZmExETOeRXIMMy1iIhIRElISCAjIyPijheGVxhobb3ghyj1eLjjk0+Itli4Nz6elKQkEhMSsNlsREdHd68oxGKB5uYLXouIiAw8drs94o4XhlcYuMDLA/VtbXz9yBGarFaeyc5mqGHQ6vVy7Ngx/KftOMRERxNjs2Gz2bDFxGCz2YK/j7JazZ2BmJgLfTUiIjIA2e12Dh06FOpl9KvwCgNJSec9ntjr93NvWRnlfj8P2WzMzMsL1gMYQJvPh9frxdvaav7q9dLS3ExdXR1tbW3Bx4mKiiLH52PbBx+w55e/pKCggIKCAux2O2lpaRF11EREJBLZ7XbWrFkT6mX0q/AKA2PGmJ/K/f4e9RnwGwYPHTnC9sZG/iM3l6FVVXhbWog7NeDIAkRHRxMdHU3HQ4jQ1tb2z5DQ0kJUTQ2fREXx5z//mZqamuD9EhISsNvtwXAQ+NVut5OZmYm1B2sWEZHwZLfbOXnyJHV1dST3wum2gSC8wsD48eZY4eZm6KR3QFeerqpiVX09i5KSaLRaWdXWxq4TJ0g49RhXnON4SFRUFPFRUcTHxYHXC34/t/7nf3LrnDk0NjZy5MgRSktLKSsrC3794x//oKKiAuPU5QebzRbcRTgzLOTm5hLdCyckRESk7wVOFJSVlTF+/PgQr6Z/hNc71MiRkJICHk+PwsCeU8V+q+rrWVVfT3NrK9HV1cE34HOFgXYaG80phuPGAeZuwOjRoxk9enSHu3q9Xo4ePRoMCqWlpZSWlrJmzRrKy8vx+XwAWK1W8vLy2u0kBEJDfn4+sd1tlywiIn3u9OOFCgOhEBMDV18Nzz1nXi7o5vX5XxYWtvv9wUOHiImODo6j7JGGBrj2WnNo0TnYbDYKCwspPOP5Afx+PxUVFcGQEPj1/fff5/XXX6elpSV43+zs7A5BIfBrYmJiz1+DiIict5SUFFJTUyPqREF4hQGA66+HF14w35TPcyaAzWaj+XyOBjY1mScaPve583re0wV2A/Ly8pg9e3a72wzDoKamJriTELj0sG/fPtxuN3V1dcH7pqent9tJOD00pKamqqBRRKQP2O32iOo1EH5hYNIkmDHDHCWcmHhepwtibTY8Hg8GnLvB0OmOH4eJE2HBgh4/Z09YLBYyMzPJzMxk2rRpHW73eDztgkLg1w0bNnD8+PHg/RITEzstaCwoKFBBo4jIBVAYCDWLBR58ELZuhZMnIT29xw9hi43F7/fj8/mI6W7hnscDNpv53CF+E01JSWHixIlMnDixw22NjY3tChkDoWHHjh1UVlZ2WtB45smH3NxcoqKi+vtliYgMGHa7nY0bN4Z6Gf0m/MIAwJw5cOut8NOfmrsDNluPftx26v7elpbuhQGfzwwDS5fCZZedz4r7TUJCAmPGjGHMmDEdbvN6vZSXl3fYUVi9ejVHjhwJ9lOIiopqV9B4emjIz88P/vsTEYlUBQUF1NTU0NjYGDyZNpiFZxgAuPdeWLECduyAoUOhB59kbTYbFouFFq/33AV4fj9UVsKIEfDtb59306NwYLPZGD58OMOHD+9wW1tbG5WVlR0uP2zZsoW//vWvwYJGi8Vy1oLGSPhDISJy+vHCzj58DTbhGwYSE+F//9f8tH7wYI8CgQWwxcTg9XrPfke/H8rLITfXfK6MjAtedrgK7Abk5eUxZ86cdrcZhkF1dXW7YsbS0lL27NnD8uXLqa+vD943IyOjy8sPKSkpKmgUkUEhMMpYYSAcXHQR/N//mZcMPvkEMjPNHgDdYIuNxXva8b0OWlrg2DHIy4Nnn4XJk3tnzQOQxWIhKyuLrKwspk+f3u42wzC6XdCYlJTUYSch8M+ZmZkKCiIyYKSlpZGYmBgxxwvDOwyA2fznj3+EBx6AtWuhvh6GDDlnkZ/t1ImCDgzDPDXQ3GyeWvjRj4INhqQji8VCamoqqampnc72DhQ0nhkUtm/fTmVlZfB+sbGxXe4o5OTkqKBRRMKKxWKJqBMF4R8GAAoLzUDw61/Dj39sbu3HxUFaWpeTDmNtNlpbW/EbhjmwqK0NamvNDoPJyfDQQ/CVr5jtj+W8naug8cxWzqWlpaxcuZLy8vJ2BY35+fmdhoW8vDwVNIpISCgMhKPoaPjyl8HphN/9Dl55xSz8MwzzDT0+3vz11I6BLSqK+LY22ioqsPr95mOkp5sNhW66CSZMCOGLiQw2m40RI0YwYsSIDre1tbVRUVHRoZXz5s2bee2114L1HhaLhZycnE5bOaugUUT6kt1uZ/v27aFeRr8YOGEgYORIeOwx87TB66/D6tXw/vtQU2NeQjj1xh8H2AyDpsREYhYuhPnz4ZprzEsMEnKB3YDOWkb7/X6qq6s7XH7YvXs377zzDg0NDcH7ZmRkdDlJMiUlpT9fkogMMgUFBVRWVtLS0jLoZ8gMvDAQkJJifsK/6SZzd+DoUfPUQXMzGAbW2FgefPhhim+6iS/ffnuoVys9YLVayc7OJjs7u9OCxtra2g6tnEtLS1m3bh0nTpwI3jc5ObnLVs5DhgxRQaOInFXgRMGRI0cYOXJkiFfTtwZuGDidxWKeCsjLa/fthLFjORQh13sihcViIS0tjbS0NCZNmtTh9oaGhk4LGj/44AOqqqqC94uLi+u0RqGgoIDc3Fy1chaRdscLFQYGsMLCQvbu3RvqZUg/SkxMZOzYsYwdO7bDbS0tLRw5cqRDK+eSkhLKy8vxn7rEFB0d3aFDY2BHYejQoSpoFIkQQ4YMIS4uLiKKCAd1GBg+fDjvvvsuhmFoS1iIjY1l5MiRnSZ8n8/XoaCxrKyMTZs28eqrr7YraMzNze20l0JBQQHx3eyDISLhL5KOFw7qMFBYWEhjYyPHjh0jOzs71MuRMBYdHR18Yz9ToKDxzJMPO3fu5O2336axsTF43yFDhnQ5STJUBY2NjY289NJLbNu2jaSkJK6++mpmzJih3g4i3aAwMAgEevQfOnRIYUDO2+kFjTNmzGh3m2EYnDx5skMx46FDh1izZg0nT54M3jclJaXLxksZGRl9tntVVVXF5s2baWlp4X/+5384cOAAv/jFL0hOTu6T5xMZTAoKCli+fHmol9HnBnUYyM/PJzo6moMHDzJr1qxQL0cGIYvFQnp6Ounp6UzupKV1fX19twoa4+PjOwSFwD/n5ORcUEGj3W7n4YcfpqCggBUrVjBu3LguL2cELqnt3LmTH//4x5SUlDB69GgeeughiouLz3sNIgOV3W6noqKC1tZWYmJiQr2cPjOow0BUVBQFBQUcOnQo1EuRCJWUlMS4ceMY10nL6+bm5g4jp0tLS3G5XBw9ejRY0BgTE3PWgsZz/QUV+HMA5hGpYcOGEd1F506LxcKRI0dYsmQJs2fP5oknnmDNmjV8/etf54UXXui0JbXIYGa32/H7/Rw9epRhw4aFejl9ZlCHATAvFRw8eDDUyxDpIC4u7qwFjUePHu1w+eG9997jL3/5S7Cg0Wq1kpubyyOPPMKMGTPOuoNQW1tLS0sLeWccwT3T9773PXJycvjjH/9IQkICV199NdOmTeOVV15hwoQJOnYpESVwvLC0tFRhYCArLCzknXfeCfUyRHokOjo6+On/TH6/n2PHjrXbUcjLy+vyTTqw9f/xxx8TExNz1vqZ6upq1q5dy/XXXx9s9ZySksINN9zAm2++yb//+7/3yusTGSiysrKw2WyDvogwIsJARUUFzc3NxMXFhXo5IhfMarWSk5NDTk4OM2fOPOf9/X4/UVFR7Ny5k4yMDNLS0jrcJxAY9u/fj8fjYeLEicHbfD4fcXFxwWJIv9+v3QGJGFarlfz8/EEfBgb9n+jhw4djGAaHDx8O9VJEQmrHjh0UFBR0OEXg9/uDEyQPHjxIYmIiQ06b4WEYBmVlZeTk5PTrekXCRSQcL4yIMACoiFAiUuDoI8DevXsZNWoUSUlJ7e5jtVqDBYUNDQ0kJia2++RfW1vL3r17uywe9Hg8rFu3jsOHD9Pa2to3L0QkhAoKCigrKwv1MvrUoL9MkJKSQnp6uooIJSIdOXKEyy67jKamJg4dOsTo0aN5+umnWbBgAfPnz+f1119n+/btfOlLXyI3N5fx48dz7NixdpMht2/fTnl5ObefGvh1ej8Ev9/P1q1beeCBB4B/FjSeOXLabreTn5+vS3UyINntdsrLy2lraxu0zboGfRgAs25AOwMSifLz8/njH//I9u3bOXr0KBs3buTZZ59l06ZNzJ07l5KSEl566SVuvfVWAObOnUtOTg6/+MUvmDRpEl6vl6997WtMmjSJxYsXA+3DgNVqZfHixbz55psdeil8+OGH/O1vf6OpqSl4/6ysrE4nSXZ2+UIkXNjtdnw+H5WVlec8jTNQRUwY0MAiiUQWi4UpU6YwZcqUTm//9re/zdKlS9v9Bffb3/6Wm2++mTFjxpCVlcW4ceN48sknu/xLMDCvITc3t0NzL8MwOHHiRIdeCp988gkrVqzA4/EE75uamtplK+f09HTNF5GQOf14ocLAAKaBRSKdy8zMJDMzs933Ro8ezdq1azl48CBHjx5l9OjRHe7TXRaLhYyMDDIyMjoNJB6PJ9hH4fRdhc2bN1NdXR28X0JCQpetnLOysnS6QfpUbm4uUVFRlJaWMmfOnFAvp09ERBjQwCKRnhs+fHiwALevpKSkMGHCBCZMmNDhtqamJo4cOdLh8sO7775LRUVFsEOjzWYjPz+/01bOQ4cO7bLbokh3RUVFDfrjhRHxp0QDi0QGnvj4eEaNGsWoUaM63Nba2tquQ2Pg8sPatWs5cuQIPp8PMGsahg4d2mVBY2xsbH+/LBmgCgoKFAYGOg0sEhlcYmJiGDZsWKftYf1+P5WVlR1aOW/bto0333yT5ubm4H2zs7M7vfxQUFDQ4QimRDa73c6mTZtCvYw+ExFhQAOLRCJHYDdg6NChzJ49u91thmFw/Pjx4E5CICh8/PHHHQoa09LSOgSEwJcKGiOP3W7n1VdfHbQdOCMiDIAGFomIWdA4ZMgQhgwZwtSpUzvcHihoPLNOYePGjdTU1ATvl5CQ0OXJBxU0Dk52ux2v18uxw4fJSU0FiwXi4syvQSBiwoAGFonIuZytoLGxsbFdQWPg6x//+AcVFRUYhgGYBY2n7yKcXq+Qm5urgsaBxDDgww9hwwYmbdjAs3v2kLpoEdhs5u1WK+Tnw4wZMGECzJsHnYwrHwgi5v/K4cOHa2CRiJy3hIQERo8ezejRozvc5vV6OxQ0lpWVsWbNGsrLy9sVNObl5XUoaCwoKFBBYzhpbIS334YXXoAtW6ClhWTDYGhLC62trcTFx5v38/vhk0/go4/M38fHwyWXwI03wmWX/TM0DAAREwYKCwuDA4vGjBkT6uWIyCBis9koLCyksLCww21+v5+Kiop2xYylpaW8//77vP7667S0tATvGyhoPPPkQ0FBAYmJif35kiLX6tXwne/Avn3m71NTYcgQLBYLdc3NWGJiOu+WaRhQXw9uN6xYARMnwve/D9On9+vyz1fEhIHTjxcqDIhIfwnsBuTl5XVa0FhTU9NhR2Hfvn243W7q6uqC901PT++ylXNaWpoKGi9UXR3853/C734Hzc2QldXhk73NZqPV6+385y0WSE42v1pazMsL118Pt90Gd99t7hqEsYgJAxpYJCLhxmKxBLtATps2rcPtHo+nw8mHsrIyNmzYwPHjx4P3S0xM7LKgMTMzUwWN51JVZb5pv/ceJCZCXp755n4Gm81GU2PjuR8vNtZ8jBMn4OmnYft2+NnPICWlDxbfOyImDIAGFonIwJKSksLEiROZOHFih9saGxs7beW8Y8cOKisrOy1oPLOVc6DNbkSrqYGbb4atW83dgLPUbdhsNmpPnsQAzrkPY7FARgY0NcHy5fDlL8Pzz0OY9q+IuDCggUUiMhgkJCQwZsyYTi97er1eysvLO+worF69miNHjtDW1gaYPVhOL2g8PTTk5+djG0AFcOfF64WvfMUMAtnZ5yz4s8XE4DcMfD4fMd09FRIfD5mZsHIl3Hcf/OIX5imEMBNRYUADi0QkEthsti5nS7S1tbXr0Bi4DLFlyxb++te/BgsaLRbLWQsaExIS+vlV9YHnnoO1a803624En0A4avV6ux8GwOxFkJ4Ob70FL78Mn/vc+a64z0RUGNDAIhGJdIHdgLy8vA4T+AzDoLq6ukMr5z179rB8+XLq6+uD983IyOiylXNqamr4f+DavRt+8hMzBHTzuHnMqTDgbW2lx1EoMdEsUnzqKZg/H06NRQ4XERUGAin54MGDCgMiImewWCxkZWWRlZXF9DOOxBmG0e2CxqSkpC5bOYdNQeN3vwu1tWbToG6yWizEREfj7epEwblkZ0N5OfzXf5lBJIxEVBgIDCw6dOhQhyM+IiLSNYvFQmpqKqmpqVx88cUdbg8UNJ4ZFLZv305VVVWwoDE2NrbLgsacnJz+KWjctQvWrzd7CPRgB8Pr9/NCWxsrKirw1tQwOjaWO7KzmdPdHhBWq1lA+Pe/wze/aZ44CBMRFQY0sEhEpG+cq6Cxs1bOK1eupLy8vF1BY35+fqdhIS8vr/cKGv/0J7PLYA92BQD+/ehR3vZ6uTImhmk5ObxZW8vXDh/mfwsLmdrdGoqUFHN34M9/NvsPhImICgOggUUiIv3NZrMxYsQIRowY0eG2trY2KioqOuwobN68mddeey24JW+xWMjJyem0lXOPChqbm8034ri4Hu0K7Gxq4h2Phy+lpuJsbmZMejqfSU3l+v37+Z+qKn7VSbFmp6xWiImBF1+Eu+7q0Rr6UsSFAQ0sEhEJH4HdgPxOPqX7/X6qq6s7tHLevXs377zzDg0NDcH7BgoaOzv5kJKS8s+Cxr17zVqBzloKn4Wrrg4rcG1aGidO7WbYoqK4Ji2Nnx47RmVrKzkxMd17sMREqKiAI0egoKBH6+grERcGNLBIRGRgsFqtZGdnk52d3WlBY21tbbuCxkBgWLduHSdOnAjeNzk5ORgMFldWstjjwZ+QQIzPR3R09LkbCAEfNTdTaLORHhvLCcxLH9Hx8Uw81WZ4T3Nz98NAXBx4POaJBoWB0NDAIhGRgc9isZCWlkZaWhqTJk3qcHtDQ0OnBY1Vq1ZRV1/PscOHgVMnBGw2bIGvmBhsNhsxNhsxMTHBoFDt85EZHR08Xtjq9UJ8PJmn+g1Un5pM2S2BHgW7d8Oll573v4PeFHFhQAOLREQGv8TERMaOHcvYsWPb33D33Rgvv0xKdjbe1la8Xi9er5dWr5c6j4fW1laMU3e1WCzEnAoHnqYm0mw2GhsbsVqtwVqG2FOXH5oNgx477ThmqEVcGNDAIhGRCNbSgsVqJTY2lthO5hAYhkFra2uHoGBta6O2oYH9n3xCm9/P0Nxc8+FOhYC4nhYCGobZDjlMRFwYAA0sEhGJWDEx5htxFywWS/CSQVt8PPV1dbS2tpJqGBwHEpOSSIiPJ/lUAWLg8kBmT9oTB5zPz/SR8FlJP9LAIhGRCHWORkO+tjbq6uqoq6ujoaEBwzCIj4/n4pQUXmtqYujw4SSe1hhpR1MTAGN7WpBusfT4RENfCoOekP1v+PDhHDp0KNgRS0REIkSgVuy0v/9bfT6OnzjBoUOH2Ld3LxVHj2L4/eTk5DB69GhGDB/ONbm5GBYLfzl5MvhzXr+fN2pruTg+vvsnCQD8fvP5z6xnCKGI3RnQwCIRkQg0fjxER+NtbKSuuRlPXR1NTU1YLBYSExPJHTqU5KQkos/Ywr84Pp4lycn8f1VVnGhroyAmhjdrayn3enmksLBna2huNo8XjhvXiy/swkRkGNDAIhGRyLN//37WbNrEktpa2qqqqI+JISkpify8PJKSk4k6xwClJ/Ly+PmxY7xVW0tdWxujYmP5b7ud6T0d59zYaF6uGDnyAl5N74rIMKCBRSIig59hGOzZswe3243b7ebgwYMkJCQwevRoph84QP7w4T2aoGizWrknJ4d7cnIuZFHmzsCyZSogDDUNLBIRGZz8fj87duzA5XJRUlJCeXk5KSkpLF68mHvuuYc5c+Zge+89WLoUWlrgVAfBftPQYD7n9df37/OeQ0SGAdDAIhGRwaKtrY2tW7fidrspKSmhurqaIUOGUFxcjMPhYPr06e1rAObPN4v3duwwxwj317Agw4CTJ2HxYpg4sX+es5siNgxoYJGIyMDl9XrZuHEjbreblStXUltbS25uLpdddhkOh4PJkyd3fQnAaoVvfhO++EWoqzPHCveHEyfMIUUPPtg/z9cDERsGNLBIRGRgaWpqYv369bjdblavXk1DQwPDhg3jX//1X3E4HIwbN+6f0wnPZckSc6v+d7+DhIS+v37v9UJTE9xzD8yc2bfPdR4iNgxoYJGISPirr69n9erVuN1u1q1bR0tLC2PGjOGmm27C6XQyYsSI7geAMz38MKxfDx9/bF4u6EExYY+0tUFVFUyeDF/7Wt88xwWK2DCggUUiIuHpxIkTrFq1CpfLxcaNG/H5fFx88cXcfvvtFBcXY7fbe+eJMjLgl7+Em26C8vK+CQQ+H1RUwEUXwbPPmrsQYShiw4AGFomIhI+qqipWrFiB2+1m69atAEydOpV7772XoqIici7kON/ZTJgAv/61WT9QVgaZmWZDoN7Q1AQ1NTB6NPzqV9DT5kT9KGLDAGhgkYhIKJWXlwePAG7fvp2oqChmz57Nww8/zOLFi8nIyOifhUyZAn/8o1nYt3Ej2GwwZMj5nzLw++HYMfNXhwN+8APord2MPhLRYWD48OHs2bMn1MsQEYkYBw4cCDYB2rNnDzabjUsuuYQnnniCBQsWkNJflf1nGj0aXn7Z3Mr/n/+BI0fMfgCpqd0vLmxtNY8OtrSYOwwPPmhegjhtsFG4iugwEDheaBjG+RegiIhIlwzDYO/evbhcrnZdABcsWMAtt9zCvHnzSAiX6+g2G9x5J1x6KbzwAvzlL2bhn98PsbHm5YPY2H/WFfj95ht/U5N5WsBigdxc+OxnzaZGYXxZ4EwRHwYaGxuprq4mKysr1MsRERkUAl0AAzsAp3cB/NrXvsbcuXOx2WyhXmbXxoyBxx+H++6D1183Txxs3Wpu/Tc2miEAzFAQG2teApgxAxYsgCuvNHsJDDAWoxtzfD0eD6mpqdTW1oZuC6cPNDc309jY2H/XpUREBqlAF8CSkhJKSko4duwYGRkZFBcX43Q6O3YBHGgMwzwVcPiwuRsA5k7B8OGQldV/XQx7qLvv3wP4v8yFi42NVcMhEZHz5PV62bRpEy6Xq10XwEsvvfTcXQAHGosFhg41vwahiA4DqhMQEemZ5uZm1q1b16EL4LXXXovD4WD8+PH6u3UAiugwICIi51ZfX8+aNWtwu92sXbuWlpYWRo8ezU033YTD4WDkyJEKAAOcwoCIiHRw8uRJVq5cidvtZuPGjbS2tjJx4sTe7wIoYUFh4Bzq6urw+/2kpqaGeikiIn3q2LFjwQLALVu2YBgG06ZN45577qG4uLjvugBKyCkMnMOvf/1rysrK+N73vkdMTEyolyMi0qvKy8uDRwBP7wL40EMPUVRUpNNWESLiw8Czzz7LggULGDNmTLD5UNRp3aKmTp3KL3/5S5YtW8bEiRNDuFIRkd5x4MABSkpKcLlc7boAPv744yxcuHBQHSGX7on4MPDII4/wyCOPMH78+HbfLy0tZc+ePaxYsYJdu3axdetWhQERGZACXQADOwAHDhwIdgG8+eabmT9/fvh0AZSQiPgwsGjRIn7/+99z4MABtm7dyt69e6msrKStrY2kpCSGDx/Opz71KfLz80O9VBGRbuuqC+CiRYu4++67w78LoPSriA8DCxcu5J577iEqKooRI0ZQXFzM6NGjKSgoICsri7S0NGJjY0kcgO0lRSSytLW18f777+N2u9t1ASwqKsLpdDJjxoyB3QVQ+kzE/18xYsQIUlJSePHFF8nMzCQ6Olp/WERkwPB6vWzevDnYBfDkyZPk5OSwZMkSHA4HU6ZMGTxdAKXPRPy73qRJk4iOjsZms6k1sYgMCIEugCUlJaxatSrYBfBf/uVf1AVQzkvEh4HCwkJ+/OMf6/iMiIS1hoYGVq9e3a4L4KhRo1i6dCkOh4OLLrpIAUDOW8SHAYDPfOYzbNmyhUmTJqmiVkTCRm1tLStXrsTlcgW7AE6YMIHbbruN4uJihg0bFuolyiChMAA89dRTvPbaa7z44ovMmDEj2G8AaPfPIiJ97dixY6xYsQK32x3sAjh16lS+9rWvUVxcTG5ubqiXKIOQwgBQVFREWloamZmZHW5TKBCRvlZeXh5sAvThhx9itVqZNWuWugBKv7EYhmGc604ej4fU1FRqa2sjpjOV3++ntLSUqqoqhgwZwsiRI0O9JBEZRA4ePBjsAfDRRx9hs9mYO3cuTqdTXQCl13T3/Vs7A6dpbW3l17/+NW+88QYfffQRbW1tpKamYrPZGD16ND/84Q/Jy8sL9TJFZAAyDIN9+/bhcrmCXQDj4+NZsGABy5YtUxdACSntDJxSW1vLHXfcwcaNG5kzZw6TJk0iNzcXq9VKRUUFL774IkOHDuXPf/4zsbGxoV6uiAwAfr+fnTt3BncAjhw5QnJyMosWLcLpdDJnzhz9fSJ9SjsDPfTyyy/z3nvv8d///d8UFRWRlJTU7vYbbriB6dOn8+GHHzJz5swQrVJEwt3pXQBXrFhBVVVVsAugw+Fg5syZamwmYUf/R57y29/+lqVLl/KZz3ym09tzc3PJzc3lk08+URgQkXZaW1vZtGlTMAAEugA6nU6Ki4uZOnWqugBKWFMYOCU7O5uKiooO329paaGiooLvfOc7REVFMWnSpBCsTkTCTXNzM+vXr8ftdrN69Wrq6+ux2+1cc801OJ1OdQGUAUVh4JRly5bx2GOPsWTJEpYtW0ZmZibHjh1j586dbNq0idraWh5++GEmTJgQ6qWKSIg0NDSwZs0aXC4X69ato7m5mVGjRvFv//Zv6gIoA5oKCE/x+/38/e9/5/nnn2fnzp0cOnQIq9XKsGHDWLRoETfeeCPFxcWhXqaI9LNAF0C32817770X7ALocDhwOBzqAihhrbvv3woDZ6ivr6eyspLs7GySk5NDvRwRCYHq6mpKSkooKSlh8+bNwS6AxcXFOBwOdQGUAUOnCc5TUlJSu5MEfr8fwzCwWq3a/hMZxAJdAN1uN9u3b8dqtTJz5kweeughFi9ezJAhQ0K9RJE+ozBwDqoAFhm8Dh06FGwCdHoXwMcee4xFixYN+p1QkQCFARGJGIEugIEmQPv37yc+Pp758+fzhS98gQULFqgLoEQkhQERGdQCXQADg4BO7wJ45513MnfuXHUBlIinMHCGtrY2LBaLLg+IDGB+v5/3338fl8vVoQtgcXExM2fOJCYmJtTLFAkbCgNnKC0t5U9/+hP33HMPNpst1MsRkW46vQvgypUrOXHiBNnZ2cEjgOoCKNI1hYEzeDweXnrpJa655hrGjBkT6uWIyFk0NzezYcMG3G43q1ator6+noKCAq666iqcTicTJkzQKSCRblAYOMPw4cMBs8pYYUAk/AS6ALrdbtauXUtzczMXXXQRN954Iw6Hg1GjRikAiPSQwsAZUlJSSE9P5+DBg6FeioicEugCWFJSwoYNG4JdAL/0pS+pC6BIL1AY6ERhYSGHDh0K9TJEIlpNTU2wCVCgC+CUKVO4++67KS4uZujQoaFeosigoTDQieHDh/PRRx+FehkiEae8vJwVK1bgcrnYvn07FouFWbNm8c1vfpOioiJ1ARTpIwoDnSgsLOSdd97BMAxdexTppr/97W9s3LiRxYsXs3jx4m5X7ge6AJaUlLB79+5gF8BHH32UxYsXqwugSD9QGOhEYWEhjY2NHDt2jOzs7FAvRySsVVdXc/3117N7926mT5/O008/ze23384DDzxAdnZ2h1BtGAb19fW88MILHboAfv7zn1cXQJEQUBjoROBEwcGDBxUGRM7hpz/9KTU1NWzYsIHCwkKeffZZfv7zn2OxWPjBD37QIQxYLBZiY2N59dVXmTNnjroAioQBhYFO5OfnEx0dzaFDh5g9e3aolyMS1nbv3s3YsWMpLCwE4JZbbuHYsWP813/9Fz/4wQ86vVxgs9l46623iI7WX0Ei4UDtuDoRFRWF3W7XiQKJWCdPnuSnP/0pjz/+OBUVFV3er6Ghgfr6ejIzMzEMA4Do6GiuuuoqmpubeeuttwCCt51OQUAkfCgMdKGwsFC9BiTi+P1+3nzzTS677DIef/xxHn/8cQ4fPtzpfQ3DIDExkYSEBGpqatqFhsLCQubMmcPLL78cfFwRCV8KA11QrwGJRFarlebmZj796U+zadMmUlNTWbt2badv5oHvFRUVsXPnTvbu3Ru8zWazMXPmzOAR3aioqP55ASJyXhQGujB8+HCOHj1Kc3NzqJci0q8uv/xyvv3tb1NYWMhll13Gm2++icfj6XC/QC3ANddcA8Dy5cuDt8XFxVFaWsrYsWP1Z0hkAFAY6EKgGKqrLVKRwSoxMTE4sfPmm29m/fr1HS6ZGYYR/MrPz+fqq6/m5Zdf5i9/+QsAR48eZevWrUyaNIm4uLhOawZEJHwoDHTh9IFFIpEmcBTw8ssvB2D9+vXtLhVYLBasVmvwfg888AAOh4O77rqLSy+9lGnTpjFy5Eg+//nPt3s8EQlPCgNd0MAiiXQ+nw8Ap9PJG2+8QX19ffC25uZm/vCHP/Czn/0MgIyMDJ5++mleeeUV5s6dy69//WvefvttcnNzQ7J2EekZne05CxURymBlGAb79u2jurqauXPndtoLIPBp/tZbb2XZsmWUl5eTkpKCx+MhMTGRP/zhDxw+fJhbb72VuLg44uLimDdvHvPmzevvlyMiF0hh4Cw0sEgGE8Mw2LlzJ263G7fbTVlZGSNHjgwe/ztT4ATAVVddRUtLC48++ihlZWUkJSXx6quv8thjj5GamkpcXFx/vgwR6QMKA2ehgUUy0Pn9fj744INgAKiqqiI9PZ2ioiK+8Y1vMGvWrC5/1ufz8atf/YrnnnuO1tZWPvzwQy699FLuuOMOEhMTz/qzIjKwKAychQYWyUDU2trK5s2bcbvdrFixghMnTpCdnU1xcTEOh4OpU6d269y/YRgcPnyYefPm8dJLLzFixIh+WL2IhILCwFloYJEMFC0tLWzYsAGXy8Xq1aupq6sjPz+fq666iuLiYiZOnNjtkcIBMTExfPe73+2jFYtIOFEYOAsNLJJw1tjYyJo1a3C73axdu5ampiZGjhzJDTfcgMPhYPTo0bq8JSLdojBwFhpYJOHG4/GwcuVKSkpK2LBhA16vl/Hjx3PLLbfgcDiCu1kiIj2hMHAOGlgkoVZTU8OKFStwu91s3rwZv9/P5MmTueuuuygqKiIvLy/USxSRAU5h4BwCJwpE+lNFRUXwBMC2bduwWCzMnDmTBx98kKKiIjIzM0O9RBEZRBQGzuH0gUU6Ty196fDhw8EAsGvXLmJiYpgzZw6PPPIIixYtIi0tLdRLFJFBSmHgHE4fWDRmzJgQr0YGE8Mw+PjjjykpKcHlcvHJJ58Eu/gtXbqUBQsWkJiYGOplikgEUBg4h9MHFikMyIUyDINdu3bhcrkoKSmhtLSUpKQkFi1axFe+8hUuueQS7UCJSL9TGDgHDSySC9VZF8C0tDSKiop48MEHmTVrFjExMaFepohEMIWBbtDAIukpn8/Xrgvg8ePHycrKori4GKfT2e0ugCIi/UFhoBs0sEi6I9AF0O12s2rVKurq6sjLy+PKK6/E4XCcVxdAEZH+oDDQDRpYJF0JdAEsKSlhzZo1wS6A119/PU6nU10ARWRAUBjoBg0sktN5PB5WrVqF2+0OdgEcN26cugCKyIClMNANGlgkNTU1rFy5EpfLxebNm2lra2Py5MnceeedFBcXqwugiAxoCgPdoIFFkSnQBbCkpIQPPvgAi8XCjBkzeOCBBygqKiIrKyvUSxQR6RUKA92ggUWRQ10ARSQSKQx0kwYWDU6GYfDJJ58EA8DHH38c7AL4b//2byxYsICkpKRQL1NEpE8pDHSTBhYNHoZhsHv3blwuF263m9LSUhITE1m0aBG33367ugCKSMRRGOgmDSwa2AJdAEtKSnC73VRWVpKamhrsAjhz5kxsNluolykiEhIKA92kgUUDz9m6ADocDqZNm6YugCIiKAx0mwYWDQxer5cNGzbgcrk6dAEsLi7m4osvVhdAEZEzKAx0kwYWha/GxkbWrl2L2+1m7dq1NDY2MmLECK6//nocDgdjxoxRF0ARkbNQGOgBDSwKHx6Ph9WrV+NyuYJdAMeOHcvNN99McXExI0aMCPUSRUQGDIWBHtDAotA6fvw4K1aswO12s2nTpmAXwDvuuAOHw6EugCIi50lhoAc0sKj/VVZWBnsAnN4F8P7776e4uFhdAEVEeoHCQA9oYFH/OHz4MCUlJbhcLnbt2kV0dLS6AIqI9CGFgR7QwKK+0VkXwNjYWObPn68ugCIi/UBhoAc0sKj3BLoABgLA4cOHSUxMZOHChdx2223MmzdPzZ1ERPqJwkAPBAYW6Xjh+fH7/Wzbti0YAE7vAnj//fcza9YsdQEUEQkBhYEe0vHCnvH5fGzZsgWXyxXsApiZmYnD4aC4uJjp06erC6CISIgpDPSQBhadW6ALoNvtZtWqVXg8HvLy8rjiiitwOBzqAigiEmYUBnpIA4s6F+gCWFJSwpo1a2hsbGT48OFcd911OJ1OdQEUEQljCgM9pIFF/xToAuh2u1m/fn2wC+CyZctwOBzqAigiMkAoDPRQpA8s6qoL4Fe/+lUcDgf5+fmhXqKIiPSQwkAPReLAosrKSkpKSoJdAAGmT5/O/fffT1FRkXouiIgMcAoD5yESThSUlpYGuwDu3LmT6OhoZs+ezbe//W0WLVpEenp6qJcoIiK9RGGgJ1paYO9eLq+rg40boaoKmpogKgpSUmD0aBg/HiZMgKFDYQAVzBmGwf79+3G5XJSUlLBv3z5iY2OZN28en/vc51i4cKG6AIqIDFIKA92xfz+88gq8+CLU1HBFbS0NjY0Yx479s0Le74fAcbn4eJg2DZYuhU9/2vx9GOqsC2BCQgKLFi3iy1/+MpdccgnxYbp2ERHpPQoDZ3P4MHzve7B8OTQ0QGwsJCXhi4ujqqyM1JwcYqLP+Ffo85m7BWvXwrp1kJcHd94JX/iCuYMQYoEugIEagIqKClJTU1m8eDH33Xcfs2fPVhdAEZEIozDQGb/f3AX4/vfNSwEpKZCfH9z2t3m9AHhbWjqGgehoSE42v1pbobISvvMdeOcdM1iMHNnfrybYBdDtdlNSUhLsAlhcXIzD4VAXQBGRCKcwcKbGRrjvPnjjDfPNPy/vn9v/p9hsNiwWCy1eL4mJiV0/VkwM5OSYOwUlJXD11fBf/2VeOuhjgS6AJSUlrFy5Eo/Hw9ChQ7n88stxOp3qAigiIkEKA6drbITbb4d334X0dOjijd6CGQi8LS3de9z4eDNUVFbC3XfDj35kBoNe1tjYyLp163C73cEugIWFhVx33XU4HA7Gjh2rLoAiItKBwkCAzwdf/7oZBIYMOWfRn81mw3vqckG3WK2Qm2sGgvvvh6QkcDgubM2YXQDXrFmDy+UKdgEcM2YMy5Yto7i4mJEhuCwhIiIDi8JAwG9+A2++CRkZ3ar+t9lseDyenj2HxWJeNigvh29+E956C86jYU+gC2BJSQkbN26kra2NSZMm8dWvfpXi4mIKCgp6/JgiIhK5FAYA9u0zt+6joyEhoVs/EhsbS2trK37DwNqTrXeLxdwhKC2FJ56AZ57pVj+Cqqqq4BHAQBfAadOmqQugiIhcMIUBw4BHH4Xjx83r+t0UOH7nbWnp+fTCqChITYXXX4fPfKbLgsKysrJgANixY0ewC+C3vvUtFi9erC6AIiLSKxQGtmwx+wGkp3c4NXA2sYEw4PWe3yjj5GTweOC55+BTnwKLBcMwOHDgAC6XC7fb3a4L4BNPPMHChQtJTk7u+XOJiIichcLASy+ZbYYzM7v9I/tbWvjfY8fY1NJC/eHDJNtsjIyN5fMZGSzqyZt1airGli3s/+tfefvIEVwuV7AL4MKFC9UFUERE+kVkh4GaGrOfQEJCj+YIHG1tpdHvZ0l8PNnR0SSmp+Ouq+O+sjK+lZvLv55j+94Amhob8dTXE3vsGCVf/Sp/Hj+eoqIidQEUEZF+F9lhYMsWc6u+h8V385OSmJ+URPnRozQ3NzMyI4Mb0tO56cABXjh+vNMwYBgGjY2NeOrqqKurw+fzER0dTU58PNfn5nLLu++qC6CIiIREZIeBjz4yfz2zpXA3xZ46XmgAVouFnJgYdjY1BW/3GwYNDQ3UeTzU1dfT1tZGTEwMKSkppKSkEB8fj8Xjgbo6OHnS7G8gIiLSzyI7DOzYYZ4mOE9tMTGcbGvjYGMj65ubWVtfz5LkZDx1dXg8Hurr6/H7/dhsNtLT00lOTiYuLo52FyTi4syTDLt3w4IFF/ySREREeiqyw8C+feb8gPP0C4+Hl1taiNm/HyswOyqKa+vqKKurIy4ujiFDhpCSnIwtNpYuKxJiYszuh6Wl570OERGRCxHZYaC5uUfHCc/0+awsxpw8yfG2NtYDbRYLaVlZFKaldb8A0GIxv7o750BERKSXRfbYugsc2jMiNpbPjh/P54YN41cTJxKTnMyTDQ3EnM9ugwYIiYhIiER2GEhIAL//gh4iKiqKjPR0YqKjcSYns6u5mcM9GWBkGObX+TQuEhER6QWRHQbGjYPW1l57uJZTxYj1PQkYXq9ZNzBiRK+tQ0REpCciOwxMnGj+2sMTBcd9vg7f8xkGb9XWEmuxMCI2tvsP1txsTkkcN65HaxAREektkV1AOGGCWUDo8/XoVMF/VFTQ0NbGtIQEsmNiqPH5+HttLQe9Xu7NziahJ0WJTU0wfjykpJzHCxAREblwkR0Gpk83G/3U1vZoNsFlKSm8dvIkr5w4QW1bG4lRUYyLi+Nr2dk9m01gGGYQufTS81i8iIhI74jsMJCcDJ/9LPz0p+Ybczcr+i9LSeGy3vgkX1cHiYlw3XUX/lgiIiLnKbJrBsAMAwkJ5htzfzIMcy5CURGMHNm/zy0iInIahYFx4+Dyy8035ra2/nvekyfNEHLbbf33nCIiIp1QGAB49FHIz4eqqv55vtZWaGiAm2+GOXP65zlFRES6oDAAkJMDjzwCUVFmMWFf8vuhstI8QXDffX37XCIiIt2gMBBwzTVwyy3mJ/a+qh/w++HoUTN8/OQnkJTUN88jIiLSA5F9muB0Fot5uaC5GX73O/PIX1pa780M8PnMHYHsbHj2WZg8uXceV0RE5AJpZ+B0UVHw1FPwta+Zn+LLyy+8XbFhmJceKirMUwO/+Q3Mnt076xUREekFCgNnslrhoYfM3YGxY81P89XV5if7njAM85JDIFAsWwZvvglTp/bJskVERM6XLhN0ZcECeOMN+PnP4YUXzJMGhmFe54+PN9sXn3kJwe83LzM0NZlfcXHmLsA994DDEZrXISIicg4Wwzj3lB6Px0Nqaiq1tbWkRGIP/bo6eOst+OMfYdcu8w2/tdXcRTi9c6FhQGysOWfg05+Gz30Opk3rvboDERGRHuju+7fCQE/V1sLu3eZXRQW0tJi1BomJMGqU2cRo5EiI1qaLiIiEVnffv/WO1VOpqTB3rvklIiIyCKiAUEREJMIpDIiIiEQ4hQEREZEIpzAgIiIS4RQGREREIpzCgIiISIRTGBAREYlwCgMiIiIRTmFAREQkwikMiIiIRDiFARERkQinMCAiIhLhujWoKDDY0OPx9OliREREpPcE3rfPNaC4W2Ggrq4OALvdfoHLEhERkf5WV1dHampql7dbjHPFBcDv91NeXk5ycjIWi6VXFygiIiJ9wzAM6urqyMvLw2rtujKgW2FAREREBi8VEIqIiEQ4hQEREZEIpzAgIiIS4RQGREREIpzCgIiISIRTGBAREYlwCgMiIiIR7v8HYTLAdg7WZQ8AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -192,10 +192,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:45.185408Z",
-     "iopub.status.busy": "2024-02-09T16:52:45.185011Z",
-     "iopub.status.idle": "2024-02-09T16:52:45.189661Z",
-     "shell.execute_reply": "2024-02-09T16:52:45.188996Z"
+     "iopub.execute_input": "2024-02-14T16:07:38.905138Z",
+     "iopub.status.busy": "2024-02-14T16:07:38.904926Z",
+     "iopub.status.idle": "2024-02-14T16:07:38.909508Z",
+     "shell.execute_reply": "2024-02-14T16:07:38.908832Z"
     }
    },
    "outputs": [
@@ -236,10 +236,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:45.192228Z",
-     "iopub.status.busy": "2024-02-09T16:52:45.191823Z",
-     "iopub.status.idle": "2024-02-09T16:52:45.335844Z",
-     "shell.execute_reply": "2024-02-09T16:52:45.335265Z"
+     "iopub.execute_input": "2024-02-14T16:07:38.911823Z",
+     "iopub.status.busy": "2024-02-14T16:07:38.911619Z",
+     "iopub.status.idle": "2024-02-14T16:07:39.086339Z",
+     "shell.execute_reply": "2024-02-14T16:07:39.085607Z"
     }
    },
    "outputs": [
@@ -269,7 +269,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -317,10 +317,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:45.338530Z",
-     "iopub.status.busy": "2024-02-09T16:52:45.338129Z",
-     "iopub.status.idle": "2024-02-09T16:52:45.448999Z",
-     "shell.execute_reply": "2024-02-09T16:52:45.448300Z"
+     "iopub.execute_input": "2024-02-14T16:07:39.089298Z",
+     "iopub.status.busy": "2024-02-14T16:07:39.088856Z",
+     "iopub.status.idle": "2024-02-14T16:07:39.209231Z",
+     "shell.execute_reply": "2024-02-14T16:07:39.208498Z"
     }
    },
    "outputs": [
@@ -354,10 +354,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:45.451721Z",
-     "iopub.status.busy": "2024-02-09T16:52:45.451316Z",
-     "iopub.status.idle": "2024-02-09T16:52:45.458347Z",
-     "shell.execute_reply": "2024-02-09T16:52:45.457772Z"
+     "iopub.execute_input": "2024-02-14T16:07:39.212215Z",
+     "iopub.status.busy": "2024-02-14T16:07:39.211786Z",
+     "iopub.status.idle": "2024-02-14T16:07:39.219076Z",
+     "shell.execute_reply": "2024-02-14T16:07:39.218506Z"
     }
    },
    "outputs": [
@@ -384,10 +384,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:45.460748Z",
-     "iopub.status.busy": "2024-02-09T16:52:45.460362Z",
-     "iopub.status.idle": "2024-02-09T16:52:45.477369Z",
-     "shell.execute_reply": "2024-02-09T16:52:45.476803Z"
+     "iopub.execute_input": "2024-02-14T16:07:39.221634Z",
+     "iopub.status.busy": "2024-02-14T16:07:39.221233Z",
+     "iopub.status.idle": "2024-02-14T16:07:39.239555Z",
+     "shell.execute_reply": "2024-02-14T16:07:39.238992Z"
     }
    },
    "outputs": [
@@ -429,10 +429,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:45.480035Z",
-     "iopub.status.busy": "2024-02-09T16:52:45.479617Z",
-     "iopub.status.idle": "2024-02-09T16:52:45.652135Z",
-     "shell.execute_reply": "2024-02-09T16:52:45.651486Z"
+     "iopub.execute_input": "2024-02-14T16:07:39.242283Z",
+     "iopub.status.busy": "2024-02-14T16:07:39.241888Z",
+     "iopub.status.idle": "2024-02-14T16:07:39.415253Z",
+     "shell.execute_reply": "2024-02-14T16:07:39.414646Z"
     }
    },
    "outputs": [
@@ -448,7 +448,7 @@
     },
     {
      "data": {
-      "image/png": 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qtXTs2JGOHTsyZswY9u3bR0JCAuvXr+ftt9+mQYMG5cGgbt26apcrhMOQMCCEHa1Zs4YtW7Ywb948GjRoUGWPUzZeKGHg6tzc3Ljtttu47bbbGDduHHv27CEhIYF169bx5ptvEhMTU37kcp06ddQuVwhVSRgQwk52797N0qVLeeKJJ+jdu3eVP15UVBRJSUlV/jg1gV6vp0ePHvTo0YOSkhJ27dpFQkICb731FkuWLKF58+blwSA8PFztcoWodtIzIIQdnDt3jqFDh9K2bVsWLFhQLSNuy5cv5/PPP+fbb7+t8seqqYqKiti5cydGo5Hdu3djMplo3bo1BoOBvn37EhoaekPXtVqtmM1m2U5ZqE4aCIWoJoWFhTz22GNYLBbWrl2Lr69vtTzuN998w+TJk9mxYwfe3t7V8pg1WUFBAdu3b8doNLJnzx5KS0u59dZbMRgMxMbGEhQUVOFrHT16lNdee43U1FRiY2N54okn5M9IqEIaCIWoBoqiMHXqVFJTU1mzZk21BQGw7UIIOO2BRY7Gx8eH/v37079/f3Jzc9m2bVv5hlGvvvoqHTt2xGAw0Lt37+u+KcrOziY8PBwPDw9GjBiBm5sbTz/9NDqdrpqejRCVI9t1CXET3nnnHbZu3cqMGTOoX79+tT52dHQ0QI3ellgt/v7+DBgwgCVLlrBp0ybGjRuHxWLhlVdewWAw8PHHH3OtRdXOnTszdepUevXqRVBQEF26dJEgIByarAwIcYO+//57li1bxtNPP03Pnj2r/fEDAgLw8/OTMFDFgoKCGDhwIAMHDiQ9PZ0tW7bQrl07rFbrVV/gTSYTer2ed955hw4dOlR7UBSismRlQIgbkJiYyMSJE+nevTtPPvmkKjVoNBqioqJkvLAahYaG8tBDD9GoUaNrvtPX6/UAbNq0iXvvvfe6txWOHDnC66+/zsMPP8z7779/zVUHIaqChAEhKqmgoIBRo0YRGhrKjBkzVD0cJyoqSlYGHExpaSkAn376Ke7u7nTt2vWaf0d++OEH7rjjDtatW4e3tzfjx4/nmWeeobCwsLpKFkJuEwhRGVarlSlTpnDx4kXWrl2Lj4+PqvVERUWxf/9+VWsQV7Zy5Up69uxZ3ttxKUVR0Gg0pKWlMXnyZBo1asTGjRvx8fHhxx9/pGfPnjz88MOy1bSoNrIyIEQlrFq1im3btjFjxgyH2M42Ojqa9PR0eRfpIBRFQafToSgKW7ZsueotAqvVCsBHH31EWloaL774YnmwbNy4MS1btuTgwYPVWbpwcbIyIEQF7dixg+XLl/Pss8/So0cPtcsBbCsDIOOFjsBsNvPqq6/i6+uLoigEBATQrVu3v52YaLVay28bfPbZZ7Ro0YLOnTuXfz4vLw+tVkt6ejrw5yqCEFVJVgaEqICzZ88yadIkevXqxfDhw9Uup1xZGJC+AfVptVr8/PyYNWsWo0aNIiMjg08++aS8wbOoqKj86zQaDcnJyVy4cIH27dtftgVyeno6Bw8exGAwqPI8hGuSMCDEdeTn5zNy5EjCwsKYPn26qg2Df1U2XigTBerT6XSMGDGCixcvlofHJUuW8MQTT2CxWFi1ahWDBw/m5MmTgC1g6vX6y4KAxWJh69at6PV6evXqBSCrAqJaOM6/akI4IKvVyuTJk8nIyGDBggUOt6Vs2XihrAw4lqioKKZNm8b58+f54IMPcHNz48yZMxw6dIhbbrkFgObNm5OYmHjZiOKJEydYv349DzzwAPBnb4EQVU16BoS4hpUrV7Jz505ee+21K3aFOwIJA46t7LCjefPmkZ6ejre3N4qi4OXlRd++ffnyyy/p06cPBQUFjB07lpKSEv7zn/8Al68KlJaWlp9S6QjNq6JmkTAgxFVs376dFStW8K9//YuuXbuqXc5VRUdH8+OPP6pdhqiAsmCg0Wjw8PAgPj6ewYMH06hRIxo2bEheXh5vvfUWLVq0KP+6MjqdjrVr17Jx40ZiYmKIi4vDYDBQp04dVZ6LqFnk1EIhruD06dMMGzaMLl268Oqrrzr0fVs5vdD57d27l4sXL9K5c2dq1ap11a8rKSlh9+7dJCQksGPHDoqLi2nevDlxcXHExcVRu3btaqxaOAM5wliIG5Sfn8/QoUNxc3Nj9erVDv8C++uvv/L444/z/vvvy3ihCykqKmLnzp0kJCSwa9cuTCYTrVu3xmAw0Ldv3/JVCOHa5AhjIW6A1Wpl4sSJZGVlsXbtWocPAnD56YUSBlyHl5cXBoMBg8FAQUEB27dvJyEhgddee40FCxbQrl07DAYDsbGxBAcHq12ucHASBoS4xJtvvsmuXbtYtGhR+Qy/o/P395fxQhfn4+ND//796d+/P7m5uWzbtg2j0cjcuXOZO3cuHTt2xGAw0Lt3b1ndFVckYUCIP2zdupW3336bF154gdtvv13tcipMxgvFpfz9/RkwYAADBgwgKyuL7777DqPRyCuvvMKsWbPo0qULBoOBnj174uvrq3a5wkFIGBACOHXqFFOnTqVv374MGzZM7XIqTcKAuJKgoCAGDhzIwIEDSU9PZ8uWLRiNRqZMmYJer+e2227DYDDQvXt3p7glJqqONBAKl5ebm8uwYcPw8PBg1apVTvmP4ptvvsmGDRvYtGmT2qUIJ3DhwgU2b96M0Wjk0KFDeHh40L17dwwGA127dsXDw0PtEoWdSAOhEBVQ1jCYk5NTfp68M4qKiiIjI4PCwkKnfQ6i+oSHh/Poo4/y6KOPkpKSQkJCAkajkTFjxuDt7U2PHj0wGAx06dIFvV6vdrmiGkgYEC5t+fLl7Nmzh8WLFzv15i1yeqG4UREREQwbNoxhw4aRmJiI0WjEaDTy7bff4uvrS69evTAYDHTq1Ak3N3nJqKnkT1a4rC1btrBq1SpefPFFunTponY5N6VsvDAxMVHCgLhh0dHRPPnkkzz55JOcOnWqPBh89dVXBAQEEBsbS1xcHB06dHCoA7vEzZMwIFzSyZMnmTp1KgaDgSFDhqhdzk0LCAjA399fmgiF3TRo0IBnn32WZ555huPHj5cHg88++4zg4GD69OlDXFwcbdu2lWBQA0gYEC4nNzeXkSNHEhkZyaRJkxx6q+HKiIyMlDAg7E6j0RATE0NMTAzPP/88v//+O0ajkYSEBD7++GPCwsLo27cvcXFxtGzZssb8PLkaCQPCpVitVsaPH09eXh7Lli3Dy8tL7ZLsJjo6WjYeElVKo9HQokULWrRowYgRI/j1118xGo1s2rSJ999/n1tuuaX8nISmTZtKMHAiEgaES1m6dCl79+5lyZIlREREqF2OXUVFRbFv3z61yxAuQqvV0qZNG9q0acOoUaM4cOAARqORL774grVr1xIVFVUeDBo1aiTBwMFJGBAuIyEhgTVr1vDSSy/RqVMntcuxOxkvFGrRarV06NCBDh06MHbsWPbt24fRaOTjjz9m1apV1K9fv/zI5Xr16qldrrgC2XRIuIRjx44xfPhwevXqxYwZM2rku5TffvuNxx57TE4vFA7DbDazZ88eEhIS2LZtG4WFhTRu3Lg8GERGRqpd4vWlpcGPP8Lhw/Dbb3D8OBQVgUYD3t7QpAm0bAlNm0LHjhAUpHbFl5EjjIX4Q05ODkOGDMHX15dVq1bh6empdklVIicnhz59+jBnzhz69u2rdjlCXMZkMrFr1y4SEhLYsWMHxcXFNG/evPxWQu3atdUu8U+KAvv2wfr18NVXkJtr+xiAXg9l0xNWK5SU2IKBRmMLAvfeCw8+CK1a2T6mMgkDQgClpaW8+OKLHDlyhHXr1tW4PoG/io2NZfDgwQwfPlztUoS4qqKiIr7//nsSEhL4/vvvMZlMtG7dmri4OPr27UutWrXUK+74cZg4Efbssb3Q+/iAvz/odNf+PosFcnJsqwZeXtC7N0ybBiqffiphQAhg0aJFvPfeeyxdupSOHTuqXU6VGzZsGA0bNmTy5MlqlyJEhRQUFLB9+3YSEhL43//+R2lpKe3atcNgMBAbG0twcHD1FGKxwOrVsHAhZGba3uV7e1f+3b2iQEEBZGdDeDiMGwcPPfTnakI1kzAgXN6mTZuYMGECI0eOZNCgQWqXUy0mTpxIamoqK1euVLsUISotNzeXbdu2kZCQwA8//ABAhw4dyoNBlb3+FBbCf/4DX34Jbm4QGnrzL95WK1y8aPv/jzwCs2aBu/vN11pJEgaESzt27BiPP/44ffr0Ydq0aTWyYfBKVqxYwSeffILRaFS7FCFuSnZ2Nlu3bsVoNHLgwAE0Gg2dO3fGYDDQq1cvfH197fNARUXw7LOwaZNtNcDHxz7XLZOXZ+s5eOAB26pDNZ/vIKcWCpeVnZ3NqFGjqF+/PhMmTHCZIAC2XQgzMzNlvFA4vcDAQAYOHMjAgQPJyMhgy5YtGI1Gpk6diru7O7fddhv9+vWje/fuN/533WqF+HgwGiEkxHav3978/Gy3Gj7+GAICYMYM+z+GHUgYEDVKaWkpL7/8MsXFxbz11lsudy572YFF586do0mTJipXI4R9hISE8OCDD/Lggw9y8eJFNm/ejNFoZMKECXh4eNCtWzcMBgNdu3at3LTQJ5/Axo22F+mq3I3U1xdKS2HtWujRA+Liqu6xbpCEAVGjLF68mP3797Ns2TLHGlWqJhIGRE0XFhbGoEGDGDRoECkpKSQkJGA0Ghk7dixeXl706NEDg8HAbbfdhl6vv/qFkpNt9/HB9mJd1fz94fx5mDIF2reH6mqMrCAJA6LG+Oabb3jvvfcYPXo07du3V7scVfj7++Pv7y9nFAiXEBERwbBhwxg2bBiJiYnlwWDTpk34+vrSq1cvDAYDnTp1wu2v9+pnz4bUVKiucWONBsLC4NQpWLwYpk6tnsetIGkgFDXCkSNHGD58OAaDgSlTprhUn8BfDRs2jAYNGjBlyhS1SxFCFadOnSo/cjkxMRF/f39iY2MxGAy0b98eXVIS9Olje4EOCKjwdQutVtZlZPBbURGHiovJLS1lyi23cHdgYMWLy8iwjSzu3FktqwPSQChcRlZWFvHx8TRq1Ijx48e7dBAA2xkFcpSxcGUNGjTg2Wef5ZlnnuH48ePlweDzzz8nODiYcXo93bOzcYuOpjL/WmRbLLyVnk5td3cae3iwv7Cw8sUFBtpWJDZuhMcfr/z3VxF1dkEQwk4sFgvjxo3DZDIxb968a98jdBFylLEQNhqNhpiYGF544QU2btzI2rVruatfPyK//5703FyOnzxJ6oULFBYVcd0lciDUzY1NjRvzVaNGjAgLu7GidDrbHgYffPDnFscOQFYGhFNbtGgRBw8eZPny5YSHh6tdjkOIioqS8UIh/kKj0dC8eXOaa7Uor79OcUgImpIScnNzyczMxN3dvbznxtPT84orBnqtlhB77CTo62vrHUhLs/UROAAJA8Jpff3113zwwQeMGTOGdu3aqV2Ow4j6Yy90mSgQ4goOH0ZTUoLXLbfgpdUSXrs2hYWF5Obmkp2dTUZGBnp3d/wDAvD398fDw6NStxIqxNMT0tNtJyE6SBiQ2wTCKf3+++/MnDmTAQMG8MADD6hdjkMpGy+UWwVCXMHhw7bGwT/e4WsAH29vbqldm5iYGKKjo/H28SErM5NTp05x6uRJ0tLSKCkpsV8Nbm62WwSHD9vvmjdJVgaE08nMzCQ+Pp7GjRszbtw4l28Y/KuypU5pIhTiClJTbRsAXYEG8PXxwdfHB2vt2hQUFJCbm0tGZiZp6el4eHgQ8MfP103RaGxh4MKFm7uOHUkYEE6lrGHQYrFIw+A1yESBEFdRXFyhQ4i0Gg1+vr74+fpiVRQK8vPJyc0lPT2d86mpKPXq3Xwt9lxtuEkSBoRTWbhwIb/88gtvvvkmYQ5yr80RyUSBEFeh1Va6i1+r0eDn54efnx+pFy6QkZ5OscVy87VU86FF1yI9A8JpfPnll6xfv57Ro0fTpk0btctxaLIyIMRV3MQSf84fkwe1b7kFr5s990SjsW0+5CAkDAincOjQIWbPns0999zDwIED1S7H4ZWNFxYUFKhdihCOpVGjP+/ZV0JxcTHnU1IICAgg+GZ3DrRabb8aNbq569iR46xRCHEVGRkZjB49miZNmjBmzBhpGKyAsomCpKQkGS8U4lLNmtk2/rFYwN29Qt9iKS3lXFISeg8Pdnp6UpCeTtoftwl25udz8Y///1BQEL463fUvWFICHh62WhyEhAHh0MxmM2PHjqW0tJS5c+dKw2AFle01kJiYKGFAiEs1b247rriwsELnEihAcnIyVquVunXrMuLsWc6bzeWf35qXx9a8PADu9PevWBgoKgIfH2jc+Eafhd1JGBAObeHChfz222+sWLGCWrVqqV2O05DxQiGuIiQEunSBzZsrFAYuXrhAYUEB0XXrond358ubXdpXFFsQuececKA3N9IzIBzWxo0b+fjjjxk7diytW7dWuxynI02EQlzFoEG2qYLrjPbl5OSQkZlJeHg4PvZq9isqst0ieOgh+1zPTiQMCIf066+/MmfOHAYOHMi9996rdjlOScYLhbiKPn2gbl3IzLzqlxQVF5Ny/jyBAQEE2fOo4awsaNkSOna03zXtQMKAcDjp6emMGTOGZs2aMXr0aLXLcVqyMiDEVej1MGKEbargChM3FouFpHPn8PTwoPYtt9jvbILc3D8f2x4HHtmRY1UjXJ7ZbGbMmDEAzJ07F/cKdvuKv5PxQiGu4YEHwGCwvVO3Wss/rCgKScnJKIpCZFQUWntNL1kstjDwwAMQF2efa9qRhAHhUObNm8fhw4eZN28eoaGhapfj1MrGC2V1QIgr0Gph+nSoXdt2XsEf+w5cuHCBosJCIiMjcbfXDoFWq+0cgvr1Yfx424qEg5EwIBzGhg0b2LBhA+PGjaNly5Zql+P0Lj3KWAhxBZGRsGgR+PlBairZ2dlkZmURXrs23vZqGLRa4fx5CA2FpUvBnv0HdiRhQDiEX375hblz5/LAAw/wz3/+U+1yagQZLxSiAnr2hNdfp8TDA0tiIkH+/gQFBdnn2mYzpKRAWBi89Rbceqt9rlsFJAwI1aWlpTF69GhatmzJyJEj1S6nRpGJAiGuL6N9e8aHh5MVGEhtqxVNbm6ltyu+jKLYehEuXLBtcrR2rW1vAwcmYUCoymQyMWbMGHQ6nTQMVgGZKBDi2sqaln/z9ydo1y40w4aByWR7R1/ZUGC1Qk4OJCfbvu/55+GLL8AJDlaTHQiFahRFYe7cuRw9epSVK1fe/OEf4m+ioqL44Ycf1C5DCIe1YMECDh06xIoVKwht3BjmzoX+/eHtt2HXLtsLu7s7eHrafun1fzYAKootOBQVQXGxbWLA1xfuvhueego6dVL3yVWChAGhmg0bNvD5558zZcoUmjdvrnY5NVJ0dHT5eKGPj4/a5QjhUD777DM++eQTJk6cePkup7162X4dOwaffALffQdnz0J2tq0P4NIwoNfbQkKrVrbNjO6/H+rVq/4nc5MkDAhVHDx4kHnz5vHggw9y9913q11OjXXpREHTpk1VrkYIx/HLL7/w6quvct9993HPPfdc+YtiYmyjgOPH224ZHDkCiYm2VQCt1ratcP360KSJ7eAhJyZhQFS7ixcvMmbMGFq3bi0Ng1Xs0tMLJQwIYZOWlsaYMWNo0aIF8fHxFfsmf3/bsr8TLf1XhjQQimplMpkYPXo0bm5uzJkzBzd7beohrqhsvDApKUntUoRwCGVNy1qtVpqWLyH/EotqoygKc+bM4fjx49IwWI1kvFAIG0VRePXVVzl69ChvvfUWISEhapfkMGRlQFSbjz/+mC+++IIJEyZIw2A1kjAghM2nn37Kxo0bGTduHC1atFC7HIciYUBUiwMHDrBgwQIeeeQR/vGPf6hdjkuJjIyU2wTC5f3000/MmzePhx56iAEDBqhdjsORMCCqXFFREZMmTaJt27aMGDFC7XJczqXjhUK4oosXLzJ27FjatGnDf/7zH7XLcUjSMyCqnIeHB4sXLyYkJEQaBlVw6emFMlEgXI3JZCI+Ph53d3dpWr4GWRkQVU6r1dKgQQMCAwPVLsUlRUZGAkjfgHA5iqIwa9YsTpw4wfz586Vp+RokDIhqoXHA87tdhb+/PwEBAXJGgXA569ev56uvvmLixIk0a9ZM7XIcmoQBYVdWq1XtEsQVyIFFwtXs37+fBQsW8Oijj9K/f3+1y3F4cvNE3JTjx49z/vx5goKCiImJwcPDA6vVilYrOdORyHihcCXnz59n7NixdOjQgRdffFHtcpyC/Istbti0adN44IEH6NOnD4MHD6Z79+7s3bu3PAgoN3MeuLCrqKgoGS8ULqG4uJj4+Hi8vb2ZNWsWOp1O7ZKcgoQBcUO+/fZb5s6dy8yZM/n555+ZPHkydevW5bbbbmPx4sWArU9AAoFjiIqKkvFCUeMpisLMmTM5c+YM8+fPl6blSpDbBOKG/PTTT9x5553lGwg1b96cHj160LZtW+Lj40lNTWXWrFnSOOggysYLExMTpZFK1Fjvv/8+//d//8esWbOIiYlRuxynIisD4oZYLBZ27dp12cdq1arFiBEjmD9/Pp999hlbtmxRqTrxV2XjhdJEKGqqvXv3smjRIoYOHYrBYFC7HKcjYUDckEcffZRatWoxZcoUMjMzyz/u6+vL4MGD0el0fPnllypWKC4l44WiJktJSWHcuHF06tSJF154Qe1ynJKEAXFDIiMj6d+/P59//jkrV6687EUmODiYO++8k8LCQkpLS1WsUlxKJgpETVRUVMSoUaPw9/dn1qxZMsl0g6RnQFSaoijo9XrmzJmDxWJh6dKlnDp1igEDBtC/f39Onz7NF198wcMPPyydvA5E9hoQNY2iKEyfPp2kpCRWr16Nv7+/2iU5LQkDotI0Gg2lpaXodDrmz59Py5Yteeedd9iyZQtDhw4lMjKSOnXqMG3aNLVLFZeIiorif//7n9plCGE3a9euJSEhgblz59KwYUO1y3FqEgbEDdHpdOWbCz322GP06tWL1NRUfvrpJ1q2bEnnzp3VLlH8RXR0NFlZWeTn5+Pr66t2OULclN27d7NkyRKGDx9ObGys2uU4PQkD4prOnTtHnTp1rngfTqvVlgeCevXqUa9ePbp06aJClaIioqKiANufqYwXCmd27tw5JkyYQNeuXXn22WfVLqdGkE4LcVVTpkzhvvvu4/vvv8dsNv/t84qioNVqOXXqFEeOHFGhQlEZMl4oaoLCwkJGjRpFUFAQM2bMkIZBO5H/iuKKEhISeOONNzh//jwPPfQQH3/8Mfn5+Zd9jUajoaSkhDlz5nD33XdLIHBwMl4onJ2iKEydOpXU1FQWLFiAn5+f2iXVGBIGxN+UlJSwZcsW7rvvPo4fP84999zD0KFDWbBgARcuXLjsaz08PLjrrrt45JFHaNq0qUoVi4qS8ULhzN555x22bt3KjBkzqF+/vtrl1CjSMyD+Rq/Xc9ddd5GTk4OnpyfLli2jTZs2/Otf/+Lo0aNMnTq1fKvPr7/+mtjYWAYMGKBy1aIiZLxQOKudO3eybNkynn76aXr27Kl2OTWOhAHxNxqNhm7dupX/3mq18uyzz9KuXTvuuusujh8/zuLFi9m7dy8zZszgxx9/lO50JyHjhcIZnT17lokTJ9KjRw+efPJJtcupkSQMiOvSaDRYrVY6d+7MyZMnueOOO+jXrx/5+fksX76cevXqqV2iqCAZLxTOpqCggFGjRlGrVi2mT58uDYNVRP6riuvSaDRotVpKS0vx9/fns88+Iz8/n4kTJ/L000+rXZ6ohEvHC4VwdFarlUmTJpGWlsbChQvx8fFRu6QaS8KAqDCdTkdeXh7//Oc/adu2LdOnT1e7JFFJEgaEM1m5ciU7d+5k5syZ5cdwi6ohYUBckaIoV/3cgAED5L6zk/Lz8yMwMFAmCoTD27ZtGytWrOC55567rIdJVA0JA+JvkpOTOXr0KFar9W+f8/Pz4+WXX8bDw0OFyoQ9yESBcHSnTp1i8uTJxMbG8vjjj6tdjkuQBkJxmcLCQv7zn/8A8O6776LX6//2NRqNprrLEnYkYUA4sry8POLj47nllluYOnWq/HtTTSQMiHKX7u61Zs2aKwYB4fyio6PlNo9wSFarlQkTJpCVlcW6devw9vZWuySXIWFAlCvb3WvBggWyu1cNFhUVJeOFwiEtX76cPXv2sGjRovKzNET1kJ4BAcD3338vu3u5iLKJAmkiFI5ky5YtrFq1ihdeeIHbbrtN7XJcjoQBQWJiIhMnTqR79+6yu5cLKAsDSUlJKlcihM2JEyeYOnUqBoOBIUOGqF2OS5Iw4OLKdvcKCQmR40BdhIwXCkeSm5vLqFGjiIyMZNKkSdIwqBLpGXBhVquVKVOmcPHiRdauXSu7e7kQOb1QOAKr1cr48ePJy8tj2bJleHl5qV2Sy5Iw4MJWrVrFtm3bWLhwIXXr1lW7HFGNIiMj5TaBUN2SJUvYu3cvS5YsISIiQu1yXJqsCbuoHTt2sHz5cp555hl69OihdjmimsnKgFCb0Whk7dq1vPTSS3Tq1EntclyehAEXdPbsWSZNmkSvXr144okn1C5HqCA6Oprs7Gzy8vLULkW4oGPHjjFt2jTuvPNOHnnkEbXLEUgYcDn5+fmMHDmSsLAwOQ7UhbVo0YKnn376iltOC1GVsrOziY+Pp379+kycOFEaBh2E9Ay4EKvVyuTJk8nIyGDt2rWyu5cLq1OnDsOHD5cwKKpVaWkpL7/8MkVFRaxYsULOOHEgEgZcSNlxoP/973/lOFCBm5v8+IvqtWjRIvbv38+yZcuoXbu22uWIS8jbAhexfft2OQ5UCKGab775hvfff59Ro0bRvn17tcsRfyFhwAWcPn2aSZMmyXGgQghVHD58mFdeeYW7776bBx98UO1yxBVoFEVRrvdFubm5BAQEkJOTg7+/f3XUJewkPz+foUOH4ubmxurVq6VPQFRYSUkJVqtVNoIRNyUzM5MhQ4YQEhLCypUr5TTUalbR129ZGajBrFYrEydOJCsriwULFkgQEJWyZs0aFi5cSGlpqdqlCCdlsVgYN24cZrOZ+fPnSxBwYBIGarA333yTXbt2MXPmzPLDaYQo8/bbb3Ps2DFKS0uxWCyUlpZy6UJhREQEH374IWfOnFGvSOHUFi5cyM8//8zcuXMJCwtTuxxxDRIGaqitW7fy9ttv8/zzz3P77berXY5wQGPHjmXbtm3odDrc3NzQ6XRoNBoKCws5fPgwiYmJHDp0iIMHD6pdqnBCX3zxBevXr2fMmDG0bdtW7XLEdchsUQ106tQppk6dSt++fRk2bJja5QgHddttt/HRRx+RnJzMTz/9xLFjx0hOTqagoACdTkft2rVp3bo1fn5+apcqnMxvv/3G7Nmzuffeexk4cKDa5YgKkDBQw5QdBxoREcHkyZNldy9xVbfffjsTJkwgNzeXqKgo7r77bho2bEjdunUJDw8nODgYDw8PAgMD1S5VOJGMjAxGjx5Ns2bNGD16tPwb5CQkDNQgZQ2DOTk5ssOguK6oqCjCwsLYuHEjgYGBuLu74+7urnZZwomZzWbGjBmDoii8+uqr0jDoRCQM1CDLly9nz549LF68mMjISLXLEQ6uRYsWaDQafH19JTgKu5g/fz6///47K1asoFatWmqXIypBGghriC1btrBq1SpeeOEFunTponY5wgm0bNmSWbNm4evrq3YpogbYsGEDn376KWPHjqVVq1ZqlyMqScJADXDy5EmmTp2KwWBgyJAhapcjnIS7uzv//Oc/+fnnn7FYLGqXI5zYL7/8wty5c7n//vu555571C5H3AAJA04uNzeXkSNHEhkZyaRJk6RZR1TK6NGjefzxx0lMTASgAhuSCnGZtLQ0Ro8eTcuWLRk1apTa5YgbJD0DTsxqtTJ+/Hjy8vJYtmyZbBsrKq1nz57Uq1ev/O+Ooih/C5RX+pgQACaTidGjR6PT6Zg7d640oDoxCQNObOnSpezdu5clS5YQERGhdjnCCQ0dOvSy32u1tsXC7OxsMjMzCQoKIigoSI3ShINTFIU5c+Zw7NgxVq5cSXBwsNoliZsgYcBJJSQksGbNGl566SU6deqkdjnCyRUUFLBu3TqMRiNHjx7FZDIRFBREWFgY3bt3Z8SIEXh6eqpdpnAgn3zyCV988QVTpkyhefPmapcjbpKEASd07Ngxpk2bxh133MGjjz6qdjnCyZ0/f54XX3yRQ4cO0bZtW+677z7CwsIwm838+uuvLFu2jAsXLrBw4UK1SxUO4qeffmL+/Pk8/PDD3H333WqXI+xAjjB2Mjk5OQwZMgRfX19WrVol79bETVEUhdmzZ/POO++wbNky2rZtS1BQEDqdrvxrtm7dyoABA0hLS5O+FMGFCxcYMmQI9evXZ+nSpbi5yXtKRyZHGNdApaWljB8/noKCAubPny9BQNw0jUbDW2+9xeTJk+nbty+hoaGXBQGANm3a4OnpKacXCkpKSoiPj0ev1zNnzhwJAjWI/Ek6kSVLlrBv3z6WLl0qDYPCbjw8PMjLy/vbx00mE+np6cTHx9OyZUs5sMjFKYrCrFmzOHnyJKtWrZLG0hpGwoCT2LRpE+vWrWPkyJF07NhR7XJEDTJ8+HDeeOMNDh48yD333IO/vz8ZGRkcPnyYHTt2kJiYyLRp02SLaxf30Ucf8fXXXzNjxgyaNm2qdjnCziQMOIFjx44xffp0+vfvzyOPPKJ2OaKG+fe//42npycbNmzgueee4/z581itVsLDw+nevTuvv/46vXv3VrtMoaIff/yRhQsXMnjwYO688061yxFVQBoIHVx2djZDhgwhICCAt99+Gw8PD7VLEjVUSkoKFy9epFatWoSFhckGMgKwTZsMGTKEmJgYXn/99b/1lAjHVtHXb1kZcGClpaW8/PLLFBcX89Zbb0kQEFUqIiLisl4URVHKdx+UHQhdU3FxMfHx8Xh7ezN79mwJAjWYhAEHtnjxYvbv38+yZcuoXbu22uUIFyMhwLUpisIrr7zC2bNnWbVqFQEBAWqXJKqQhAEH9c033/Dee+8RHx9P+/bt1S5HCOFi3nvvPb799ltmz55NTEyM2uWIKib7DDigI0eO8Morr3DXXXfx0EMPqV2OEMLF/PDDDyxevJhhw4YRFxendjmiGkgYcDBZWVnEx8fTqFEjxo8fL8u0QohqlZyczMsvv0znzp15/vnn1S5HVBMJAw7EYrEwbtw4TCYT8+bNQ6/Xq12ScCFms5n09HS1yxAqKioqYtSoUfj7+zNz5szyUyxFzSc9Aw5k0aJFHDx4kGXLlhEeHq52OcLFzJo1i9OnT7N69Wq1SxEqUBSFadOmkZyczOrVq2WM3MVI7HMQX3/9NR988AGjRo3i1ltvVbsc4YKioqI4d+6c2mUIlaxZs4bNmzczbdo0GjZsqHY5oppJGHAAv//+OzNnzmTAgAE88MADapcjXFRkZCQ5OTnk5uaqXYqoZrt372bp0qU88cQTxMbGql2OUIGEAZVlZmYSHx9P48aNGTdunDQMCtVER0cDyOqAi0lMTGTChAl069aNZ555Ru1yhEokDKiorGHQYrFIw6BQXVRUFCBhwJUUFhYSHx9PUFAQM2bMkIZBFyYNhCpauHAhP//8M2+++SZhYWFqlyNcnI+PD8HBwSQmJqpdiqgGVquVKVOmkJqaypo1a/D19VW7JKEiCQMq+fLLL1m/fj0vv/wybdu2VbscIQDb6kBSUpLaZYhq8M477/Ddd9+xcOFC6tevr3Y5QmWyJqSCQ4cOMXv2bO655x4GDhyodjlClIuKipKVARewY8cOli9fztNPP02PHj3ULkc4AAkD1SwjI4PRo0fTpEkTxowZIw2DwqFER0dLGKjhzp49y6RJk+jRowdPPvmk2uUIByFhoBqZzWbGjh1LaWkpc+fOlYZB4XAiIyPJzc2V8cIaqqCggJEjRxIWFsb06dOlYVCUk78J1WjhwoX89ttvzJs3j1q1aqldjhB/I+OFNZeiKLi7uxMTE8OCBQvw8fFRuyThQCQMVJONGzfy8ccfM2bMGFq3bq12OUJcUdl4odwqqHk0Gg1ubm7Mnj27PPQJUUbCQDX49ddfmTNnDgMHDpSGQeHQysYLZWWgZpLbAuJq5G9GFUtPT2fMmDE0a9aM0aNHq12OENclZxQI4XokDFQhs9nMmDFjAJg7dy7u7u4qVyTE9UVHR0sYqAGsVqvaJQgnIpsOVaF58+Zx+PBhVqxYQWhoqNrlCFEhUVFRbN++Xe0yRCWdOHGC1NRUAgMDadSoEZ6enlitVrk1ICpE/pZUkQ0bNrBhwwbGjRtHq1at1C5HiAqLioqS8UInM3PmTO6//35iY2MZNGgQ3bt3Z8+ePeVBQFEUlSsUjk7CQBX45ZdfmDt3Lvfffz///Oc/1S5HiEqRA4ucy5YtW5g5cybTp0/n4MGDTJkyhfr163P77bezaNEiwDZJIIFAXIvcJrCztLQ0Ro8eTcuWLRk1apTa5QhRaWVjZ4mJibRo0ULlasT1HDhwAIPBwIABAwBo3rw5PXr0oF27dowZM4bz588zZ84c2e1UXJOEATsymUyMGTMGnU4nDYPCaXl7e8t4oROxWCz873//Q1GU8hf8WrVq8eKLL+Ln58fSpUvp06cPcXFxKlcqHJncJrATRVGYO3cuR48eZd68eQQHB6tdkhA3TMYLnccjjzxC7dq1mTRpEhkZGeUf9/Hx4dFHH0Wv1/Pll1+qWKFwBhIG7GTDhg18/vnnjBs3TpZWhdOTA4ucR0REBP/4xz/48ssvWbly5WV/bkFBQdxxxx0UFBRgsVhUrFI4OgkDdnDw4EHmzZvHgw8+WH7fTghnJisDzkFRFPR6PbNmzcJgMPDGG28we/ZsvvnmG8B2QuGXX35JeHg4bm5yV1hcnfztuEkXL14sP29g5MiRapcjhF1cOl7o7++vdjniKjQaDaWlpeh0OubNm0fLli1ZtWoVmzdvZtiwYURGRhIaGsqsWbPULlU4OAkDN8FkMjF69Gjc3NyYM2eOJG9RY1w6UdCyZUuVqxHXotPpyjcXGjZsGD179iQlJYWffvqJli1b0rlzZ7VLFE5AXr1ukKIozJkzh+PHj7Ny5UppGBQ1yqV7DUgYcAyJiYlERkZecUdBrVZbHgjq1atHvXr1uP3221WoUjgr6Rm4QR9//DFffPEFEyZMoHnz5mqXI4RdyXihY5k6dSr33nsvO3fuxGw2/+3ziqKg1Wo5ffo0R44cUaFC4ewkDNyAAwcOsGDBAh555BH+8Y9/qF2OEFVCDixyDFu2bOGNN94gLS2Nhx56iI8++oj8/PzLvkaj0VBSUsKrr77K3XffzeHDh1WqVjgrCQOVdOHCBcaNG0fbtm0ZMWKE2uUIUWWioqJkvFBlJSUlJCQkcO+993Ls2DEGDhzIsGHDmD9/PhcuXLjsaz08POjfvz8PP/wwzZo1U6li4aykZ6ASSkpKGD16NHq9XhoGRY0npxeqT6/Xc/fdd5OdnY2npydvvPEGbdq04bnnnuPo0aNMnTqVJk2aAPDNN98QGxsr483ihsirWQUpisLs2bM5ceIEq1atIigoSO2ShKhSMl6oPo1GQ9euXct/b7VaeeaZZ2jbti133XUXx48f5/XXX+fHH39k2rRp7N27F19fXxUrFs5KwkAFrV+/nq+++ooZM2bQtGlTtcsRosrJeKHjKdtXoHPnzpw6dYp+/frRr18/8vPzeeONN2jQoIHaJQonJT0DFbB//34WLFjAoEGDuPPOO9UuR4hqUTZeKH0DjkOj0aDT6SgtLcXPz4/PP/+c/Px8xo8fz7PPPqt2ecKJSRi4jtTUVMaOHUv79u2lYVC4lLLxwqSkJLVLEX+h0+nIy8vjnnvuoXXr1rzyyitqlyScnISBaygpKSE+Ph4vLy9mz56NTqdTuyQhqpUcWKSuoqIiwNYr8FcajYa77rqLPXv2VHdZogaSMHAViqIwc+ZMTp8+zYIFCwgMDFS7JCGqnew1oB6z2czzzz/PzJkz0Wg0f/u8r68vL7/8Mp6enipUJ2oaCQNX8cEHH/DNN98wefJkYmJi1C5HCFVERkZKGFDJvHnzOHz4MAMGDLhiGACu+nEhKkvCwBXs27eP1157jSFDhtCvXz+1yxFCNdHR0eTm5pKTk6N2KS5lw4YNbNiwgXHjxtGqVSu1yxEuQMLAX6SkpDBu3Dg6dOjACy+8oHY5Qqjq0gOLRPX4+eefmTt3Lg888AD//Oc/1S5HuAgJA5coLi4mPj4eHx8faRgUAhkvrG4XL15kzJgxtGrVipEjR6pdjnAhsunQHxRF4ZVXXiExMZF33nmHgIAAtUsSQnXe3t6EhITIeGE1MJlMjB49Gp1Ox6uvvoq7u7vaJQkXUnPCQGEhnDsHxcWgKODhAVFRUMGtOd977z2+/fZbZs+eTePGjau4WCGchxxYVPUURWHOnDkcP36clStXEhwcrHZJwsU4bxgwmWDrVti9Gw4cgBMnbEGgbB5Xo7EFgoYNoW1buO02MBjgCmM4e/fuZfHixQwbNoy4uLjqfR5COLjo6GhOnDihdhk12scff8wXX3zBtGnTaN68udrlCBfkfGHg4kVYvx4++ADOnLG9+Ot0thd5Hx/Q/tEGYbXaAsOvv8LBg7BmDURGwsMPw0MPQZ06wJ8Ng507d+b5559X7WkJ4agiIyPZtm2b2mXUWAcOHGDBggU88sgj/OMf/1C7HOGinCcMKAp89hm88gqcPw9ubhAcbHv3fzVeXlB2799kgtRUmDcP3nkHxo6l6N57GTVqFH5+fsycOROtVvophfirS8cLpZfGvi5cuMDYsWNp166dbHcuVOUcYSAtDSZOhG++sYWC2rVtqwGVoddDWJhtxSAtDWXsWI7MmUOJtzcL3n9fjmgV4iouPb1QZt7tp2y7cw8PD2bPno2bm3P8cyxqJsd/K3zuHAwaBBs32m4D3EgQuJRWC+HhZAPRR4+yqqiIRlfY91sIYRMZGQnIXgP2pCgKs2bN4tSpUyxYsICgoCC1SxIuzrHDwPnzMHSo7b5/eHiFJwOuJ7+ggNTsbKzh4QSmpdke49Qpu1xbiJqmbLxQwoD9fPjhh3z99ddMmjSJJk2aqF2OEA4cBkwmeO45OHzYFgTsNHNrMplITk7Gx9eX0Nq14ZZbbI2ITz0FeXl2eQwhaho5sMh+9u3bx3//+18GDx7MHXfcoXY5QgCOHAaWL4cffoBatewWBEqtVs4lJaHT6ahTpw4asN1yCA+HQ4dg4UK7PI4QNY3sNWAfl253/u9//1vtcoQo55hh4NdfYelS26TAtaYFKkEBzqekYDabiYqMRHfp5IC7u+0WxJo1tn0LhBCXiYqK4ty5cyiKonYpTku2OxeOzDHDwLx5kJMDISF2u2RGRga5eXlERETgcaWAERAARUUwc6ZtYkEIUS4qKoq8vDxyc3PVLsUpKYrCjBkzSExMZMGCBTKiKRyO482yHDsG338P/v62XQQr4FRJCW+mpXG4uJgMiwVPrZYGHh4MCQ6mh58f+fn5XLx4kdDQUPz9/K58EY0GgoLgt99g3z7o1MmOT0oI5ybjhTfn3XffZdOmTbLduXBYjrcy8MkntnMGrvaifQXnzWYKrVbuCgggPjycJ0NDARiZlMT6tDSSk5Px9fWlVq1a176QlxeUlNh2OBRClJPxwhv3ww8/8Prrr/PYY4/JdufCYTnWykBpKXz6qa1PoIKrAgBdfX3p+pexw4eCgnj09GlWnT/PEh+fPxsGr0WjAW9v+PprmDrVbqOMQjg7GS+8McnJybz88st06dKFf/3rX2qXI8RVOdbKwJkzkJlpe0G+SRqNBp+SEvKs1r83DF6Ljw8UFMDRozddgxA1SXR0tEwUVEJhYSGjRo0iICCAV155RbY7Fw7Nsf52Hj5sO3nwCicLVkSR1Uq2xUKSycSKs2f5obiY2wMCrtwweDV6PZjNtlqEEOXKJgrE9SmKwrRp00hJSWHBggWy3blweI51m+DoUdtS/Q2O3Pz3wgU2ZGdjLS3FbDbTy8eHSX80PlVY2e0JWRkQ4jJRUVF89913KIqCphK38VzR6tWr2bJlC3PnzqVBgwZqlyPEdTlWGMjJualvHxQcTA8vL35LSmKfhwce3t5YbmRMUFEgO/umahGipomOjiYvL4+cnBwCAwPVLsdh7d69mzfeeIMnn3yS2NhYtcsRokIc6zaBxWI7VfAG1fPwoJO3N7dbrUzw8iKrsJAXExMrv1GKRmObKhBClIuKigJkouBaEhMTGT9+PN26dePpp59WuxwhKsyxwoCbW6WmCK54CXd36tarh7ubG21MJg7m5LDr9Gkys7KwWCwVu4ii2G3nQyFqChkvvLbCwkLi4+MJCQlhxowZ0jAonIpj3SYIDr7pMKDVaAjw9yfA358QLy/cU1MpBi6kpnIhNRVvHx8C/P3x8/O7+nagGo2tFiFEOW9vb0JDQyUMXIHVamXy5Mmkpqaydu1afGUsWTgZxwoDTZrY3pWXllaqiTDTYiHY7fKnYlEU/i8vD283N26vVw+9oti2U83JIeX8eTSpqfj8EQx8/fz+HD0su6XQtKm9npUQNYYcWHRlq1atYtu2bSxcuJB69eqpXY4QleZYYaBZM9sugMXFtnn/CpqVmkpBaSntvL0Jc3cnw2Lh/3JyOGMy8Z+wMLz/eKEPCgwkKDAQs8VCXm4uubm5JKekoNFo8PP1xd/fH193d7Tu7rZahBCXiYqK4sSJE2qX4VB27NjB8uXLeeaZZ+jRo4fa5QhxQxwrDERH244sPn++UmHA4O/P59nZfJKVRU5pKT46HU09PXkxLIweV9jW2N3NjeDgYIKDgzGbzeTm5pKTm0tScjKBpaXo/Pw4duECnU0m9Hq9PZ+hEE4tKiqKrVu3ynjhH86cOcPEiRPp1asXTzzxhNrlCHHDHCsMaLXw4IO2UwutVtvvK8Dg74/hBjf1cHd3JyQkhJCQEEwlJVgSE9lUqxYzJ0zA19eXXr16YTAY6NSpE25ujvWfS4jqFh0dTX5+vowXAvn5+YwaNYratWszffp0aRgUTs3xXt3uvx+WLYO8PNuxwtVIbzajDwri3g8+oI2fHwkJCWzatImvvvqKgIAA+vTpQ1xcHO3bt5cffOGSysYLExMTXToMWK1WJk2aREZGBmvXrsXbDluoC6EmxwsDdetCnz6wcaPt5MLqetEt22ioWzdo3ZoGGg3PPPMMTz/9NMeOHcNoNGI0GtmwYQMhISH07dsXg8FAq1atJBgIl1E2XpiUlETr1q1VrkY9K1as4Pvvv2fRokXlxzsL4cwcLwwAjB4NO3dCejqEhVXPY2Zm2sLHxImXjTdqNBqaNGlCkyZNeOGFFzh06BBGo5GEhAQ++ugjwsPDiYuLw2Aw0KxZM7mPKmq0svFCV54o2Lp1KytXruT555/n9ttvV7scIezCMcNAo0YQH297YS4qsk0YVKWSEtsEw+jR0LbtVb9Mo9HQsmVLWrZsyUsvvcTBgwdJSEjg66+/5t1336VOnTr069ePuLg4GjVqJMFA1EjR0dEuu9fAqVOnmDp1Kn369OGxxx5Tuxwh7EajVGCv3tzcXAICAsjJyam+07csFhg6FLZuta0OVFVXv8UCqanQoQN88skNnZhYWlrK/v372bRpE9999x25ubnUr1+fuLg4+vXrR926daugcCHUMX36dE6cOMHatWvVLqVa5ebmMmzYMDw8PFi1apX0CQinUNHXb8cNA2Bbuh86FH780TZyaO8tgs1muHDBtqfA++/DLbfY4ZJmfvjhB4xGI9u2baOwsJCYmBgMBgMGg4GIiAg7FC6EelavXs3q1av57rvvXGb1y2q18tJLL/Hbb7+xbt066tSpo3ZJQlRIRV+/HfM2QZngYFi9Gp56Cv73P9s9fX//m96yGLBNK+TkQOvWsGqVXYIA2EYVu3XrRrdu3SgpKWH37t0YjUbeeustlixZQosWLTAYDMTFxRFWXf0QQthRVFSUy40XLlu2jD179rB48WIJAqJGcvw2+NBQWLMGnnjC9k4+JcX2vzfKYrFtalRUZNvT4IMP4I8OaXvz8PCgd+/ezJ49m4SEBGbNmkVoaChLliyhf//+PPXUU6xfv57MzMwqeXwhqsKl44WuICEhgXfeeYd///vfdOnSRe1yhKgSjn2b4K+2bYNJk+DkSdvvAwNtzYUVWSkoKrKNDlqtEBUFU6fCnXfaZ5WhkvLz89m2bRtGo5EffvgBRVHo0KEDBoOB2NhYdf8bC3EdRUVFdO/enenTp9O/f3+1y6lSx48f5/HHH6dnz5688sorLnNbRNQcNaNn4Ery8uDLL+G99+C338Bksn3cw8P2S6u1vcBbrX9OCYDteOQmTeDRR+GeeyAoSLWncKmcnBy2bt2K0Whk//79aDQaunTpgsFgoGfPnnL6mXBId9xxB/fccw/PPvus2qVUmZycHIYMGYKvry+rVq3C8waai4VQW80NA2WsVti3D3bvhl9/hYMHbT0AimL7pdXaegxat7b96tTJtqGQA28QlJGRwZYtW9i0aRM///wzer2e22+/HYPBQPfu3fGq6hFLISro6aefJjQ0lFmzZqldSpUoLS3lxRdf5MiRI6xbt04af4XTqhkNhNei1ULnzrZfYAsH6el/rgR4eNgmEBz4xf+vQkJCePDBB3nwwQe5cOECCQkJGI1Gxo8fj6enJz169MBgMHD77bfLAUpCVZGRkTX69MIlS5awb98+li5dKkFAuATnDQN/pdVW326F1SA8PJzBgwczePBgkpKSyoNBfHw8Pj4+lx2g5O7urna5wsVER0fX2NMLv/32W9atW8fIkSPp2LGj2uUIUS1qThiowSIjI3n88cd5/PHHOX36dPkBSl9//TX+/v7ExsZiMBjo0KGDnJMgqkXZ6YXZ2dkEOUj/jT0cPXq0vDHykUceUbscIaqN8/YMuDhFUTh+/Hj5AUopKSkEBwfTt29f4uLiaNOmjQQDUWWOHTvGoEGDWLVqVY05sCgrK4shQ4YQFBTEypUr8bD3JmdCqKDm9wy4OI1GQ0xMDDExMTz//PP8/vvv5QcorV+/nrCwMPr27Uu/fv1o3rx5jVvKFeoq22vg3LlzNSIMWCwWXn75ZUpKSpg/f74EAeFyJAzUABqNhhYtWtCiRQtGjBjBL7/8gtFo5Ntvv+X9998nIiKifDvkxo0bSzAQN83Ly4tatWrVmAOLFi1axE8//cSyZcsIDw9Xuxwhqp2EgRpGq9XStm1b2rZty6hRo9i/fz8JCQls2LCB1atXU7du3fKTFevXr692ucKJRUVF1YhdCL/++ms++OADxowZw6233qp2OUKoQnoGXITZbGbv3r0YjUa+++47CgsLady4MXFxcRgMBiKraEtmUXPNmDGDY8eOsW7dOrVLuWG///47Tz75JHfccQeTJk2SVTNR49T8TYfEDTOZTOUHKO3YsYPi4mKaN29efoCSLJOKinD20wszMzMZPHgwtWrV4q233pK9O0SNJA2E4qr0ej29evWiV69eFBUVsXPnThISEnjjjTd47bXXaNOmDf369aNPnz6EhISoXa5wUM48Xmg2mxk7diwWi4V58+ZJEBAuT8KAi/Py8ipvLiwoKGD79u1s2rSJBQsWMH/+fNq3b09cXByxsbEuc1ytqJhLJwqcLQz897//5ddff+XNN9+Uo8SFwBmOMBbVxsfHh/79+7No0aLybZA1Gg1z5syhX79+vPjii3z11Vfk5+erXapwAGV9Js42UbBx40bWr1/PmDFjaNOmjdrlCOEQZGVAXFFAQAD33HMP99xzD5mZmWzZsgWj0cjUqVNxd3e/7AAlb2/vG3oMq9WK2WyWmW4nVTZe6EwTBb/++itz5sxh4MCBDBw4UO1yhHAY0kAoKuXixYts3ryZTZs2cejQITw8POjRowdxcXF07dq1Ui/sx48f57XXXuP8+fPExsbyxBNPyMmMTsaZTi9MT09n8ODBREREsHz5cukTEC5BGghFlQgLC2PQoEEMGjSI5OTk8gOUxowZg7e3d/kBSp07d77uAUpZWVmEhobi7u7Oiy++CMBzzz2HTqerjqci7CAqKopjx46pXcZ1mUwmxowZg0ajYe7cuRIEhPgL6RkQN6xOnTo89thjvP/++3z66acMGTKE33//nZdeeol+/fqxY8cOrrXw1KlTJ6ZNm0afPn0ICAigS5cuEgScTHR0NImJidf8c3YE8+fP5/Dhw8ybN4/Q0FC1yxHC4cjKgLCLunXr8tRTT/Hkk09y4sQJEhISaNy48TW/x2QyodfrWbNmDe3bt6dRo0bVVK2wl6ioKAoKChx6vHDDhg1s2LCByZMn07JlS7XLEcIhSRgQdqXRaGjcuPF1gwBQvlT79ddfM3/+/Ov2o5w4cYItW7awe/duBgwYwH333WeXmsWNc/Txwp9//pm5c+fy4IMPMmDAALXLEcJhyW0CoYrS0lLANual0+no1q3bNY9c3r9/P3fccQdvvvkmpaWljBgxgmeeeYaioqLqKllcgSOPF168eJHRo0fTunVrRo4cqXY5Qjg0WRkQqnrzzTfp0aMH9erV+9vnFEVBo9GQnp7OxIkTiYyMZOPGjQQEBLB371769OnDQw89RGxsbPUXLgDHHS80mUyMHj0aNzc35syZg5ub/FMnxLXIyoCodoqilDcKbtmyhXvvvfeKtwisVisAn376Kampqbz00ksEBAQA0LRpU5o1a8bPP/9cfYWLK4qKinKolQFFUZgzZw7Hjx9n/vz5BAcHq12SEA5P4rKoVmazmblz5+Lr6wuAn58f3bt3/9tBN1artfy2wSeffELz5s3p0qVL+edzc3Nxc3PjwoULwJ+rCKL6Odp44ccff8wXX3zBtGnTaN68udrlCOEUZGVAVCutVouvry+vvPIK//nPf8jMzOSzzz4jKSkJoLwHQKvVotFoOH/+POfPn6dDhw7Url27/DoZGRkcPHiQuLg4VZ6H+JMjjRceOHCABQsWMGjQIP7xj3+oXY4QTkPCgKhWOp2OESNGkJaWxpkzZ5gwYQKvvfYajz/+OBaLhdWrVzN06FBOnToFwKlTp9Dr9Zcdq2yxWNi2bRs6nY4+ffoAyKqAisrGC7OyslStIzU1lbFjx9KuXTtGjBihai1COBsJA0I10dHRzJgxgwsXLvDee+/h5ubGiRMn+Pnnn8tXAZo3b05iYuJlO8adPHmSDz/8kPvvvx/4s7dAqKNsvLBsdUcNJSUlxMfH4+npyezZs2XzKiEqScKAcAhlx8guWLCAhIQEvL29URSlfIvjL7/8kqysLJKTkxk/fjyFhYXl42KXrgqUlpZy7tw5CgsLVXkerqgsDKg1UaAoCjNnzuT06dPMnz/fIfc7EMLRSQOhcDhlwUCj0eDh4cGYMWN49NFHady4MfXr1ycrK4sVK1bQqlWr8q8ro9PpmDVrFr/88gvdunWjX79+lT5ASVSOp6cnYWFhqk0UfPDBB3zzzTfMnDmTJk2aqFKDEM5OwoBweJ06deL48ePs3LmT9PR0brvttsuaCf9q0qRJ5Scrlh2g1LNnT+Li4ujSpYscUlMFIiMjVQkDe/fu5bXXXmPIkCH069ev2h9fiJpCjjAWNVpiYiJGoxGj0cipU6fw8/Ojd+/eGAwGOnbsKPeW7eSVV17hyJEjvPvuu9X2mCkpKQwZMoSmTZuyePFi+bMU4goq+votYUC4jJMnT5YHg3PnzhEYGEifPn0wGAy0a9fumtshi2tbs2YNq1atYtu2bdUy2VFcXMzw4cMpKChg3bp18u+SEFdR0ddvuU0gXEbDhg157rnnePbZZzly5AgJCQkYjUY+/fRTQkND6du3LwaDgVatWsmoYiVdOl5Y1Tv+KYrC9OnTSUxMZPXq1RIEhLADCQPC5Wg0Gpo1a0azZs144YUX+O233zAajWzevJkPP/yQ2rVrYzAYMBgMNGnSRIJBBURHRwO28cKqDgPr1q3DaDQyZ84cOfZaCDuRMCBcmlarpXXr1uUn2/30008YjUa++OIL1q5dS1RUVHkwaNiwodrlOqyy0wsTExNp3bp1lT3Onj17WLJkCY8//jh9+/atsscRwtVIz4AQV2CxWNi3bx8JCQls3bqV/Px8GjRoUB4Myt4Jiz/179+fu+66i3/9619Vcv2kpCSGDh1Ky5Ytee2116THQ4gKkAZCIezEZDKxZ88eEhIS2L59O4WFhTRt2hSDwUDfvn2JiIhQu0SH8PTTTxMaGsqsWbPsfu3CwkIef/xxTCYTa9euxc/Pz+6PIURNJA2EQtiJXq+nR48e9OjRg+LiYnbt2oXRaGT58uUsXryYVq1alQeDWrVqqV2uaqKjozly5Ijdr6soCtOmTeP8+fOsXr1agoAQVUDCgBCV4OnpSZ8+fejTpw+FhYXs2LEDo9HIokWLWLhwIe3atcNgMNCnTx+X2xY3Ojoao9Fo9+OkV69ezZYtW5g3bx4NGjSw23WFEH+SMCDEDfL29uaOO+7gjjvuIDc3l23btpGQkMDcuXOZO3cuHTt2pF+/fvTq1cslbq9FRkZSWFho1/HCXbt28cYbb/DUU0/Ru3dvu1xTCPF30jMghJ1lZWXx3XffsWnTJg4cOIBOp+O2224jLi6OXr164e3trXaJVeLEiRM8/PDDvP3227Rp0+amr5eYmMjQoUO59dZbmT9/vjQMCnEDpIFQCAeQlpbGli1bMBqN/PLLL+j1erp164bBYKBbt254enqqXaLdFBcX061bN6ZOncpdd911U9cqKCjgsccew2q1smbNGnx9fe1UpRCuRRoIhXAAtWrV4uGHH+bhhx/m/PnzJCQkkJCQwLhx4/Dy8qJHjx7069evRhygVHZ64c0eZWy1WpkyZQoXL15k7dq1EgSEqAYSBoSoJrfccgtDhw5l6NChJCYmlp+suGnTJnx9fenduzdxcXF06tQJNzfn/NGMioq66dML3377bbZv387ChQupW7eunSoTQlyL3CYQQmWnTp0qP0ApMTGRgICA8gOUbr31Vqe4V55vsXC4sJA5H37IkcJCutxxByVWK+5aLcFubjT19qaZtzfNfXwIdne/6nV27NjByJEjefbZZ3nyySer8RkIUTNJz4AQTkZRFI4dO8amTZtISEjg/PnzhISEXHaAkiMFA0VR+Ck/n48vXmRjRgZ5paXkFRRQWFRESHAwGo0GRVFQAA2g02jw0umIDQzkobAwegYGortkBPHMmTMMHTqUzp078+qrrzrUcxXCWUkYEMKJKYrCb7/9Vt5jkJaWRnh4OHFxcRgMBpo1a6bqAUo/5eUx4+xZDuTlUWy14q3V4qPTUVJQQHJSEo1jYnDT6S57PmZFodBqpaC0FDeNhkbe3oyLjsYQFERBQQFDhw7Fzc2N1atX19iJCyGqm4QBIWoIq9XKwYMHMRqNbNmyhaysLCIjIy87QKm6gkFxaSlLU1JYnpxMXmkpQW5ueGu15Y9fUlLCqVOnqFevHl5eXle/jtVKpsWCu0bDwNBQCles4NiPP5YfDiWEsA8JA0LUQKWlpfz4448YjUa2bt1KXl4e9evXLw8GVdlwl1pSwlNHj/Jjfj6eGg3Bbm5/CyGKonDkyBEiIiIICAi47jVzLRZS8/KwJiezrFEjhnTrVlXlC+GSJAwIUcOZzebyA5S2bdtGYWEhMTEx5cHAngcopZSUMOTwYQ4VFFDL3R2Pa9zPP37iBIEBARU6pyEvL49zycm4165Ng8BAVjVpQjs5e0AIu5EwIIQLKSkpKT9AaefOnZSUlNCiRQv69etH3759CQsLu+FrZ5nNDDp8mJ/y8gh3d8f9Oo19Z8+exc3NjTp16ly35jNnzuDr68stERGcN5uJ0Ov5oHlzYqRnQAi7kDAghIsqLCxk586dGI1Gdu/ejcVioW3btuUHKFXm3ABFURh98iTvXrhAmLs7+gp0+J9PTaW4qIj69etf9WtKrVZOnz6NVqOhXv36aDUarIpCislEF39/1rdoUaHHEkJcm4QBIQR5eXls374do9HInj17AOjQoQMGg4HY2Njr/jwbMzN56uhRPDQa/Cu4EVJGRgbp6ek0adLkql+TeO5ceWBwv2TfgbLGwgl16/L8dVYWhBDXJ2FACHGZ7Oxstm7dSkJCAj/++CNarZYuXbrQr18/evbsiY+Pz2Vfn2exYPjlF84WFxNRia2S8/LySLrCeGGZi2lpZGRkEB0V9bfHBEg3m/HQavmyVSu5XSDETZIwIIS4qvT09PIDlH7++Wf0ej1du3YlLi6O7t274+XlxfsXLhB/8iS13N1xr8To4rXGC3Nzc0lOTiY8PPyqtysURSHZZOLZiAimXuNWgxDi+iQMCCEqJDU1lc2bN2M0Gvn999/x9PSke48efGcwcNbdnToeHhW+VtGpU2Tv3MmFvXtxLyhAHxiIV8OGhD3wANagIM6cOYOfnx91rjPpkG424+fmxvft2hHgpOc0COEIJAwIISotKSkJo9HIhwcPsr13b3RmM4GenvgHBODj48P11geSFi+m6PhxSho0wK9BA3yAzIQErMXFaB5/HH1EBPXq1bvuJkkWReGi2cy8hg15NDzcbs9PCFcjYUAIccOWJCUx/dQpPAsLycvJwWQyodPp8PP3x9/fH5+r3MsvPH4cr/r1SUxOLh8vNKWmcjg+Hm2zZjQdM+ayhsFrSTaZuD80lNdjYuz51IRwKRV9/Zb1NyHE3/xWUIBWpyMsNJSw0FCKS0rIzc0lNyeH7KwsdG5u+P8RDLwv6QvwbtwYAL2HB8VFRQBkabUoISF4FhRUOAgA6DUaDuTnoyiKqucwCOEKJAwIIS5TdhrhpbsMenp44FmrFmG1alFUXExuTg65eXlkZWbi5u6Ov78/Af7+eHp6AqB3dyc3J4ecnBwy0tPRm814BAZWqg5PjYYLJhNpZjNhlZhmEEJUnoQBIcRl8ktLybRY8LjKu3EvT0+8PD0JDw+nsKiI3NxccnJyyMzIwF2vx9/fH51Wi8ViIeX8eTxPnKAkPx//zp0rVYdeqyW7tJRzJSUSBoSoYhIGhBCXKbZaURQFXQWW5r29vPD28qJ2eDgFhYXltxHMFgsmkwmf4mLMX32FV6NGBHTvXqk6tNhWKUqs1ht8JkKIipIwIIS4TFkEuG5n8V/4eHvj4+1N7dq1KSgooDQ3l4vz56Px9qbOv/+N5ga3F5ZuASGqnmz+LYS4jJdOh+aPswJuhEajwUurJeONN1CKioiOj8c9KKjS17ECWo0GTzmjQIgqJz9lQojL+Oh01Nbrb3h53mo2k7RwIabUVCJHjsQjMvKGrlNiteKh1VL/j6ZEIUTVkTAghPibW319Md3AyoBitZK8ZAmFJ04Q+e9/l48a3ogiq5UoDw8CKzGOKIS4MdIzIIT4mxZ/7DZY2Rn/i++/T/5PP+Hbrh2l+fnk7Np12ecDunat8LXMikJ7X98Kf70Q4sZJGBBC/E23gAA8dTryrVb8rnDy4NUUnz0LQP5PP5H/009/+3xFw4DJakWr0dA1IKDCjy2EuHESBoQQf9Pcx4cufn5sy8mpVBioO2GCXR4/22Khjl7PHVc52VAIYV/SMyCEuKJB4eFooNrn/K2KgklReCQ8HM9KBBEhxI2TMCCEuCJDcDAtfXxIN5upwHlmdnPRbKa2Xs/DYWHV9phCuDoJA0KIK/LQapndoAE+Oh1ZFku1PGZBaSkAL9etS4SHR7U8phBCwoAQ4hra+/nxdEQERYpCURXfLjBbrWRZLBiCg3mgVq0qfSwhxOUkDAghrunFOnWICwoiw2ymuIoCgUVRuGA208LHh9kNGqCVI4uFqFYSBoQQ1+Sp07G0cWN6BwaSbjaXL+XbS4nVSqrJRFNvb1Y1bUq4nFAoRLWTMCCEuC4/NzdWNm3KPaGh5JWWkmoy3fDZBWUURSHdbCbdYqGTvz/vN29OXdl6WAhVSBgQQlSIj07HGzEx/LdRI2rp9aSYTGRZLJUOBYqikGuxkGwy4aHV8nJ0NJ+0aCENg0KoSDYdEkJUmFaj4YGwMLoGBLDw3Dm+zMjgvNmMDvDRavHUanHTaP62hbFFUSi2Wim0WjFZrfjodNwZEsKoyEhaypbDQqhOo1RggDg3N5eAgABycnLw9/evjrqEEE7gosnEhrQ0Prp4kXMlJRRZrZT9g6IAl0YCT62WEHd37gsN5YGwMBp6ealQsRCupaKv37IyIIS4YWF6Pc/WqcMzERGkmc0cLizkcEEBmRYLJqsVd60WP52Opt7eNPX2JtLDQyYFhHBAEgaEEDdNo9EQptcTptfTMzBQ7XKEEJUkDYRCCCGEi5MwIIQQQrg4CQNCCCGEi5MwIIQQQrg4CQNCCCGEi5MwIIQQQrg4CQNCCCGEi5MwIIQQQrg4CQNCCCGEi5MwIIQQQrg4CQNCCCGEi5MwIIQQQri4Ch1UVHbKcW5ubpUWI4QQQgj7KXvdLnsdv5oKhYG8vDwAoqKibrIsIYQQQlS3vLw8AgICrvp5jXK9uABYrVZSUlLw8/NDI2eRCyGEEE5BURTy8vKIiIhAq716Z0CFwoAQQgghai5pIBRCCCFcnIQBIYQQwsVJGBBCCCFcnIQBIYQQwsVJGBBCCCFcnIQBIYQQwsVJGBBCCCFc3P8Dt0KI6xKCrg4AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -486,10 +486,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:45.654678Z",
-     "iopub.status.busy": "2024-02-09T16:52:45.654282Z",
-     "iopub.status.idle": "2024-02-09T16:52:45.657371Z",
-     "shell.execute_reply": "2024-02-09T16:52:45.656794Z"
+     "iopub.execute_input": "2024-02-14T16:07:39.418166Z",
+     "iopub.status.busy": "2024-02-14T16:07:39.417696Z",
+     "iopub.status.idle": "2024-02-14T16:07:39.420835Z",
+     "shell.execute_reply": "2024-02-14T16:07:39.420227Z"
     }
    },
    "outputs": [],
@@ -503,10 +503,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:45.659923Z",
-     "iopub.status.busy": "2024-02-09T16:52:45.659475Z",
-     "iopub.status.idle": "2024-02-09T16:52:48.853614Z",
-     "shell.execute_reply": "2024-02-09T16:52:48.852909Z"
+     "iopub.execute_input": "2024-02-14T16:07:39.423452Z",
+     "iopub.status.busy": "2024-02-14T16:07:39.423073Z",
+     "iopub.status.idle": "2024-02-14T16:07:42.613305Z",
+     "shell.execute_reply": "2024-02-14T16:07:42.612611Z"
     }
    },
    "outputs": [
@@ -515,7 +515,7 @@
      "output_type": "stream",
      "text": [
       "energy: -1.4996861455587294\n",
-      "time: 3.03641676902771\n",
+      "time: 3.004215717315674\n",
       "max-cut objective: -3.999686145558729\n",
       "solution: [0 1 0 1]\n",
       "solution objective: 4.0\n"
@@ -523,7 +523,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -559,10 +559,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:48.856200Z",
-     "iopub.status.busy": "2024-02-09T16:52:48.855797Z",
-     "iopub.status.idle": "2024-02-09T16:52:52.144777Z",
-     "shell.execute_reply": "2024-02-09T16:52:52.144121Z"
+     "iopub.execute_input": "2024-02-14T16:07:42.616100Z",
+     "iopub.status.busy": "2024-02-14T16:07:42.615696Z",
+     "iopub.status.idle": "2024-02-14T16:07:45.836270Z",
+     "shell.execute_reply": "2024-02-14T16:07:45.835555Z"
     }
    },
    "outputs": [
@@ -577,7 +577,7 @@
     },
     {
      "data": {
-      "image/png": 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qtXTs2JGOHTsyZswY9u3bR0JCAuvXr+ftt9+mQYMG5cGgbt26apcrhMOQMCCEHa1Zs4YtW7Ywb948GjRoUGWPUzZeKGHg6tzc3Ljtttu47bbbGDduHHv27CEhIYF169bx5ptvEhMTU37kcp06ddQuVwhVSRgQwk52797N0qVLeeKJJ+jdu3eVP15UVBRJSUlV/jg1gV6vp0ePHvTo0YOSkhJ27dpFQkICb731FkuWLKF58+blwSA8PFztcoWodtIzIIQdnDt3jqFDh9K2bVsWLFhQLSNuy5cv5/PPP+fbb7+t8seqqYqKiti5cydGo5Hdu3djMplo3bo1BoOBvn37EhoaekPXtVqtmM1m2U5ZqE4aCIWoJoWFhTz22GNYLBbWrl2Lr69vtTzuN998w+TJk9mxYwfe3t7V8pg1WUFBAdu3b8doNLJnzx5KS0u59dZbMRgMxMbGEhQUVOFrHT16lNdee43U1FRiY2N54okn5M9IqEIaCIWoBoqiMHXqVFJTU1mzZk21BQGw7UIIOO2BRY7Gx8eH/v37079/f3Jzc9m2bVv5hlGvvvoqHTt2xGAw0Lt37+u+KcrOziY8PBwPDw9GjBiBm5sbTz/9NDqdrpqejRCVI9t1CXET3nnnHbZu3cqMGTOoX79+tT52dHQ0QI3ellgt/v7+DBgwgCVLlrBp0ybGjRuHxWLhlVdewWAw8PHHH3OtRdXOnTszdepUevXqRVBQEF26dJEgIByarAwIcYO+//57li1bxtNPP03Pnj2r/fEDAgLw8/OTMFDFgoKCGDhwIAMHDiQ9PZ0tW7bQrl07rFbrVV/gTSYTer2ed955hw4dOlR7UBSismRlQIgbkJiYyMSJE+nevTtPPvmkKjVoNBqioqJkvLAahYaG8tBDD9GoUaNrvtPX6/UAbNq0iXvvvfe6txWOHDnC66+/zsMPP8z7779/zVUHIaqChAEhKqmgoIBRo0YRGhrKjBkzVD0cJyoqSlYGHExpaSkAn376Ke7u7nTt2vWaf0d++OEH7rjjDtatW4e3tzfjx4/nmWeeobCwsLpKFkJuEwhRGVarlSlTpnDx4kXWrl2Lj4+PqvVERUWxf/9+VWsQV7Zy5Up69uxZ3ttxKUVR0Gg0pKWlMXnyZBo1asTGjRvx8fHhxx9/pGfPnjz88MOy1bSoNrIyIEQlrFq1im3btjFjxgyH2M42Ojqa9PR0eRfpIBRFQafToSgKW7ZsueotAqvVCsBHH31EWloaL774YnmwbNy4MS1btuTgwYPVWbpwcbIyIEQF7dixg+XLl/Pss8/So0cPtcsBbCsDIOOFjsBsNvPqq6/i6+uLoigEBATQrVu3v52YaLVay28bfPbZZ7Ro0YLOnTuXfz4vLw+tVkt6ejrw5yqCEFVJVgaEqICzZ88yadIkevXqxfDhw9Uup1xZGJC+AfVptVr8/PyYNWsWo0aNIiMjg08++aS8wbOoqKj86zQaDcnJyVy4cIH27dtftgVyeno6Bw8exGAwqPI8hGuSMCDEdeTn5zNy5EjCwsKYPn26qg2Df1U2XigTBerT6XSMGDGCixcvlofHJUuW8MQTT2CxWFi1ahWDBw/m5MmTgC1g6vX6y4KAxWJh69at6PV6evXqBSCrAqJaOM6/akI4IKvVyuTJk8nIyGDBggUOt6Vs2XihrAw4lqioKKZNm8b58+f54IMPcHNz48yZMxw6dIhbbrkFgObNm5OYmHjZiOKJEydYv349DzzwAPBnb4EQVU16BoS4hpUrV7Jz505ee+21K3aFOwIJA46t7LCjefPmkZ6ejre3N4qi4OXlRd++ffnyyy/p06cPBQUFjB07lpKSEv7zn/8Al68KlJaWlp9S6QjNq6JmkTAgxFVs376dFStW8K9//YuuXbuqXc5VRUdH8+OPP6pdhqiAsmCg0Wjw8PAgPj6ewYMH06hRIxo2bEheXh5vvfUWLVq0KP+6MjqdjrVr17Jx40ZiYmKIi4vDYDBQp04dVZ6LqFnk1EIhruD06dMMGzaMLl268Oqrrzr0fVs5vdD57d27l4sXL9K5c2dq1ap11a8rKSlh9+7dJCQksGPHDoqLi2nevDlxcXHExcVRu3btaqxaOAM5wliIG5Sfn8/QoUNxc3Nj9erVDv8C++uvv/L444/z/vvvy3ihCykqKmLnzp0kJCSwa9cuTCYTrVu3xmAw0Ldv3/JVCOHa5AhjIW6A1Wpl4sSJZGVlsXbtWocPAnD56YUSBlyHl5cXBoMBg8FAQUEB27dvJyEhgddee40FCxbQrl07DAYDsbGxBAcHq12ucHASBoS4xJtvvsmuXbtYtGhR+Qy/o/P395fxQhfn4+ND//796d+/P7m5uWzbtg2j0cjcuXOZO3cuHTt2xGAw0Lt3b1ndFVckYUCIP2zdupW3336bF154gdtvv13tcipMxgvFpfz9/RkwYAADBgwgKyuL7777DqPRyCuvvMKsWbPo0qULBoOBnj174uvrq3a5wkFIGBACOHXqFFOnTqVv374MGzZM7XIqTcKAuJKgoCAGDhzIwIEDSU9PZ8uWLRiNRqZMmYJer+e2227DYDDQvXt3p7glJqqONBAKl5ebm8uwYcPw8PBg1apVTvmP4ptvvsmGDRvYtGmT2qUIJ3DhwgU2b96M0Wjk0KFDeHh40L17dwwGA127dsXDw0PtEoWdSAOhEBVQ1jCYk5NTfp68M4qKiiIjI4PCwkKnfQ6i+oSHh/Poo4/y6KOPkpKSQkJCAkajkTFjxuDt7U2PHj0wGAx06dIFvV6vdrmiGkgYEC5t+fLl7Nmzh8WLFzv15i1yeqG4UREREQwbNoxhw4aRmJiI0WjEaDTy7bff4uvrS69evTAYDHTq1Ak3N3nJqKnkT1a4rC1btrBq1SpefPFFunTponY5N6VsvDAxMVHCgLhh0dHRPPnkkzz55JOcOnWqPBh89dVXBAQEEBsbS1xcHB06dHCoA7vEzZMwIFzSyZMnmTp1KgaDgSFDhqhdzk0LCAjA399fmgiF3TRo0IBnn32WZ555huPHj5cHg88++4zg4GD69OlDXFwcbdu2lWBQA0gYEC4nNzeXkSNHEhkZyaRJkxx6q+HKiIyMlDAg7E6j0RATE0NMTAzPP/88v//+O0ajkYSEBD7++GPCwsLo27cvcXFxtGzZssb8PLkaCQPCpVitVsaPH09eXh7Lli3Dy8tL7ZLsJjo6WjYeElVKo9HQokULWrRowYgRI/j1118xGo1s2rSJ999/n1tuuaX8nISmTZtKMHAiEgaES1m6dCl79+5lyZIlREREqF2OXUVFRbFv3z61yxAuQqvV0qZNG9q0acOoUaM4cOAARqORL774grVr1xIVFVUeDBo1aiTBwMFJGBAuIyEhgTVr1vDSSy/RqVMntcuxOxkvFGrRarV06NCBDh06MHbsWPbt24fRaOTjjz9m1apV1K9fv/zI5Xr16qldrrgC2XRIuIRjx44xfPhwevXqxYwZM2rku5TffvuNxx57TE4vFA7DbDazZ88eEhIS2LZtG4WFhTRu3Lg8GERGRqpd4vWlpcGPP8Lhw/Dbb3D8OBQVgUYD3t7QpAm0bAlNm0LHjhAUpHbFl5EjjIX4Q05ODkOGDMHX15dVq1bh6empdklVIicnhz59+jBnzhz69u2rdjlCXMZkMrFr1y4SEhLYsWMHxcXFNG/evPxWQu3atdUu8U+KAvv2wfr18NVXkJtr+xiAXg9l0xNWK5SU2IKBRmMLAvfeCw8+CK1a2T6mMgkDQgClpaW8+OKLHDlyhHXr1tW4PoG/io2NZfDgwQwfPlztUoS4qqKiIr7//nsSEhL4/vvvMZlMtG7dmri4OPr27UutWrXUK+74cZg4Efbssb3Q+/iAvz/odNf+PosFcnJsqwZeXtC7N0ybBiqffiphQAhg0aJFvPfeeyxdupSOHTuqXU6VGzZsGA0bNmTy5MlqlyJEhRQUFLB9+3YSEhL43//+R2lpKe3atcNgMBAbG0twcHD1FGKxwOrVsHAhZGba3uV7e1f+3b2iQEEBZGdDeDiMGwcPPfTnakI1kzAgXN6mTZuYMGECI0eOZNCgQWqXUy0mTpxIamoqK1euVLsUISotNzeXbdu2kZCQwA8//ABAhw4dyoNBlb3+FBbCf/4DX34Jbm4QGnrzL95WK1y8aPv/jzwCs2aBu/vN11pJEgaESzt27BiPP/44ffr0Ydq0aTWyYfBKVqxYwSeffILRaFS7FCFuSnZ2Nlu3bsVoNHLgwAE0Gg2dO3fGYDDQq1cvfH197fNARUXw7LOwaZNtNcDHxz7XLZOXZ+s5eOAB26pDNZ/vIKcWCpeVnZ3NqFGjqF+/PhMmTHCZIAC2XQgzMzNlvFA4vcDAQAYOHMjAgQPJyMhgy5YtGI1Gpk6diru7O7fddhv9+vWje/fuN/533WqF+HgwGiEkxHav3978/Gy3Gj7+GAICYMYM+z+GHUgYEDVKaWkpL7/8MsXFxbz11lsudy572YFF586do0mTJipXI4R9hISE8OCDD/Lggw9y8eJFNm/ejNFoZMKECXh4eNCtWzcMBgNdu3at3LTQJ5/Axo22F+mq3I3U1xdKS2HtWujRA+Liqu6xbpCEAVGjLF68mP3797Ns2TLHGlWqJhIGRE0XFhbGoEGDGDRoECkpKSQkJGA0Ghk7dixeXl706NEDg8HAbbfdhl6vv/qFkpNt9/HB9mJd1fz94fx5mDIF2reH6mqMrCAJA6LG+Oabb3jvvfcYPXo07du3V7scVfj7++Pv7y9nFAiXEBERwbBhwxg2bBiJiYnlwWDTpk34+vrSq1cvDAYDnTp1wu2v9+pnz4bUVKiucWONBsLC4NQpWLwYpk6tnsetIGkgFDXCkSNHGD58OAaDgSlTprhUn8BfDRs2jAYNGjBlyhS1SxFCFadOnSo/cjkxMRF/f39iY2MxGAy0b98eXVIS9Olje4EOCKjwdQutVtZlZPBbURGHiovJLS1lyi23cHdgYMWLy8iwjSzu3FktqwPSQChcRlZWFvHx8TRq1Ijx48e7dBAA2xkFcpSxcGUNGjTg2Wef5ZlnnuH48ePlweDzzz8nODiYcXo93bOzcYuOpjL/WmRbLLyVnk5td3cae3iwv7Cw8sUFBtpWJDZuhMcfr/z3VxF1dkEQwk4sFgvjxo3DZDIxb968a98jdBFylLEQNhqNhpiYGF544QU2btzI2rVruatfPyK//5703FyOnzxJ6oULFBYVcd0lciDUzY1NjRvzVaNGjAgLu7GidDrbHgYffPDnFscOQFYGhFNbtGgRBw8eZPny5YSHh6tdjkOIioqS8UIh/kKj0dC8eXOaa7Uor79OcUgImpIScnNzyczMxN3dvbznxtPT84orBnqtlhB77CTo62vrHUhLs/UROAAJA8Jpff3113zwwQeMGTOGdu3aqV2Ow4j6Yy90mSgQ4goOH0ZTUoLXLbfgpdUSXrs2hYWF5Obmkp2dTUZGBnp3d/wDAvD398fDw6NStxIqxNMT0tNtJyE6SBiQ2wTCKf3+++/MnDmTAQMG8MADD6hdjkMpGy+UWwVCXMHhw7bGwT/e4WsAH29vbqldm5iYGKKjo/H28SErM5NTp05x6uRJ0tLSKCkpsV8Nbm62WwSHD9vvmjdJVgaE08nMzCQ+Pp7GjRszbtw4l28Y/KuypU5pIhTiClJTbRsAXYEG8PXxwdfHB2vt2hQUFJCbm0tGZiZp6el4eHgQ8MfP103RaGxh4MKFm7uOHUkYEE6lrGHQYrFIw+A1yESBEFdRXFyhQ4i0Gg1+vr74+fpiVRQK8vPJyc0lPT2d86mpKPXq3Xwt9lxtuEkSBoRTWbhwIb/88gtvvvkmYQ5yr80RyUSBEFeh1Va6i1+r0eDn54efnx+pFy6QkZ5OscVy87VU86FF1yI9A8JpfPnll6xfv57Ro0fTpk0btctxaLIyIMRV3MQSf84fkwe1b7kFr5s990SjsW0+5CAkDAincOjQIWbPns0999zDwIED1S7H4ZWNFxYUFKhdihCOpVGjP+/ZV0JxcTHnU1IICAgg+GZ3DrRabb8aNbq569iR46xRCHEVGRkZjB49miZNmjBmzBhpGKyAsomCpKQkGS8U4lLNmtk2/rFYwN29Qt9iKS3lXFISeg8Pdnp6UpCeTtoftwl25udz8Y///1BQEL463fUvWFICHh62WhyEhAHh0MxmM2PHjqW0tJS5c+dKw2AFle01kJiYKGFAiEs1b247rriwsELnEihAcnIyVquVunXrMuLsWc6bzeWf35qXx9a8PADu9PevWBgoKgIfH2jc+Eafhd1JGBAObeHChfz222+sWLGCWrVqqV2O05DxQiGuIiQEunSBzZsrFAYuXrhAYUEB0XXrond358ubXdpXFFsQuececKA3N9IzIBzWxo0b+fjjjxk7diytW7dWuxynI02EQlzFoEG2qYLrjPbl5OSQkZlJeHg4PvZq9isqst0ieOgh+1zPTiQMCIf066+/MmfOHAYOHMi9996rdjlOScYLhbiKPn2gbl3IzLzqlxQVF5Ny/jyBAQEE2fOo4awsaNkSOna03zXtQMKAcDjp6emMGTOGZs2aMXr0aLXLcVqyMiDEVej1MGKEbargChM3FouFpHPn8PTwoPYtt9jvbILc3D8f2x4HHtmRY1UjXJ7ZbGbMmDEAzJ07F/cKdvuKv5PxQiGu4YEHwGCwvVO3Wss/rCgKScnJKIpCZFQUWntNL1kstjDwwAMQF2efa9qRhAHhUObNm8fhw4eZN28eoaGhapfj1MrGC2V1QIgr0Gph+nSoXdt2XsEf+w5cuHCBosJCIiMjcbfXDoFWq+0cgvr1Yfx424qEg5EwIBzGhg0b2LBhA+PGjaNly5Zql+P0Lj3KWAhxBZGRsGgR+PlBairZ2dlkZmURXrs23vZqGLRa4fx5CA2FpUvBnv0HdiRhQDiEX375hblz5/LAAw/wz3/+U+1yagQZLxSiAnr2hNdfp8TDA0tiIkH+/gQFBdnn2mYzpKRAWBi89Rbceqt9rlsFJAwI1aWlpTF69GhatmzJyJEj1S6nRpGJAiGuL6N9e8aHh5MVGEhtqxVNbm6ltyu+jKLYehEuXLBtcrR2rW1vAwcmYUCoymQyMWbMGHQ6nTQMVgGZKBDi2sqaln/z9ydo1y40w4aByWR7R1/ZUGC1Qk4OJCfbvu/55+GLL8AJDlaTHQiFahRFYe7cuRw9epSVK1fe/OEf4m+ioqL44Ycf1C5DCIe1YMECDh06xIoVKwht3BjmzoX+/eHtt2HXLtsLu7s7eHrafun1fzYAKootOBQVQXGxbWLA1xfuvhueego6dVL3yVWChAGhmg0bNvD5558zZcoUmjdvrnY5NVJ0dHT5eKGPj4/a5QjhUD777DM++eQTJk6cePkup7162X4dOwaffALffQdnz0J2tq0P4NIwoNfbQkKrVrbNjO6/H+rVq/4nc5MkDAhVHDx4kHnz5vHggw9y9913q11OjXXpREHTpk1VrkYIx/HLL7/w6quvct9993HPPfdc+YtiYmyjgOPH224ZHDkCiYm2VQCt1ratcP360KSJ7eAhJyZhQFS7ixcvMmbMGFq3bi0Ng1Xs0tMLJQwIYZOWlsaYMWNo0aIF8fHxFfsmf3/bsr8TLf1XhjQQimplMpkYPXo0bm5uzJkzBzd7beohrqhsvDApKUntUoRwCGVNy1qtVpqWLyH/EotqoygKc+bM4fjx49IwWI1kvFAIG0VRePXVVzl69ChvvfUWISEhapfkMGRlQFSbjz/+mC+++IIJEyZIw2A1kjAghM2nn37Kxo0bGTduHC1atFC7HIciYUBUiwMHDrBgwQIeeeQR/vGPf6hdjkuJjIyU2wTC5f3000/MmzePhx56iAEDBqhdjsORMCCqXFFREZMmTaJt27aMGDFC7XJczqXjhUK4oosXLzJ27FjatGnDf/7zH7XLcUjSMyCqnIeHB4sXLyYkJEQaBlVw6emFMlEgXI3JZCI+Ph53d3dpWr4GWRkQVU6r1dKgQQMCAwPVLsUlRUZGAkjfgHA5iqIwa9YsTpw4wfz586Vp+RokDIhqoXHA87tdhb+/PwEBAXJGgXA569ev56uvvmLixIk0a9ZM7XIcmoQBYVdWq1XtEsQVyIFFwtXs37+fBQsW8Oijj9K/f3+1y3F4cvNE3JTjx49z/vx5goKCiImJwcPDA6vVilYrOdORyHihcCXnz59n7NixdOjQgRdffFHtcpyC/Istbti0adN44IEH6NOnD4MHD6Z79+7s3bu3PAgoN3MeuLCrqKgoGS8ULqG4uJj4+Hi8vb2ZNWsWOp1O7ZKcgoQBcUO+/fZb5s6dy8yZM/n555+ZPHkydevW5bbbbmPx4sWArU9AAoFjiIqKkvFCUeMpisLMmTM5c+YM8+fPl6blSpDbBOKG/PTTT9x5553lGwg1b96cHj160LZtW+Lj40lNTWXWrFnSOOggysYLExMTpZFK1Fjvv/8+//d//8esWbOIiYlRuxynIisD4oZYLBZ27dp12cdq1arFiBEjmD9/Pp999hlbtmxRqTrxV2XjhdJEKGqqvXv3smjRIoYOHYrBYFC7HKcjYUDckEcffZRatWoxZcoUMjMzyz/u6+vL4MGD0el0fPnllypWKC4l44WiJktJSWHcuHF06tSJF154Qe1ynJKEAXFDIiMj6d+/P59//jkrV6687EUmODiYO++8k8LCQkpLS1WsUlxKJgpETVRUVMSoUaPw9/dn1qxZMsl0g6RnQFSaoijo9XrmzJmDxWJh6dKlnDp1igEDBtC/f39Onz7NF198wcMPPyydvA5E9hoQNY2iKEyfPp2kpCRWr16Nv7+/2iU5LQkDotI0Gg2lpaXodDrmz59Py5Yteeedd9iyZQtDhw4lMjKSOnXqMG3aNLVLFZeIiorif//7n9plCGE3a9euJSEhgblz59KwYUO1y3FqEgbEDdHpdOWbCz322GP06tWL1NRUfvrpJ1q2bEnnzp3VLlH8RXR0NFlZWeTn5+Pr66t2OULclN27d7NkyRKGDx9ObGys2uU4PQkD4prOnTtHnTp1rngfTqvVlgeCevXqUa9ePbp06aJClaIioqKiANufqYwXCmd27tw5JkyYQNeuXXn22WfVLqdGkE4LcVVTpkzhvvvu4/vvv8dsNv/t84qioNVqOXXqFEeOHFGhQlEZMl4oaoLCwkJGjRpFUFAQM2bMkIZBO5H/iuKKEhISeOONNzh//jwPPfQQH3/8Mfn5+Zd9jUajoaSkhDlz5nD33XdLIHBwMl4onJ2iKEydOpXU1FQWLFiAn5+f2iXVGBIGxN+UlJSwZcsW7rvvPo4fP84999zD0KFDWbBgARcuXLjsaz08PLjrrrt45JFHaNq0qUoVi4qS8ULhzN555x22bt3KjBkzqF+/vtrl1CjSMyD+Rq/Xc9ddd5GTk4OnpyfLli2jTZs2/Otf/+Lo0aNMnTq1fKvPr7/+mtjYWAYMGKBy1aIiZLxQOKudO3eybNkynn76aXr27Kl2OTWOhAHxNxqNhm7dupX/3mq18uyzz9KuXTvuuusujh8/zuLFi9m7dy8zZszgxx9/lO50JyHjhcIZnT17lokTJ9KjRw+efPJJtcupkSQMiOvSaDRYrVY6d+7MyZMnueOOO+jXrx/5+fksX76cevXqqV2iqCAZLxTOpqCggFGjRlGrVi2mT58uDYNVRP6riuvSaDRotVpKS0vx9/fns88+Iz8/n4kTJ/L000+rXZ6ohEvHC4VwdFarlUmTJpGWlsbChQvx8fFRu6QaS8KAqDCdTkdeXh7//Oc/adu2LdOnT1e7JFFJEgaEM1m5ciU7d+5k5syZ5cdwi6ohYUBckaIoV/3cgAED5L6zk/Lz8yMwMFAmCoTD27ZtGytWrOC55567rIdJVA0JA+JvkpOTOXr0KFar9W+f8/Pz4+WXX8bDw0OFyoQ9yESBcHSnTp1i8uTJxMbG8vjjj6tdjkuQBkJxmcLCQv7zn/8A8O6776LX6//2NRqNprrLEnYkYUA4sry8POLj47nllluYOnWq/HtTTSQMiHKX7u61Zs2aKwYB4fyio6PlNo9wSFarlQkTJpCVlcW6devw9vZWuySXIWFAlCvb3WvBggWyu1cNFhUVJeOFwiEtX76cPXv2sGjRovKzNET1kJ4BAcD3338vu3u5iLKJAmkiFI5ky5YtrFq1ihdeeIHbbrtN7XJcjoQBQWJiIhMnTqR79+6yu5cLKAsDSUlJKlcihM2JEyeYOnUqBoOBIUOGqF2OS5Iw4OLKdvcKCQmR40BdhIwXCkeSm5vLqFGjiIyMZNKkSdIwqBLpGXBhVquVKVOmcPHiRdauXSu7e7kQOb1QOAKr1cr48ePJy8tj2bJleHl5qV2Sy5Iw4MJWrVrFtm3bWLhwIXXr1lW7HFGNIiMj5TaBUN2SJUvYu3cvS5YsISIiQu1yXJqsCbuoHTt2sHz5cp555hl69OihdjmimsnKgFCb0Whk7dq1vPTSS3Tq1EntclyehAEXdPbsWSZNmkSvXr144okn1C5HqCA6Oprs7Gzy8vLULkW4oGPHjjFt2jTuvPNOHnnkEbXLEUgYcDn5+fmMHDmSsLAwOQ7UhbVo0YKnn376iltOC1GVsrOziY+Pp379+kycOFEaBh2E9Ay4EKvVyuTJk8nIyGDt2rWyu5cLq1OnDsOHD5cwKKpVaWkpL7/8MkVFRaxYsULOOHEgEgZcSNlxoP/973/lOFCBm5v8+IvqtWjRIvbv38+yZcuoXbu22uWIS8jbAhexfft2OQ5UCKGab775hvfff59Ro0bRvn17tcsRfyFhwAWcPn2aSZMmyXGgQghVHD58mFdeeYW7776bBx98UO1yxBVoFEVRrvdFubm5BAQEkJOTg7+/f3XUJewkPz+foUOH4ubmxurVq6VPQFRYSUkJVqtVNoIRNyUzM5MhQ4YQEhLCypUr5TTUalbR129ZGajBrFYrEydOJCsriwULFkgQEJWyZs0aFi5cSGlpqdqlCCdlsVgYN24cZrOZ+fPnSxBwYBIGarA333yTXbt2MXPmzPLDaYQo8/bbb3Ps2DFKS0uxWCyUlpZy6UJhREQEH374IWfOnFGvSOHUFi5cyM8//8zcuXMJCwtTuxxxDRIGaqitW7fy9ttv8/zzz3P77berXY5wQGPHjmXbtm3odDrc3NzQ6XRoNBoKCws5fPgwiYmJHDp0iIMHD6pdqnBCX3zxBevXr2fMmDG0bdtW7XLEdchsUQ106tQppk6dSt++fRk2bJja5QgHddttt/HRRx+RnJzMTz/9xLFjx0hOTqagoACdTkft2rVp3bo1fn5+apcqnMxvv/3G7Nmzuffeexk4cKDa5YgKkDBQw5QdBxoREcHkyZNldy9xVbfffjsTJkwgNzeXqKgo7r77bho2bEjdunUJDw8nODgYDw8PAgMD1S5VOJGMjAxGjx5Ns2bNGD16tPwb5CQkDNQgZQ2DOTk5ssOguK6oqCjCwsLYuHEjgYGBuLu74+7urnZZwomZzWbGjBmDoii8+uqr0jDoRCQM1CDLly9nz549LF68mMjISLXLEQ6uRYsWaDQafH19JTgKu5g/fz6///47K1asoFatWmqXIypBGghriC1btrBq1SpeeOEFunTponY5wgm0bNmSWbNm4evrq3YpogbYsGEDn376KWPHjqVVq1ZqlyMqScJADXDy5EmmTp2KwWBgyJAhapcjnIS7uzv//Oc/+fnnn7FYLGqXI5zYL7/8wty5c7n//vu555571C5H3AAJA04uNzeXkSNHEhkZyaRJk6RZR1TK6NGjefzxx0lMTASgAhuSCnGZtLQ0Ro8eTcuWLRk1apTa5YgbJD0DTsxqtTJ+/Hjy8vJYtmyZbBsrKq1nz57Uq1ev/O+Ooih/C5RX+pgQACaTidGjR6PT6Zg7d640oDoxCQNObOnSpezdu5clS5YQERGhdjnCCQ0dOvSy32u1tsXC7OxsMjMzCQoKIigoSI3ShINTFIU5c+Zw7NgxVq5cSXBwsNoliZsgYcBJJSQksGbNGl566SU6deqkdjnCyRUUFLBu3TqMRiNHjx7FZDIRFBREWFgY3bt3Z8SIEXh6eqpdpnAgn3zyCV988QVTpkyhefPmapcjbpKEASd07Ngxpk2bxh133MGjjz6qdjnCyZ0/f54XX3yRQ4cO0bZtW+677z7CwsIwm838+uuvLFu2jAsXLrBw4UK1SxUO4qeffmL+/Pk8/PDD3H333WqXI+xAjjB2Mjk5OQwZMgRfX19WrVol79bETVEUhdmzZ/POO++wbNky2rZtS1BQEDqdrvxrtm7dyoABA0hLS5O+FMGFCxcYMmQI9evXZ+nSpbi5yXtKRyZHGNdApaWljB8/noKCAubPny9BQNw0jUbDW2+9xeTJk+nbty+hoaGXBQGANm3a4OnpKacXCkpKSoiPj0ev1zNnzhwJAjWI/Ek6kSVLlrBv3z6WLl0qDYPCbjw8PMjLy/vbx00mE+np6cTHx9OyZUs5sMjFKYrCrFmzOHnyJKtWrZLG0hpGwoCT2LRpE+vWrWPkyJF07NhR7XJEDTJ8+HDeeOMNDh48yD333IO/vz8ZGRkcPnyYHTt2kJiYyLRp02SLaxf30Ucf8fXXXzNjxgyaNm2qdjnCziQMOIFjx44xffp0+vfvzyOPPKJ2OaKG+fe//42npycbNmzgueee4/z581itVsLDw+nevTuvv/46vXv3VrtMoaIff/yRhQsXMnjwYO688061yxFVQBoIHVx2djZDhgwhICCAt99+Gw8PD7VLEjVUSkoKFy9epFatWoSFhckGMgKwTZsMGTKEmJgYXn/99b/1lAjHVtHXb1kZcGClpaW8/PLLFBcX89Zbb0kQEFUqIiLisl4URVHKdx+UHQhdU3FxMfHx8Xh7ezN79mwJAjWYhAEHtnjxYvbv38+yZcuoXbu22uUIFyMhwLUpisIrr7zC2bNnWbVqFQEBAWqXJKqQhAEH9c033/Dee+8RHx9P+/bt1S5HCOFi3nvvPb799ltmz55NTEyM2uWIKib7DDigI0eO8Morr3DXXXfx0EMPqV2OEMLF/PDDDyxevJhhw4YRFxendjmiGkgYcDBZWVnEx8fTqFEjxo8fL8u0QohqlZyczMsvv0znzp15/vnn1S5HVBMJAw7EYrEwbtw4TCYT8+bNQ6/Xq12ScCFms5n09HS1yxAqKioqYtSoUfj7+zNz5szyUyxFzSc9Aw5k0aJFHDx4kGXLlhEeHq52OcLFzJo1i9OnT7N69Wq1SxEqUBSFadOmkZyczOrVq2WM3MVI7HMQX3/9NR988AGjRo3i1ltvVbsc4YKioqI4d+6c2mUIlaxZs4bNmzczbdo0GjZsqHY5oppJGHAAv//+OzNnzmTAgAE88MADapcjXFRkZCQ5OTnk5uaqXYqoZrt372bp0qU88cQTxMbGql2OUIGEAZVlZmYSHx9P48aNGTdunDQMCtVER0cDyOqAi0lMTGTChAl069aNZ555Ru1yhEokDKiorGHQYrFIw6BQXVRUFCBhwJUUFhYSHx9PUFAQM2bMkIZBFyYNhCpauHAhP//8M2+++SZhYWFqlyNcnI+PD8HBwSQmJqpdiqgGVquVKVOmkJqaypo1a/D19VW7JKEiCQMq+fLLL1m/fj0vv/wybdu2VbscIQDb6kBSUpLaZYhq8M477/Ddd9+xcOFC6tevr3Y5QmWyJqSCQ4cOMXv2bO655x4GDhyodjlClIuKipKVARewY8cOli9fztNPP02PHj3ULkc4AAkD1SwjI4PRo0fTpEkTxowZIw2DwqFER0dLGKjhzp49y6RJk+jRowdPPvmk2uUIByFhoBqZzWbGjh1LaWkpc+fOlYZB4XAiIyPJzc2V8cIaqqCggJEjRxIWFsb06dOlYVCUk78J1WjhwoX89ttvzJs3j1q1aqldjhB/I+OFNZeiKLi7uxMTE8OCBQvw8fFRuyThQCQMVJONGzfy8ccfM2bMGFq3bq12OUJcUdl4odwqqHk0Gg1ubm7Mnj27PPQJUUbCQDX49ddfmTNnDgMHDpSGQeHQysYLZWWgZpLbAuJq5G9GFUtPT2fMmDE0a9aM0aNHq12OENclZxQI4XokDFQhs9nMmDFjAJg7dy7u7u4qVyTE9UVHR0sYqAGsVqvaJQgnIpsOVaF58+Zx+PBhVqxYQWhoqNrlCFEhUVFRbN++Xe0yRCWdOHGC1NRUAgMDadSoEZ6enlitVrk1ICpE/pZUkQ0bNrBhwwbGjRtHq1at1C5HiAqLioqS8UInM3PmTO6//35iY2MZNGgQ3bt3Z8+ePeVBQFEUlSsUjk7CQBX45ZdfmDt3Lvfffz///Oc/1S5HiEqRA4ucy5YtW5g5cybTp0/n4MGDTJkyhfr163P77bezaNEiwDZJIIFAXIvcJrCztLQ0Ro8eTcuWLRk1apTa5QhRaWVjZ4mJibRo0ULlasT1HDhwAIPBwIABAwBo3rw5PXr0oF27dowZM4bz588zZ84c2e1UXJOEATsymUyMGTMGnU4nDYPCaXl7e8t4oROxWCz873//Q1GU8hf8WrVq8eKLL+Ln58fSpUvp06cPcXFxKlcqHJncJrATRVGYO3cuR48eZd68eQQHB6tdkhA3TMYLnccjjzxC7dq1mTRpEhkZGeUf9/Hx4dFHH0Wv1/Pll1+qWKFwBhIG7GTDhg18/vnnjBs3TpZWhdOTA4ucR0REBP/4xz/48ssvWbly5WV/bkFBQdxxxx0UFBRgsVhUrFI4OgkDdnDw4EHmzZvHgw8+WH7fTghnJisDzkFRFPR6PbNmzcJgMPDGG28we/ZsvvnmG8B2QuGXX35JeHg4bm5yV1hcnfztuEkXL14sP29g5MiRapcjhF1cOl7o7++vdjniKjQaDaWlpeh0OubNm0fLli1ZtWoVmzdvZtiwYURGRhIaGsqsWbPULlU4OAkDN8FkMjF69Gjc3NyYM2eOJG9RY1w6UdCyZUuVqxHXotPpyjcXGjZsGD179iQlJYWffvqJli1b0rlzZ7VLFE5AXr1ukKIozJkzh+PHj7Ny5UppGBQ1yqV7DUgYcAyJiYlERkZecUdBrVZbHgjq1atHvXr1uP3221WoUjgr6Rm4QR9//DFffPEFEyZMoHnz5mqXI4RdyXihY5k6dSr33nsvO3fuxGw2/+3ziqKg1Wo5ffo0R44cUaFC4ewkDNyAAwcOsGDBAh555BH+8Y9/qF2OEFVCDixyDFu2bOGNN94gLS2Nhx56iI8++oj8/PzLvkaj0VBSUsKrr77K3XffzeHDh1WqVjgrCQOVdOHCBcaNG0fbtm0ZMWKE2uUIUWWioqJkvFBlJSUlJCQkcO+993Ls2DEGDhzIsGHDmD9/PhcuXLjsaz08POjfvz8PP/wwzZo1U6li4aykZ6ASSkpKGD16NHq9XhoGRY0npxeqT6/Xc/fdd5OdnY2npydvvPEGbdq04bnnnuPo0aNMnTqVJk2aAPDNN98QGxsr483ihsirWQUpisLs2bM5ceIEq1atIigoSO2ShKhSMl6oPo1GQ9euXct/b7VaeeaZZ2jbti133XUXx48f5/XXX+fHH39k2rRp7N27F19fXxUrFs5KwkAFrV+/nq+++ooZM2bQtGlTtcsRosrJeKHjKdtXoHPnzpw6dYp+/frRr18/8vPzeeONN2jQoIHaJQonJT0DFbB//34WLFjAoEGDuPPOO9UuR4hqUTZeKH0DjkOj0aDT6SgtLcXPz4/PP/+c/Px8xo8fz7PPPqt2ecKJSRi4jtTUVMaOHUv79u2lYVC4lLLxwqSkJLVLEX+h0+nIy8vjnnvuoXXr1rzyyitqlyScnISBaygpKSE+Ph4vLy9mz56NTqdTuyQhqpUcWKSuoqIiwNYr8FcajYa77rqLPXv2VHdZogaSMHAViqIwc+ZMTp8+zYIFCwgMDFS7JCGqnew1oB6z2czzzz/PzJkz0Wg0f/u8r68vL7/8Mp6enipUJ2oaCQNX8cEHH/DNN98wefJkYmJi1C5HCFVERkZKGFDJvHnzOHz4MAMGDLhiGACu+nEhKkvCwBXs27eP1157jSFDhtCvXz+1yxFCNdHR0eTm5pKTk6N2KS5lw4YNbNiwgXHjxtGqVSu1yxEuQMLAX6SkpDBu3Dg6dOjACy+8oHY5Qqjq0gOLRPX4+eefmTt3Lg888AD//Oc/1S5HuAgJA5coLi4mPj4eHx8faRgUAhkvrG4XL15kzJgxtGrVipEjR6pdjnAhsunQHxRF4ZVXXiExMZF33nmHgIAAtUsSQnXe3t6EhITIeGE1MJlMjB49Gp1Ox6uvvoq7u7vaJQkXUnPCQGEhnDsHxcWgKODhAVFRUMGtOd977z2+/fZbZs+eTePGjau4WCGchxxYVPUURWHOnDkcP36clStXEhwcrHZJwsU4bxgwmWDrVti9Gw4cgBMnbEGgbB5Xo7EFgoYNoW1buO02MBjgCmM4e/fuZfHixQwbNoy4uLjqfR5COLjo6GhOnDihdhk12scff8wXX3zBtGnTaN68udrlCBfkfGHg4kVYvx4++ADOnLG9+Ot0thd5Hx/Q/tEGYbXaAsOvv8LBg7BmDURGwsMPw0MPQZ06wJ8Ng507d+b5559X7WkJ4agiIyPZtm2b2mXUWAcOHGDBggU88sgj/OMf/1C7HOGinCcMKAp89hm88gqcPw9ubhAcbHv3fzVeXlB2799kgtRUmDcP3nkHxo6l6N57GTVqFH5+fsycOROtVvophfirS8cLpZfGvi5cuMDYsWNp166dbHcuVOUcYSAtDSZOhG++sYWC2rVtqwGVoddDWJhtxSAtDWXsWI7MmUOJtzcL3n9fjmgV4iouPb1QZt7tp2y7cw8PD2bPno2bm3P8cyxqJsd/K3zuHAwaBBs32m4D3EgQuJRWC+HhZAPRR4+yqqiIRlfY91sIYRMZGQnIXgP2pCgKs2bN4tSpUyxYsICgoCC1SxIuzrHDwPnzMHSo7b5/eHiFJwOuJ7+ggNTsbKzh4QSmpdke49Qpu1xbiJqmbLxQwoD9fPjhh3z99ddMmjSJJk2aqF2OEA4cBkwmeO45OHzYFgTsNHNrMplITk7Gx9eX0Nq14ZZbbI2ITz0FeXl2eQwhaho5sMh+9u3bx3//+18GDx7MHXfcoXY5QgCOHAaWL4cffoBatewWBEqtVs4lJaHT6ahTpw4asN1yCA+HQ4dg4UK7PI4QNY3sNWAfl253/u9//1vtcoQo55hh4NdfYelS26TAtaYFKkEBzqekYDabiYqMRHfp5IC7u+0WxJo1tn0LhBCXiYqK4ty5cyiKonYpTku2OxeOzDHDwLx5kJMDISF2u2RGRga5eXlERETgcaWAERAARUUwc6ZtYkEIUS4qKoq8vDxyc3PVLsUpKYrCjBkzSExMZMGCBTKiKRyO482yHDsG338P/v62XQQr4FRJCW+mpXG4uJgMiwVPrZYGHh4MCQ6mh58f+fn5XLx4kdDQUPz9/K58EY0GgoLgt99g3z7o1MmOT0oI5ybjhTfn3XffZdOmTbLduXBYjrcy8MkntnMGrvaifQXnzWYKrVbuCgggPjycJ0NDARiZlMT6tDSSk5Px9fWlVq1a176QlxeUlNh2OBRClJPxwhv3ww8/8Prrr/PYY4/JdufCYTnWykBpKXz6qa1PoIKrAgBdfX3p+pexw4eCgnj09GlWnT/PEh+fPxsGr0WjAW9v+PprmDrVbqOMQjg7GS+8McnJybz88st06dKFf/3rX2qXI8RVOdbKwJkzkJlpe0G+SRqNBp+SEvKs1r83DF6Ljw8UFMDRozddgxA1SXR0tEwUVEJhYSGjRo0iICCAV155RbY7Fw7Nsf52Hj5sO3nwCicLVkSR1Uq2xUKSycSKs2f5obiY2wMCrtwweDV6PZjNtlqEEOXKJgrE9SmKwrRp00hJSWHBggWy3blweI51m+DoUdtS/Q2O3Pz3wgU2ZGdjLS3FbDbTy8eHSX80PlVY2e0JWRkQ4jJRUVF89913KIqCphK38VzR6tWr2bJlC3PnzqVBgwZqlyPEdTlWGMjJualvHxQcTA8vL35LSmKfhwce3t5YbmRMUFEgO/umahGipomOjiYvL4+cnBwCAwPVLsdh7d69mzfeeIMnn3yS2NhYtcsRokIc6zaBxWI7VfAG1fPwoJO3N7dbrUzw8iKrsJAXExMrv1GKRmObKhBClIuKigJkouBaEhMTGT9+PN26dePpp59WuxwhKsyxwoCbW6WmCK54CXd36tarh7ubG21MJg7m5LDr9Gkys7KwWCwVu4ii2G3nQyFqChkvvLbCwkLi4+MJCQlhxowZ0jAonIpj3SYIDr7pMKDVaAjw9yfA358QLy/cU1MpBi6kpnIhNRVvHx8C/P3x8/O7+nagGo2tFiFEOW9vb0JDQyUMXIHVamXy5Mmkpqaydu1afGUsWTgZxwoDTZrY3pWXllaqiTDTYiHY7fKnYlEU/i8vD283N26vVw+9oti2U83JIeX8eTSpqfj8EQx8/fz+HD0su6XQtKm9npUQNYYcWHRlq1atYtu2bSxcuJB69eqpXY4QleZYYaBZM9sugMXFtnn/CpqVmkpBaSntvL0Jc3cnw2Lh/3JyOGMy8Z+wMLz/eKEPCgwkKDAQs8VCXm4uubm5JKekoNFo8PP1xd/fH193d7Tu7rZahBCXiYqK4sSJE2qX4VB27NjB8uXLeeaZZ+jRo4fa5QhxQxwrDERH244sPn++UmHA4O/P59nZfJKVRU5pKT46HU09PXkxLIweV9jW2N3NjeDgYIKDgzGbzeTm5pKTm0tScjKBpaXo/Pw4duECnU0m9Hq9PZ+hEE4tKiqKrVu3ynjhH86cOcPEiRPp1asXTzzxhNrlCHHDHCsMaLXw4IO2UwutVtvvK8Dg74/hBjf1cHd3JyQkhJCQEEwlJVgSE9lUqxYzJ0zA19eXXr16YTAY6NSpE25ujvWfS4jqFh0dTX5+vowXAvn5+YwaNYratWszffp0aRgUTs3xXt3uvx+WLYO8PNuxwtVIbzajDwri3g8+oI2fHwkJCWzatImvvvqKgIAA+vTpQ1xcHO3bt5cffOGSysYLExMTXToMWK1WJk2aREZGBmvXrsXbDluoC6EmxwsDdetCnz6wcaPt5MLqetEt22ioWzdo3ZoGGg3PPPMMTz/9NMeOHcNoNGI0GtmwYQMhISH07dsXg8FAq1atJBgIl1E2XpiUlETr1q1VrkY9K1as4Pvvv2fRokXlxzsL4cwcLwwAjB4NO3dCejqEhVXPY2Zm2sLHxImXjTdqNBqaNGlCkyZNeOGFFzh06BBGo5GEhAQ++ugjwsPDiYuLw2Aw0KxZM7mPKmq0svFCV54o2Lp1KytXruT555/n9ttvV7scIezCMcNAo0YQH297YS4qsk0YVKWSEtsEw+jR0LbtVb9Mo9HQsmVLWrZsyUsvvcTBgwdJSEjg66+/5t1336VOnTr069ePuLg4GjVqJMFA1EjR0dEuu9fAqVOnmDp1Kn369OGxxx5Tuxwh7EajVGCv3tzcXAICAsjJyam+07csFhg6FLZuta0OVFVXv8UCqanQoQN88skNnZhYWlrK/v372bRpE9999x25ubnUr1+fuLg4+vXrR926daugcCHUMX36dE6cOMHatWvVLqVa5ebmMmzYMDw8PFi1apX0CQinUNHXb8cNA2Bbuh86FH780TZyaO8tgs1muHDBtqfA++/DLbfY4ZJmfvjhB4xGI9u2baOwsJCYmBgMBgMGg4GIiAg7FC6EelavXs3q1av57rvvXGb1y2q18tJLL/Hbb7+xbt066tSpo3ZJQlRIRV+/HfM2QZngYFi9Gp56Cv73P9s9fX//m96yGLBNK+TkQOvWsGqVXYIA2EYVu3XrRrdu3SgpKWH37t0YjUbeeustlixZQosWLTAYDMTFxRFWXf0QQthRVFSUy40XLlu2jD179rB48WIJAqJGcvw2+NBQWLMGnnjC9k4+JcX2vzfKYrFtalRUZNvT4IMP4I8OaXvz8PCgd+/ezJ49m4SEBGbNmkVoaChLliyhf//+PPXUU6xfv57MzMwqeXwhqsKl44WuICEhgXfeeYd///vfdOnSRe1yhKgSjn2b4K+2bYNJk+DkSdvvAwNtzYUVWSkoKrKNDlqtEBUFU6fCnXfaZ5WhkvLz89m2bRtGo5EffvgBRVHo0KEDBoOB2NhYdf8bC3EdRUVFdO/enenTp9O/f3+1y6lSx48f5/HHH6dnz5688sorLnNbRNQcNaNn4Ery8uDLL+G99+C338Bksn3cw8P2S6u1vcBbrX9OCYDteOQmTeDRR+GeeyAoSLWncKmcnBy2bt2K0Whk//79aDQaunTpgsFgoGfPnnL6mXBId9xxB/fccw/PPvus2qVUmZycHIYMGYKvry+rVq3C8waai4VQW80NA2WsVti3D3bvhl9/hYMHbT0AimL7pdXaegxat7b96tTJtqGQA28QlJGRwZYtW9i0aRM///wzer2e22+/HYPBQPfu3fGq6hFLISro6aefJjQ0lFmzZqldSpUoLS3lxRdf5MiRI6xbt04af4XTqhkNhNei1ULnzrZfYAsH6el/rgR4eNgmEBz4xf+vQkJCePDBB3nwwQe5cOECCQkJGI1Gxo8fj6enJz169MBgMHD77bfLAUpCVZGRkTX69MIlS5awb98+li5dKkFAuATnDQN/pdVW326F1SA8PJzBgwczePBgkpKSyoNBfHw8Pj4+lx2g5O7urna5wsVER0fX2NMLv/32W9atW8fIkSPp2LGj2uUIUS1qThiowSIjI3n88cd5/PHHOX36dPkBSl9//TX+/v7ExsZiMBjo0KGDnJMgqkXZ6YXZ2dkEOUj/jT0cPXq0vDHykUceUbscIaqN8/YMuDhFUTh+/Hj5AUopKSkEBwfTt29f4uLiaNOmjQQDUWWOHTvGoEGDWLVqVY05sCgrK4shQ4YQFBTEypUr8bD3JmdCqKDm9wy4OI1GQ0xMDDExMTz//PP8/vvv5QcorV+/nrCwMPr27Uu/fv1o3rx5jVvKFeoq22vg3LlzNSIMWCwWXn75ZUpKSpg/f74EAeFyJAzUABqNhhYtWtCiRQtGjBjBL7/8gtFo5Ntvv+X9998nIiKifDvkxo0bSzAQN83Ly4tatWrVmAOLFi1axE8//cSyZcsIDw9Xuxwhqp2EgRpGq9XStm1b2rZty6hRo9i/fz8JCQls2LCB1atXU7du3fKTFevXr692ucKJRUVF1YhdCL/++ms++OADxowZw6233qp2OUKoQnoGXITZbGbv3r0YjUa+++47CgsLady4MXFxcRgMBiKraEtmUXPNmDGDY8eOsW7dOrVLuWG///47Tz75JHfccQeTJk2SVTNR49T8TYfEDTOZTOUHKO3YsYPi4mKaN29efoCSLJOKinD20wszMzMZPHgwtWrV4q233pK9O0SNJA2E4qr0ej29evWiV69eFBUVsXPnThISEnjjjTd47bXXaNOmDf369aNPnz6EhISoXa5wUM48Xmg2mxk7diwWi4V58+ZJEBAuT8KAi/Py8ipvLiwoKGD79u1s2rSJBQsWMH/+fNq3b09cXByxsbEuc1ytqJhLJwqcLQz897//5ddff+XNN9+Uo8SFwBmOMBbVxsfHh/79+7No0aLybZA1Gg1z5syhX79+vPjii3z11Vfk5+erXapwAGV9Js42UbBx40bWr1/PmDFjaNOmjdrlCOEQZGVAXFFAQAD33HMP99xzD5mZmWzZsgWj0cjUqVNxd3e/7AAlb2/vG3oMq9WK2WyWmW4nVTZe6EwTBb/++itz5sxh4MCBDBw4UO1yhHAY0kAoKuXixYts3ryZTZs2cejQITw8POjRowdxcXF07dq1Ui/sx48f57XXXuP8+fPExsbyxBNPyMmMTsaZTi9MT09n8ODBREREsHz5cukTEC5BGghFlQgLC2PQoEEMGjSI5OTk8gOUxowZg7e3d/kBSp07d77uAUpZWVmEhobi7u7Oiy++CMBzzz2HTqerjqci7CAqKopjx46pXcZ1mUwmxowZg0ajYe7cuRIEhPgL6RkQN6xOnTo89thjvP/++3z66acMGTKE33//nZdeeol+/fqxY8cOrrXw1KlTJ6ZNm0afPn0ICAigS5cuEgScTHR0NImJidf8c3YE8+fP5/Dhw8ybN4/Q0FC1yxHC4cjKgLCLunXr8tRTT/Hkk09y4sQJEhISaNy48TW/x2QyodfrWbNmDe3bt6dRo0bVVK2wl6ioKAoKChx6vHDDhg1s2LCByZMn07JlS7XLEcIhSRgQdqXRaGjcuPF1gwBQvlT79ddfM3/+/Ov2o5w4cYItW7awe/duBgwYwH333WeXmsWNc/Txwp9//pm5c+fy4IMPMmDAALXLEcJhyW0CoYrS0lLANual0+no1q3bNY9c3r9/P3fccQdvvvkmpaWljBgxgmeeeYaioqLqKllcgSOPF168eJHRo0fTunVrRo4cqXY5Qjg0WRkQqnrzzTfp0aMH9erV+9vnFEVBo9GQnp7OxIkTiYyMZOPGjQQEBLB371769OnDQw89RGxsbPUXLgDHHS80mUyMHj0aNzc35syZg5ub/FMnxLXIyoCodoqilDcKbtmyhXvvvfeKtwisVisAn376Kampqbz00ksEBAQA0LRpU5o1a8bPP/9cfYWLK4qKinKolQFFUZgzZw7Hjx9n/vz5BAcHq12SEA5P4rKoVmazmblz5+Lr6wuAn58f3bt3/9tBN1artfy2wSeffELz5s3p0qVL+edzc3Nxc3PjwoULwJ+rCKL6Odp44ccff8wXX3zBtGnTaN68udrlCOEUZGVAVCutVouvry+vvPIK//nPf8jMzOSzzz4jKSkJoLwHQKvVotFoOH/+POfPn6dDhw7Url27/DoZGRkcPHiQuLg4VZ6H+JMjjRceOHCABQsWMGjQIP7xj3+oXY4QTkPCgKhWOp2OESNGkJaWxpkzZ5gwYQKvvfYajz/+OBaLhdWrVzN06FBOnToFwKlTp9Dr9Zcdq2yxWNi2bRs6nY4+ffoAyKqAisrGC7OyslStIzU1lbFjx9KuXTtGjBihai1COBsJA0I10dHRzJgxgwsXLvDee+/h5ubGiRMn+Pnnn8tXAZo3b05iYuJlO8adPHmSDz/8kPvvvx/4s7dAqKNsvLBsdUcNJSUlxMfH4+npyezZs2XzKiEqScKAcAhlx8guWLCAhIQEvL29URSlfIvjL7/8kqysLJKTkxk/fjyFhYXl42KXrgqUlpZy7tw5CgsLVXkerqgsDKg1UaAoCjNnzuT06dPMnz/fIfc7EMLRSQOhcDhlwUCj0eDh4cGYMWN49NFHady4MfXr1ycrK4sVK1bQqlWr8q8ro9PpmDVrFr/88gvdunWjX79+lT5ASVSOp6cnYWFhqk0UfPDBB3zzzTfMnDmTJk2aqFKDEM5OwoBweJ06deL48ePs3LmT9PR0brvttsuaCf9q0qRJ5Scrlh2g1LNnT+Li4ujSpYscUlMFIiMjVQkDe/fu5bXXXmPIkCH069ev2h9fiJpCjjAWNVpiYiJGoxGj0cipU6fw8/Ojd+/eGAwGOnbsKPeW7eSVV17hyJEjvPvuu9X2mCkpKQwZMoSmTZuyePFi+bMU4goq+votYUC4jJMnT5YHg3PnzhEYGEifPn0wGAy0a9fumtshi2tbs2YNq1atYtu2bdUy2VFcXMzw4cMpKChg3bp18u+SEFdR0ddvuU0gXEbDhg157rnnePbZZzly5AgJCQkYjUY+/fRTQkND6du3LwaDgVatWsmoYiVdOl5Y1Tv+KYrC9OnTSUxMZPXq1RIEhLADCQPC5Wg0Gpo1a0azZs144YUX+O233zAajWzevJkPP/yQ2rVrYzAYMBgMNGnSRIJBBURHRwO28cKqDgPr1q3DaDQyZ84cOfZaCDuRMCBcmlarpXXr1uUn2/30008YjUa++OIL1q5dS1RUVHkwaNiwodrlOqyy0wsTExNp3bp1lT3Onj17WLJkCY8//jh9+/atsscRwtVIz4AQV2CxWNi3bx8JCQls3bqV/Px8GjRoUB4Myt4Jiz/179+fu+66i3/9619Vcv2kpCSGDh1Ky5Ytee2116THQ4gKkAZCIezEZDKxZ88eEhIS2L59O4WFhTRt2hSDwUDfvn2JiIhQu0SH8PTTTxMaGsqsWbPsfu3CwkIef/xxTCYTa9euxc/Pz+6PIURNJA2EQtiJXq+nR48e9OjRg+LiYnbt2oXRaGT58uUsXryYVq1alQeDWrVqqV2uaqKjozly5Ijdr6soCtOmTeP8+fOsXr1agoAQVUDCgBCV4OnpSZ8+fejTpw+FhYXs2LEDo9HIokWLWLhwIe3atcNgMNCnTx+X2xY3Ojoao9Fo9+OkV69ezZYtW5g3bx4NGjSw23WFEH+SMCDEDfL29uaOO+7gjjvuIDc3l23btpGQkMDcuXOZO3cuHTt2pF+/fvTq1cslbq9FRkZSWFho1/HCXbt28cYbb/DUU0/Ru3dvu1xTCPF30jMghJ1lZWXx3XffsWnTJg4cOIBOp+O2224jLi6OXr164e3trXaJVeLEiRM8/PDDvP3227Rp0+amr5eYmMjQoUO59dZbmT9/vjQMCnEDpIFQCAeQlpbGli1bMBqN/PLLL+j1erp164bBYKBbt254enqqXaLdFBcX061bN6ZOncpdd911U9cqKCjgsccew2q1smbNGnx9fe1UpRCuRRoIhXAAtWrV4uGHH+bhhx/m/PnzJCQkkJCQwLhx4/Dy8qJHjx7069evRhygVHZ64c0eZWy1WpkyZQoXL15k7dq1EgSEqAYSBoSoJrfccgtDhw5l6NChJCYmlp+suGnTJnx9fenduzdxcXF06tQJNzfn/NGMioq66dML3377bbZv387ChQupW7eunSoTQlyL3CYQQmWnTp0qP0ApMTGRgICA8gOUbr31Vqe4V55vsXC4sJA5H37IkcJCutxxByVWK+5aLcFubjT19qaZtzfNfXwIdne/6nV27NjByJEjefbZZ3nyySer8RkIUTNJz4AQTkZRFI4dO8amTZtISEjg/PnzhISEXHaAkiMFA0VR+Ck/n48vXmRjRgZ5paXkFRRQWFRESHAwGo0GRVFQAA2g02jw0umIDQzkobAwegYGortkBPHMmTMMHTqUzp078+qrrzrUcxXCWUkYEMKJKYrCb7/9Vt5jkJaWRnh4OHFxcRgMBpo1a6bqAUo/5eUx4+xZDuTlUWy14q3V4qPTUVJQQHJSEo1jYnDT6S57PmZFodBqpaC0FDeNhkbe3oyLjsYQFERBQQFDhw7Fzc2N1atX19iJCyGqm4QBIWoIq9XKwYMHMRqNbNmyhaysLCIjIy87QKm6gkFxaSlLU1JYnpxMXmkpQW5ueGu15Y9fUlLCqVOnqFevHl5eXle/jtVKpsWCu0bDwNBQCles4NiPP5YfDiWEsA8JA0LUQKWlpfz4448YjUa2bt1KXl4e9evXLw8GVdlwl1pSwlNHj/Jjfj6eGg3Bbm5/CyGKonDkyBEiIiIICAi47jVzLRZS8/KwJiezrFEjhnTrVlXlC+GSJAwIUcOZzebyA5S2bdtGYWEhMTEx5cHAngcopZSUMOTwYQ4VFFDL3R2Pa9zPP37iBIEBARU6pyEvL49zycm4165Ng8BAVjVpQjs5e0AIu5EwIIQLKSkpKT9AaefOnZSUlNCiRQv69etH3759CQsLu+FrZ5nNDDp8mJ/y8gh3d8f9Oo19Z8+exc3NjTp16ly35jNnzuDr68stERGcN5uJ0Ov5oHlzYqRnQAi7kDAghIsqLCxk586dGI1Gdu/ejcVioW3btuUHKFXm3ABFURh98iTvXrhAmLs7+gp0+J9PTaW4qIj69etf9WtKrVZOnz6NVqOhXv36aDUarIpCislEF39/1rdoUaHHEkJcm4QBIQR5eXls374do9HInj17AOjQoQMGg4HY2Njr/jwbMzN56uhRPDQa/Cu4EVJGRgbp6ek0adLkql+TeO5ceWBwv2TfgbLGwgl16/L8dVYWhBDXJ2FACHGZ7Oxstm7dSkJCAj/++CNarZYuXbrQr18/evbsiY+Pz2Vfn2exYPjlF84WFxNRia2S8/LySLrCeGGZi2lpZGRkEB0V9bfHBEg3m/HQavmyVSu5XSDETZIwIIS4qvT09PIDlH7++Wf0ej1du3YlLi6O7t274+XlxfsXLhB/8iS13N1xr8To4rXGC3Nzc0lOTiY8PPyqtysURSHZZOLZiAimXuNWgxDi+iQMCCEqJDU1lc2bN2M0Gvn999/x9PSke48efGcwcNbdnToeHhW+VtGpU2Tv3MmFvXtxLyhAHxiIV8OGhD3wANagIM6cOYOfnx91rjPpkG424+fmxvft2hHgpOc0COEIJAwIISotKSkJo9HIhwcPsr13b3RmM4GenvgHBODj48P11geSFi+m6PhxSho0wK9BA3yAzIQErMXFaB5/HH1EBPXq1bvuJkkWReGi2cy8hg15NDzcbs9PCFcjYUAIccOWJCUx/dQpPAsLycvJwWQyodPp8PP3x9/fH5+r3MsvPH4cr/r1SUxOLh8vNKWmcjg+Hm2zZjQdM+ayhsFrSTaZuD80lNdjYuz51IRwKRV9/Zb1NyHE3/xWUIBWpyMsNJSw0FCKS0rIzc0lNyeH7KwsdG5u+P8RDLwv6QvwbtwYAL2HB8VFRQBkabUoISF4FhRUOAgA6DUaDuTnoyiKqucwCOEKJAwIIS5TdhrhpbsMenp44FmrFmG1alFUXExuTg65eXlkZWbi5u6Ov78/Af7+eHp6AqB3dyc3J4ecnBwy0tPRm814BAZWqg5PjYYLJhNpZjNhlZhmEEJUnoQBIcRl8ktLybRY8LjKu3EvT0+8PD0JDw+nsKiI3NxccnJyyMzIwF2vx9/fH51Wi8ViIeX8eTxPnKAkPx//zp0rVYdeqyW7tJRzJSUSBoSoYhIGhBCXKbZaURQFXQWW5r29vPD28qJ2eDgFhYXltxHMFgsmkwmf4mLMX32FV6NGBHTvXqk6tNhWKUqs1ht8JkKIipIwIIS4TFkEuG5n8V/4eHvj4+1N7dq1KSgooDQ3l4vz56Px9qbOv/+N5ga3F5ZuASGqnmz+LYS4jJdOh+aPswJuhEajwUurJeONN1CKioiOj8c9KKjS17ECWo0GTzmjQIgqJz9lQojL+Oh01Nbrb3h53mo2k7RwIabUVCJHjsQjMvKGrlNiteKh1VL/j6ZEIUTVkTAghPibW319Md3AyoBitZK8ZAmFJ04Q+e9/l48a3ogiq5UoDw8CKzGOKIS4MdIzIIT4mxZ/7DZY2Rn/i++/T/5PP+Hbrh2l+fnk7Np12ecDunat8LXMikJ7X98Kf70Q4sZJGBBC/E23gAA8dTryrVb8rnDy4NUUnz0LQP5PP5H/009/+3xFw4DJakWr0dA1IKDCjy2EuHESBoQQf9Pcx4cufn5sy8mpVBioO2GCXR4/22Khjl7PHVc52VAIYV/SMyCEuKJB4eFooNrn/K2KgklReCQ8HM9KBBEhxI2TMCCEuCJDcDAtfXxIN5upwHlmdnPRbKa2Xs/DYWHV9phCuDoJA0KIK/LQapndoAE+Oh1ZFku1PGZBaSkAL9etS4SHR7U8phBCwoAQ4hra+/nxdEQERYpCURXfLjBbrWRZLBiCg3mgVq0qfSwhxOUkDAghrunFOnWICwoiw2ymuIoCgUVRuGA208LHh9kNGqCVI4uFqFYSBoQQ1+Sp07G0cWN6BwaSbjaXL+XbS4nVSqrJRFNvb1Y1bUq4nFAoRLWTMCCEuC4/NzdWNm3KPaGh5JWWkmoy3fDZBWUURSHdbCbdYqGTvz/vN29OXdl6WAhVSBgQQlSIj07HGzEx/LdRI2rp9aSYTGRZLJUOBYqikGuxkGwy4aHV8nJ0NJ+0aCENg0KoSDYdEkJUmFaj4YGwMLoGBLDw3Dm+zMjgvNmMDvDRavHUanHTaP62hbFFUSi2Wim0WjFZrfjodNwZEsKoyEhaypbDQqhOo1RggDg3N5eAgABycnLw9/evjrqEEE7gosnEhrQ0Prp4kXMlJRRZrZT9g6IAl0YCT62WEHd37gsN5YGwMBp6ealQsRCupaKv37IyIIS4YWF6Pc/WqcMzERGkmc0cLizkcEEBmRYLJqsVd60WP52Opt7eNPX2JtLDQyYFhHBAEgaEEDdNo9EQptcTptfTMzBQ7XKEEJUkDYRCCCGEi5MwIIQQQrg4CQNCCCGEi5MwIIQQQrg4CQNCCCGEi5MwIIQQQrg4CQNCCCGEi5MwIIQQQrg4CQNCCCGEi5MwIIQQQrg4CQNCCCGEi5MwIIQQQri4Ch1UVHbKcW5ubpUWI4QQQgj7KXvdLnsdv5oKhYG8vDwAoqKibrIsIYQQQlS3vLw8AgICrvp5jXK9uABYrVZSUlLw8/NDI2eRCyGEEE5BURTy8vKIiIhAq716Z0CFwoAQQgghai5pIBRCCCFcnIQBIYQQwsVJGBBCCCFcnIQBIYQQwsVJGBBCCCFcnIQBIYQQwsVJGBBCCCFc3P8Dt0KI6xKCrg4AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -638,10 +638,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:52.147417Z",
-     "iopub.status.busy": "2024-02-09T16:52:52.147004Z",
-     "iopub.status.idle": "2024-02-09T16:52:52.313773Z",
-     "shell.execute_reply": "2024-02-09T16:52:52.313107Z"
+     "iopub.execute_input": "2024-02-14T16:07:45.838992Z",
+     "iopub.status.busy": "2024-02-14T16:07:45.838754Z",
+     "iopub.status.idle": "2024-02-14T16:07:46.007436Z",
+     "shell.execute_reply": "2024-02-14T16:07:46.006728Z"
     }
    },
    "outputs": [
@@ -692,10 +692,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:52.316569Z",
-     "iopub.status.busy": "2024-02-09T16:52:52.316044Z",
-     "iopub.status.idle": "2024-02-09T16:52:52.485287Z",
-     "shell.execute_reply": "2024-02-09T16:52:52.484452Z"
+     "iopub.execute_input": "2024-02-14T16:07:46.010407Z",
+     "iopub.status.busy": "2024-02-14T16:07:46.009943Z",
+     "iopub.status.idle": "2024-02-14T16:07:46.199893Z",
+     "shell.execute_reply": "2024-02-14T16:07:46.199182Z"
     }
    },
    "outputs": [
@@ -780,10 +780,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:52.488288Z",
-     "iopub.status.busy": "2024-02-09T16:52:52.487701Z",
-     "iopub.status.idle": "2024-02-09T16:52:52.515566Z",
-     "shell.execute_reply": "2024-02-09T16:52:52.514861Z"
+     "iopub.execute_input": "2024-02-14T16:07:46.203403Z",
+     "iopub.status.busy": "2024-02-14T16:07:46.203185Z",
+     "iopub.status.idle": "2024-02-14T16:07:46.230626Z",
+     "shell.execute_reply": "2024-02-14T16:07:46.229962Z"
     }
    },
    "outputs": [
@@ -825,10 +825,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:52:52.518594Z",
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-     "shell.execute_reply": "2024-02-09T16:52:52.615993Z"
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@@ -902,10 +902,10 @@
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-     "shell.execute_reply": "2024-02-09T16:53:07.182907Z"
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@@ -989,7 +989,7 @@
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-      "time: 14.17830228805542\n",
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@@ -1045,10 +1045,10 @@
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-     "shell.execute_reply": "2024-02-09T16:53:21.703256Z"
+     "iopub.execute_input": "2024-02-14T16:08:00.929971Z",
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@@ -1093,10 +1093,10 @@
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-     "shell.execute_reply": "2024-02-09T16:53:21.791876Z"
+     "iopub.execute_input": "2024-02-14T16:08:15.583025Z",
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@@ -1104,14 +1104,14 @@
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-      "/tmp/ipykernel_10116/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
+      "/tmp/ipykernel_10107/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
       "  import qiskit.tools.jupyter\n"
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+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:08:15 2024 UTC</td></tr></table>"
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diff --git a/tutorials/07_examples_vehicle_routing.html b/tutorials/07_examples_vehicle_routing.html
index d230a6e1..8b3f1173 100644
--- a/tutorials/07_examples_vehicle_routing.html
+++ b/tutorials/07_examples_vehicle_routing.html
@@ -1091,7 +1091,7 @@ <h3>Step 5<a class="headerlink" href="#Step-5" title="Permalink to this heading"
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 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/tmp/ipykernel_12741/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
+/tmp/ipykernel_12733/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
   import qiskit.tools.jupyter
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@@ -1099,7 +1099,7 @@ <h3>Step 5<a class="headerlink" href="#Step-5" title="Permalink to this heading"
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+<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:08:21 2024 UTC</td></tr></table></div>
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diff --git a/tutorials/07_examples_vehicle_routing.ipynb b/tutorials/07_examples_vehicle_routing.ipynb
index d9fdffab..857ee2f1 100644
--- a/tutorials/07_examples_vehicle_routing.ipynb
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-     "shell.execute_reply": "2024-02-09T16:53:25.556310Z"
+     "iopub.execute_input": "2024-02-14T16:08:18.252957Z",
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diff --git a/tutorials/08_cvar_optimization.html b/tutorials/08_cvar_optimization.html
index 545e66de..314301a9 100644
--- a/tutorials/08_cvar_optimization.html
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diff --git a/tutorials/08_cvar_optimization.ipynb b/tutorials/08_cvar_optimization.ipynb
index 483a0bab..026fc15f 100644
--- a/tutorials/08_cvar_optimization.ipynb
+++ b/tutorials/08_cvar_optimization.ipynb
@@ -30,10 +30,10 @@
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@@ -57,10 +57,10 @@
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-     "shell.execute_reply": "2024-02-09T16:53:31.796573Z"
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@@ -82,10 +82,10 @@
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@@ -102,10 +102,10 @@
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@@ -132,10 +132,10 @@
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@@ -185,10 +185,10 @@
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@@ -242,10 +242,10 @@
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@@ -324,10 +324,10 @@
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@@ -362,10 +362,10 @@
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-     "shell.execute_reply": "2024-02-09T16:53:33.853726Z"
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@@ -402,10 +402,10 @@
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@@ -413,14 +413,14 @@
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      "output_type": "stream",
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-      "/tmp/ipykernel_13106/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
+      "/tmp/ipykernel_13096/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
       "  import qiskit.tools.jupyter\n"
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+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:08:27 2024 UTC</td></tr></table>"
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-       "style": "IPY_MODEL_b729f9486c474b5ab78bd7aa22c5f237",
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diff --git a/tutorials/09_application_classes.html b/tutorials/09_application_classes.html
index 92172cfe..0c70754c 100644
--- a/tutorials/09_application_classes.html
+++ b/tutorials/09_application_classes.html
@@ -658,7 +658,7 @@ <h2>Vertex cover problem<a class="headerlink" href="#Vertex-cover-problem" title
 
 solution: [0, 1, 3, 4]
 
-time: 1.3467717170715332
+time: 1.335968017578125
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -757,7 +757,7 @@ <h2>Knapsack problem<a class="headerlink" href="#Knapsack-problem" title="Permal
 
 solution: [0, 1, 3]
 
-time: 0.916522741317749
+time: 0.915625810623169
 </pre></div></div>
 </div>
 </section>
@@ -893,7 +893,7 @@ <h2>How to check the Hamiltonian<a class="headerlink" href="#How-to-check-the-Ha
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/tmp/ipykernel_13753/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
+/tmp/ipykernel_13743/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
   import qiskit.tools.jupyter
 </pre></div></div>
 </div>
@@ -901,7 +901,7 @@ <h2>How to check the Hamiltonian<a class="headerlink" href="#How-to-check-the-Ha
 <div class="prompt empty docutils container">
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diff --git a/tutorials/09_application_classes.ipynb b/tutorials/09_application_classes.ipynb
index d06cc479..c39ad639 100644
--- a/tutorials/09_application_classes.ipynb
+++ b/tutorials/09_application_classes.ipynb
@@ -70,10 +70,10 @@
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-     "shell.execute_reply": "2024-02-09T16:53:36.658972Z"
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@@ -103,10 +103,10 @@
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@@ -123,10 +123,10 @@
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@@ -140,10 +140,10 @@
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@@ -176,10 +176,10 @@
    "execution_count": 5,
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@@ -228,10 +228,10 @@
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@@ -271,10 +271,10 @@
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@@ -288,7 +288,7 @@
       "\n",
       "solution: [0, 1, 3, 4]\n",
       "\n",
-      "time: 1.3467717170715332\n"
+      "time: 1.335968017578125\n"
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@@ -328,10 +328,10 @@
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@@ -344,10 +344,10 @@
    "execution_count": 9,
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@@ -381,10 +381,10 @@
    "execution_count": 10,
    "metadata": {
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@@ -413,10 +413,10 @@
    "execution_count": 11,
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-     "shell.execute_reply": "2024-02-09T16:53:40.732059Z"
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@@ -430,7 +430,7 @@
       "\n",
       "solution: [0, 1, 3]\n",
       "\n",
-      "time: 0.916522741317749\n"
+      "time: 0.915625810623169\n"
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@@ -458,10 +458,10 @@
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@@ -474,10 +474,10 @@
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@@ -512,10 +512,10 @@
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@@ -566,10 +566,10 @@
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@@ -603,10 +603,10 @@
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@@ -614,14 +614,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_13753/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
+      "/tmp/ipykernel_13743/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
       "  import qiskit.tools.jupyter\n"
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+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:08:34 2024 UTC</td></tr></table>"
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+       "tabbable": null,
+       "tooltip": null,
+       "value": "<p style='font-family: IBM Plex Sans, Arial, Helvetica, sans-serif; font-size: 20px; font-weight: medium;'>Circuit Properties</p>"
       }
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diff --git a/tutorials/10_warm_start_qaoa.html b/tutorials/10_warm_start_qaoa.html
index 6324c98f..ec860504 100644
--- a/tutorials/10_warm_start_qaoa.html
+++ b/tutorials/10_warm_start_qaoa.html
@@ -958,7 +958,7 @@ <h2>Warm-start QAOA<a class="headerlink" href="#id2" title="Permalink to this he
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/tmp/ipykernel_14175/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
+/tmp/ipykernel_14165/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
   import qiskit.tools.jupyter
 </pre></div></div>
 </div>
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+<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:09:15 2024 UTC</td></tr></table></div>
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diff --git a/tutorials/10_warm_start_qaoa.ipynb b/tutorials/10_warm_start_qaoa.ipynb
index bfdad90a..f0b77410 100644
--- a/tutorials/10_warm_start_qaoa.ipynb
+++ b/tutorials/10_warm_start_qaoa.ipynb
@@ -36,10 +36,10 @@
    "id": "engaging-agreement",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:43.004679Z",
-     "iopub.status.busy": "2024-02-09T16:53:43.004482Z",
-     "iopub.status.idle": "2024-02-09T16:53:43.867474Z",
-     "shell.execute_reply": "2024-02-09T16:53:43.866801Z"
+     "iopub.execute_input": "2024-02-14T16:08:36.770164Z",
+     "iopub.status.busy": "2024-02-14T16:08:36.769960Z",
+     "iopub.status.idle": "2024-02-14T16:08:37.687224Z",
+     "shell.execute_reply": "2024-02-14T16:08:37.686451Z"
     }
    },
    "outputs": [],
@@ -78,10 +78,10 @@
    "id": "southwest-stake",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:43.870496Z",
-     "iopub.status.busy": "2024-02-09T16:53:43.870047Z",
-     "iopub.status.idle": "2024-02-09T16:53:43.876066Z",
-     "shell.execute_reply": "2024-02-09T16:53:43.875428Z"
+     "iopub.execute_input": "2024-02-14T16:08:37.690561Z",
+     "iopub.status.busy": "2024-02-14T16:08:37.690201Z",
+     "iopub.status.idle": "2024-02-14T16:08:37.696426Z",
+     "shell.execute_reply": "2024-02-14T16:08:37.695780Z"
     }
    },
    "outputs": [],
@@ -127,10 +127,10 @@
    "id": "laden-number",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:43.878469Z",
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-     "iopub.status.idle": "2024-02-09T16:53:43.882598Z",
-     "shell.execute_reply": "2024-02-09T16:53:43.882042Z"
+     "iopub.execute_input": "2024-02-14T16:08:37.698961Z",
+     "iopub.status.busy": "2024-02-14T16:08:37.698595Z",
+     "iopub.status.idle": "2024-02-14T16:08:37.702952Z",
+     "shell.execute_reply": "2024-02-14T16:08:37.702307Z"
     }
    },
    "outputs": [],
@@ -162,10 +162,10 @@
    "id": "supreme-wallace",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:43.884987Z",
-     "iopub.status.busy": "2024-02-09T16:53:43.884611Z",
-     "iopub.status.idle": "2024-02-09T16:53:43.920005Z",
-     "shell.execute_reply": "2024-02-09T16:53:43.919278Z"
+     "iopub.execute_input": "2024-02-14T16:08:37.705371Z",
+     "iopub.status.busy": "2024-02-14T16:08:37.705022Z",
+     "iopub.status.idle": "2024-02-14T16:08:37.742451Z",
+     "shell.execute_reply": "2024-02-14T16:08:37.741661Z"
     }
    },
    "outputs": [
@@ -213,10 +213,10 @@
    "id": "contrary-bumper",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:43.922826Z",
-     "iopub.status.busy": "2024-02-09T16:53:43.922439Z",
-     "iopub.status.idle": "2024-02-09T16:53:43.948464Z",
-     "shell.execute_reply": "2024-02-09T16:53:43.947782Z"
+     "iopub.execute_input": "2024-02-14T16:08:37.745156Z",
+     "iopub.status.busy": "2024-02-14T16:08:37.744882Z",
+     "iopub.status.idle": "2024-02-14T16:08:37.772611Z",
+     "shell.execute_reply": "2024-02-14T16:08:37.771858Z"
     }
    },
    "outputs": [
@@ -249,10 +249,10 @@
    "id": "spectacular-african",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:43.951131Z",
-     "iopub.status.busy": "2024-02-09T16:53:43.950644Z",
-     "iopub.status.idle": "2024-02-09T16:53:43.968009Z",
-     "shell.execute_reply": "2024-02-09T16:53:43.967352Z"
+     "iopub.execute_input": "2024-02-14T16:08:37.775492Z",
+     "iopub.status.busy": "2024-02-14T16:08:37.775218Z",
+     "iopub.status.idle": "2024-02-14T16:08:37.793557Z",
+     "shell.execute_reply": "2024-02-14T16:08:37.792938Z"
     }
    },
    "outputs": [
@@ -306,10 +306,10 @@
    "id": "moderate-photograph",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:43.970311Z",
-     "iopub.status.busy": "2024-02-09T16:53:43.970112Z",
-     "iopub.status.idle": "2024-02-09T16:53:43.993624Z",
-     "shell.execute_reply": "2024-02-09T16:53:43.993004Z"
+     "iopub.execute_input": "2024-02-14T16:08:37.796316Z",
+     "iopub.status.busy": "2024-02-14T16:08:37.795921Z",
+     "iopub.status.idle": "2024-02-14T16:08:37.821821Z",
+     "shell.execute_reply": "2024-02-14T16:08:37.821059Z"
     }
    },
    "outputs": [
@@ -334,10 +334,10 @@
    "id": "smoking-discretion",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:43.996112Z",
-     "iopub.status.busy": "2024-02-09T16:53:43.995716Z",
-     "iopub.status.idle": "2024-02-09T16:53:43.999528Z",
-     "shell.execute_reply": "2024-02-09T16:53:43.998861Z"
+     "iopub.execute_input": "2024-02-14T16:08:37.824829Z",
+     "iopub.status.busy": "2024-02-14T16:08:37.824389Z",
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+     "shell.execute_reply": "2024-02-14T16:08:37.827742Z"
     }
    },
    "outputs": [
@@ -374,10 +374,10 @@
    "id": "recreational-packing",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:44.002007Z",
-     "iopub.status.busy": "2024-02-09T16:53:44.001519Z",
-     "iopub.status.idle": "2024-02-09T16:53:44.004975Z",
-     "shell.execute_reply": "2024-02-09T16:53:44.004418Z"
+     "iopub.execute_input": "2024-02-14T16:08:37.831006Z",
+     "iopub.status.busy": "2024-02-14T16:08:37.830623Z",
+     "iopub.status.idle": "2024-02-14T16:08:37.834213Z",
+     "shell.execute_reply": "2024-02-14T16:08:37.833613Z"
     }
    },
    "outputs": [],
@@ -393,10 +393,10 @@
    "id": "pursuant-pendant",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:44.007248Z",
-     "iopub.status.busy": "2024-02-09T16:53:44.006886Z",
-     "iopub.status.idle": "2024-02-09T16:53:44.009812Z",
-     "shell.execute_reply": "2024-02-09T16:53:44.009218Z"
+     "iopub.execute_input": "2024-02-14T16:08:37.836694Z",
+     "iopub.status.busy": "2024-02-14T16:08:37.836321Z",
+     "iopub.status.idle": "2024-02-14T16:08:37.839326Z",
+     "shell.execute_reply": "2024-02-14T16:08:37.838691Z"
     }
    },
    "outputs": [],
@@ -410,10 +410,10 @@
    "id": "painful-packing",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:44.012091Z",
-     "iopub.status.busy": "2024-02-09T16:53:44.011731Z",
-     "iopub.status.idle": "2024-02-09T16:53:56.998632Z",
-     "shell.execute_reply": "2024-02-09T16:53:56.997922Z"
+     "iopub.execute_input": "2024-02-14T16:08:37.841624Z",
+     "iopub.status.busy": "2024-02-14T16:08:37.841416Z",
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+     "shell.execute_reply": "2024-02-14T16:08:50.926997Z"
     }
    },
    "outputs": [
@@ -455,10 +455,10 @@
    "id": "controversial-model",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:57.001274Z",
-     "iopub.status.busy": "2024-02-09T16:53:57.001054Z",
-     "iopub.status.idle": "2024-02-09T16:53:57.552341Z",
-     "shell.execute_reply": "2024-02-09T16:53:57.551558Z"
+     "iopub.execute_input": "2024-02-14T16:08:50.930280Z",
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+     "shell.execute_reply": "2024-02-14T16:08:51.512511Z"
     }
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    "outputs": [
@@ -510,10 +510,10 @@
    "id": "pacific-destiny",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:57.555007Z",
-     "iopub.status.busy": "2024-02-09T16:53:57.554716Z",
-     "iopub.status.idle": "2024-02-09T16:53:58.044557Z",
-     "shell.execute_reply": "2024-02-09T16:53:58.043823Z"
+     "iopub.execute_input": "2024-02-14T16:08:51.516319Z",
+     "iopub.status.busy": "2024-02-14T16:08:51.515799Z",
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     },
     "tags": [
      "nbsphinx-thumbnail"
@@ -560,10 +560,10 @@
    "id": "settled-mistress",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:58.047562Z",
-     "iopub.status.busy": "2024-02-09T16:53:58.046995Z",
-     "iopub.status.idle": "2024-02-09T16:53:58.050682Z",
-     "shell.execute_reply": "2024-02-09T16:53:58.050024Z"
+     "iopub.execute_input": "2024-02-14T16:08:52.042252Z",
+     "iopub.status.busy": "2024-02-14T16:08:52.041745Z",
+     "iopub.status.idle": "2024-02-14T16:08:52.045307Z",
+     "shell.execute_reply": "2024-02-14T16:08:52.044647Z"
     }
    },
    "outputs": [],
@@ -583,10 +583,10 @@
    "id": "wrapped-alberta",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:58.053022Z",
-     "iopub.status.busy": "2024-02-09T16:53:58.052650Z",
-     "iopub.status.idle": "2024-02-09T16:53:58.055649Z",
-     "shell.execute_reply": "2024-02-09T16:53:58.055024Z"
+     "iopub.execute_input": "2024-02-14T16:08:52.047604Z",
+     "iopub.status.busy": "2024-02-14T16:08:52.047395Z",
+     "iopub.status.idle": "2024-02-14T16:08:52.050476Z",
+     "shell.execute_reply": "2024-02-14T16:08:52.049782Z"
     }
    },
    "outputs": [],
@@ -600,10 +600,10 @@
    "id": "aerial-parcel",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:53:58.058045Z",
-     "iopub.status.busy": "2024-02-09T16:53:58.057843Z",
-     "iopub.status.idle": "2024-02-09T16:54:09.718160Z",
-     "shell.execute_reply": "2024-02-09T16:54:09.717501Z"
+     "iopub.execute_input": "2024-02-14T16:08:52.052975Z",
+     "iopub.status.busy": "2024-02-14T16:08:52.052613Z",
+     "iopub.status.idle": "2024-02-14T16:09:03.772185Z",
+     "shell.execute_reply": "2024-02-14T16:09:03.771459Z"
     }
    },
    "outputs": [
@@ -638,10 +638,10 @@
    "id": "sharp-military",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:09.720729Z",
-     "iopub.status.busy": "2024-02-09T16:54:09.720506Z",
-     "iopub.status.idle": "2024-02-09T16:54:09.727324Z",
-     "shell.execute_reply": "2024-02-09T16:54:09.726744Z"
+     "iopub.execute_input": "2024-02-14T16:09:03.775094Z",
+     "iopub.status.busy": "2024-02-14T16:09:03.774605Z",
+     "iopub.status.idle": "2024-02-14T16:09:03.781314Z",
+     "shell.execute_reply": "2024-02-14T16:09:03.780656Z"
     }
    },
    "outputs": [
@@ -686,10 +686,10 @@
    "id": "political-dependence",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:09.729787Z",
-     "iopub.status.busy": "2024-02-09T16:54:09.729464Z",
-     "iopub.status.idle": "2024-02-09T16:54:09.733955Z",
-     "shell.execute_reply": "2024-02-09T16:54:09.733385Z"
+     "iopub.execute_input": "2024-02-14T16:09:03.783965Z",
+     "iopub.status.busy": "2024-02-14T16:09:03.783600Z",
+     "iopub.status.idle": "2024-02-14T16:09:03.787876Z",
+     "shell.execute_reply": "2024-02-14T16:09:03.787242Z"
     }
    },
    "outputs": [
@@ -729,10 +729,10 @@
    "id": "random-happiness",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:09.736164Z",
-     "iopub.status.busy": "2024-02-09T16:54:09.735963Z",
-     "iopub.status.idle": "2024-02-09T16:54:09.738973Z",
-     "shell.execute_reply": "2024-02-09T16:54:09.738412Z"
+     "iopub.execute_input": "2024-02-14T16:09:03.790665Z",
+     "iopub.status.busy": "2024-02-14T16:09:03.790094Z",
+     "iopub.status.idle": "2024-02-14T16:09:03.793262Z",
+     "shell.execute_reply": "2024-02-14T16:09:03.792626Z"
     }
    },
    "outputs": [],
@@ -746,10 +746,10 @@
    "id": "tracked-encoding",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:09.741226Z",
-     "iopub.status.busy": "2024-02-09T16:54:09.741026Z",
-     "iopub.status.idle": "2024-02-09T16:54:09.744670Z",
-     "shell.execute_reply": "2024-02-09T16:54:09.744088Z"
+     "iopub.execute_input": "2024-02-14T16:09:03.795616Z",
+     "iopub.status.busy": "2024-02-14T16:09:03.795252Z",
+     "iopub.status.idle": "2024-02-14T16:09:03.798806Z",
+     "shell.execute_reply": "2024-02-14T16:09:03.798149Z"
     }
    },
    "outputs": [],
@@ -766,10 +766,10 @@
    "id": "insured-champagne",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:09.746888Z",
-     "iopub.status.busy": "2024-02-09T16:54:09.746691Z",
-     "iopub.status.idle": "2024-02-09T16:54:21.568012Z",
-     "shell.execute_reply": "2024-02-09T16:54:21.567337Z"
+     "iopub.execute_input": "2024-02-14T16:09:03.801209Z",
+     "iopub.status.busy": "2024-02-14T16:09:03.800844Z",
+     "iopub.status.idle": "2024-02-14T16:09:15.659326Z",
+     "shell.execute_reply": "2024-02-14T16:09:15.658573Z"
     }
    },
    "outputs": [
@@ -794,10 +794,10 @@
    "id": "grave-initial",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:21.570970Z",
-     "iopub.status.busy": "2024-02-09T16:54:21.570386Z",
-     "iopub.status.idle": "2024-02-09T16:54:21.575235Z",
-     "shell.execute_reply": "2024-02-09T16:54:21.574587Z"
+     "iopub.execute_input": "2024-02-14T16:09:15.662302Z",
+     "iopub.status.busy": "2024-02-14T16:09:15.661904Z",
+     "iopub.status.idle": "2024-02-14T16:09:15.666529Z",
+     "shell.execute_reply": "2024-02-14T16:09:15.665881Z"
     }
    },
    "outputs": [
@@ -827,10 +827,10 @@
    "id": "weird-dispatch",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:21.577710Z",
-     "iopub.status.busy": "2024-02-09T16:54:21.577342Z",
-     "iopub.status.idle": "2024-02-09T16:54:21.664810Z",
-     "shell.execute_reply": "2024-02-09T16:54:21.664105Z"
+     "iopub.execute_input": "2024-02-14T16:09:15.669099Z",
+     "iopub.status.busy": "2024-02-14T16:09:15.668730Z",
+     "iopub.status.idle": "2024-02-14T16:09:15.757111Z",
+     "shell.execute_reply": "2024-02-14T16:09:15.756450Z"
     }
    },
    "outputs": [
@@ -838,14 +838,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_14175/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
+      "/tmp/ipykernel_14165/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
       "  import qiskit.tools.jupyter\n"
      ]
     },
     {
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+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:09:15 2024 UTC</td></tr></table>"
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@@ -904,30 +904,7 @@
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-       "description_allow_html": false,
-       "layout": "IPY_MODEL_b13e9cccd54842ce9c8d6badcf3e5ce4",
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-       "style": "IPY_MODEL_6bd3f754dfa44cb78af8808dc5622e24",
-       "tabbable": null,
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-       "value": "<p style='font-family: IBM Plex Sans, Arial, Helvetica, sans-serif; font-size: 20px; font-weight: medium;'>Circuit Properties</p>"
-      }
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-     "6bd3f754dfa44cb78af8808dc5622e24": {
+     "874ce8a671f242e59719ee81666490df": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "HTMLStyleModel",
@@ -945,7 +922,7 @@
        "text_color": null
       }
      },
-     "b13e9cccd54842ce9c8d6badcf3e5ce4": {
+     "b332de5ee86441f99f6c2712c151db05": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -997,6 +974,29 @@
        "visibility": null,
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+     },
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+       "style": "IPY_MODEL_874ce8a671f242e59719ee81666490df",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "<p style='font-family: IBM Plex Sans, Arial, Helvetica, sans-serif; font-size: 20px; font-weight: medium;'>Circuit Properties</p>"
+      }
      }
     },
     "version_major": 2,
diff --git a/tutorials/11_using_classical_optimization_solvers_and_models.html b/tutorials/11_using_classical_optimization_solvers_and_models.html
index f9317d73..411db452 100644
--- a/tutorials/11_using_classical_optimization_solvers_and_models.html
+++ b/tutorials/11_using_classical_optimization_solvers_and_models.html
@@ -672,11 +672,11 @@ <h2>CplexSolver and GurobiSolver<a class="headerlink" href="#CplexSolver-and-Gur
 status: SUCCESS
 
 display the best 5 solution samples
-SolutionSample(x=array([1., 4.]), fval=4.0, probability=0.051276918982979904, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([1., 3.]), fval=3.0, probability=0.057468569687663, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([1., 2.]), fval=2.0, probability=0.1352112491092225, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([1., 1.]), fval=1.0, probability=0.13617505365527624, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([0., 0.]), fval=0.0, probability=0.0776693319328813, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([1., 4.]), fval=4.0, probability=0.1305411562032251, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([1., 3.]), fval=3.0, probability=0.1167094470574642, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([1., 2.]), fval=2.0, probability=0.08161165517949098, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([1., 1.]), fval=1.0, probability=0.1136687559480952, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([0., 0.]), fval=0.0, probability=0.0464259187911496, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
 </pre></div></div>
 </div>
 </section>
@@ -997,7 +997,7 @@ <h3>Indicator constraints of Docplex<a class="headerlink" href="#Indicator-const
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/tmp/ipykernel_20358/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
+/tmp/ipykernel_20408/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
   import qiskit.tools.jupyter
 </pre></div></div>
 </div>
@@ -1005,7 +1005,7 @@ <h3>Indicator constraints of Docplex<a class="headerlink" href="#Indicator-const
 <div class="prompt empty docutils container">
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 <div class="output_area rendered_html docutils container">
-<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Fri Feb 09 16:54:28 2024 UTC</td></tr></table></div>
+<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:09:22 2024 UTC</td></tr></table></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
@@ -1014,7 +1014,7 @@ <h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr
 <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, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div></div>
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diff --git a/tutorials/11_using_classical_optimization_solvers_and_models.ipynb b/tutorials/11_using_classical_optimization_solvers_and_models.ipynb
index bf9d9e8a..63a1461f 100644
--- a/tutorials/11_using_classical_optimization_solvers_and_models.ipynb
+++ b/tutorials/11_using_classical_optimization_solvers_and_models.ipynb
@@ -40,10 +40,10 @@
    "execution_count": 1,
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-     "iopub.status.busy": "2024-02-09T16:54:24.035294Z",
-     "iopub.status.idle": "2024-02-09T16:54:24.703263Z",
-     "shell.execute_reply": "2024-02-09T16:54:24.702541Z"
+     "iopub.execute_input": "2024-02-14T16:09:18.390242Z",
+     "iopub.status.busy": "2024-02-14T16:09:18.390040Z",
+     "iopub.status.idle": "2024-02-14T16:09:19.050155Z",
+     "shell.execute_reply": "2024-02-14T16:09:19.049348Z"
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@@ -86,10 +86,10 @@
    "execution_count": 2,
    "metadata": {
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-     "iopub.execute_input": "2024-02-09T16:54:24.706099Z",
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-     "iopub.status.idle": "2024-02-09T16:54:24.971505Z",
-     "shell.execute_reply": "2024-02-09T16:54:24.970772Z"
+     "iopub.execute_input": "2024-02-14T16:09:19.052956Z",
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+     "shell.execute_reply": "2024-02-14T16:09:19.316366Z"
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@@ -142,10 +142,10 @@
    "execution_count": 3,
    "metadata": {
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-     "iopub.execute_input": "2024-02-09T16:54:24.974471Z",
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-     "shell.execute_reply": "2024-02-09T16:54:25.010421Z"
+     "iopub.execute_input": "2024-02-14T16:09:19.320201Z",
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@@ -261,10 +261,10 @@
    "execution_count": 4,
    "metadata": {
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-     "iopub.execute_input": "2024-02-09T16:54:25.014210Z",
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-     "iopub.status.idle": "2024-02-09T16:54:26.961070Z",
-     "shell.execute_reply": "2024-02-09T16:54:26.960361Z"
+     "iopub.execute_input": "2024-02-14T16:09:19.362407Z",
+     "iopub.status.busy": "2024-02-14T16:09:19.361983Z",
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+     "shell.execute_reply": "2024-02-14T16:09:21.352468Z"
     }
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    "outputs": [
@@ -277,11 +277,11 @@
       "status: SUCCESS\n",
       "\n",
       "display the best 5 solution samples\n",
-      "SolutionSample(x=array([1., 4.]), fval=4.0, probability=0.051276918982979904, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 3.]), fval=3.0, probability=0.057468569687663, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 2.]), fval=2.0, probability=0.1352112491092225, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1., 1.]), fval=1.0, probability=0.13617505365527624, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0., 0.]), fval=0.0, probability=0.0776693319328813, status=<OptimizationResultStatus.SUCCESS: 0>)\n"
+      "SolutionSample(x=array([1., 4.]), fval=4.0, probability=0.1305411562032251, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 3.]), fval=3.0, probability=0.1167094470574642, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 2.]), fval=2.0, probability=0.08161165517949098, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1., 1.]), fval=1.0, probability=0.1136687559480952, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0., 0.]), fval=0.0, probability=0.0464259187911496, status=<OptimizationResultStatus.SUCCESS: 0>)\n"
      ]
     }
    ],
@@ -316,10 +316,10 @@
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-     "shell.execute_reply": "2024-02-09T16:54:26.984380Z"
+     "iopub.execute_input": "2024-02-14T16:09:21.356056Z",
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@@ -360,10 +360,10 @@
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-     "shell.execute_reply": "2024-02-09T16:54:26.993877Z"
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@@ -427,10 +427,10 @@
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@@ -498,10 +498,10 @@
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-     "shell.execute_reply": "2024-02-09T16:54:27.030218Z"
+     "iopub.execute_input": "2024-02-14T16:09:21.399417Z",
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+     "shell.execute_reply": "2024-02-14T16:09:21.423616Z"
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@@ -581,10 +581,10 @@
    "execution_count": 9,
    "metadata": {
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-     "iopub.execute_input": "2024-02-09T16:54:27.033522Z",
-     "iopub.status.busy": "2024-02-09T16:54:27.033101Z",
-     "iopub.status.idle": "2024-02-09T16:54:27.052464Z",
-     "shell.execute_reply": "2024-02-09T16:54:27.051786Z"
+     "iopub.execute_input": "2024-02-14T16:09:21.427079Z",
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+     "shell.execute_reply": "2024-02-14T16:09:21.446007Z"
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@@ -643,10 +643,10 @@
    "execution_count": 10,
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-     "shell.execute_reply": "2024-02-09T16:54:28.013955Z"
+     "iopub.execute_input": "2024-02-14T16:09:21.449613Z",
+     "iopub.status.busy": "2024-02-14T16:09:21.449172Z",
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+     "shell.execute_reply": "2024-02-14T16:09:22.395537Z"
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@@ -684,10 +684,10 @@
    "execution_count": 11,
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-     "iopub.execute_input": "2024-02-09T16:54:28.017441Z",
-     "iopub.status.busy": "2024-02-09T16:54:28.016907Z",
-     "iopub.status.idle": "2024-02-09T16:54:28.448361Z",
-     "shell.execute_reply": "2024-02-09T16:54:28.447568Z"
+     "iopub.execute_input": "2024-02-14T16:09:22.398822Z",
+     "iopub.status.busy": "2024-02-14T16:09:22.398419Z",
+     "iopub.status.idle": "2024-02-14T16:09:22.831686Z",
+     "shell.execute_reply": "2024-02-14T16:09:22.830919Z"
     }
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    "outputs": [
@@ -695,14 +695,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_20358/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
+      "/tmp/ipykernel_20408/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
       "  import qiskit.tools.jupyter\n"
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     },
     {
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+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:09:22 2024 UTC</td></tr></table>"
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diff --git a/tutorials/12_quantum_random_access_optimizer.html b/tutorials/12_quantum_random_access_optimizer.html
index 000e55d5..da77022d 100644
--- a/tutorials/12_quantum_random_access_optimizer.html
+++ b/tutorials/12_quantum_random_access_optimizer.html
@@ -457,7 +457,7 @@ <h1>Quantum Random Access Optimization<a class="headerlink" href="#Quantum-Rando
 <h2>Set up a combinatorial optimization problem<a class="headerlink" href="#Set-up-a-combinatorial-optimization-problem" title="Permalink to this heading">#</a></h2>
 <p>In this tutorial, we will consider a random max-cut problem instance and use QRAO to try to find a maximum cut; in other words, a partition of the graph’s vertices (nodes) into two sets that maximizes the number of edges between the sets.</p>
 <p>To begin, we utilize the <code class="docutils literal notranslate"><span class="pre">Maxcut</span></code> class from Qiskit Optimization’s application module. It allows us to generate a <code class="docutils literal notranslate"><span class="pre">QuadraticProgram</span></code> representation of the given graph.</p>
-<p>Note that once our problem has been represented as a <code class="docutils literal notranslate"><span class="pre">QuadraticProgram</span></code>, it will need to be converted to the correct type, a <a class="reference external" href="https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization">quadratic unconstrained binary optimization (QUBO)</a> problem, so that it is compatible with QRAO. A <code class="docutils literal notranslate"><span class="pre">QuadraticProgram</span></code> generated by <code class="docutils literal notranslate"><span class="pre">Maxcut</span></code> is already a QUBO, but if you define your own problem be sure you convert it to a QUBO before proceeding. Here is <a class="reference external" href="https://qiskit.org/documentation/optimization/tutorials/02_converters_for_quadratic_programs.html">a
+<p>Note that once our problem has been represented as a <code class="docutils literal notranslate"><span class="pre">QuadraticProgram</span></code>, it will need to be converted to the correct type, a <a class="reference external" href="https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization">quadratic unconstrained binary optimization (QUBO)</a> problem, so that it is compatible with QRAO. A <code class="docutils literal notranslate"><span class="pre">QuadraticProgram</span></code> generated by <code class="docutils literal notranslate"><span class="pre">Maxcut</span></code> is already a QUBO, but if you define your own problem be sure you convert it to a QUBO before proceeding. Here is <a class="reference external" href="https://qiskit-community.github.io/qiskit-optimization/tutorials/02_converters_for_quadratic_programs.html">a
 tutorial</a> on converting <code class="docutils literal notranslate"><span class="pre">QuadraticPrograms</span></code>.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
@@ -509,7 +509,7 @@ <h2>Set up a combinatorial optimization problem<a class="headerlink" href="#Set-
 <section id="Encode-the-problem-into-a-quantum-Hamiltonian">
 <h2>Encode the problem into a quantum Hamiltonian<a class="headerlink" href="#Encode-the-problem-into-a-quantum-Hamiltonian" title="Permalink to this heading">#</a></h2>
 <p>Once we have appropriately configured our problem, we proceed to encode it using the <code class="docutils literal notranslate"><span class="pre">QuantumRandomAccessEncoding</span></code> class from the <code class="docutils literal notranslate"><span class="pre">qrao</span></code> module. This encoding step allows us to generate a quantum Hamiltonian operator that represents our problem. In particular, we employ a Quantum Random Access Code (QRAC) to encode multiple classical binary variables (corresponding to the nodes of our max-cut graph) into each qubit.</p>
-<p>It’s important to note that the resulting “relaxed” Hamiltonian, produced by this encoding, will not be diagonal. This differs from the standard workflow in <code class="docutils literal notranslate"><span class="pre">qiskit-optimization</span></code>, which typically generates a diagonal (Ising) Hamiltonian suitable for optimization using a <code class="docutils literal notranslate"><span class="pre">MinimumEigenOptimizer</span></code>. You can find a tutorial on the <code class="docutils literal notranslate"><span class="pre">MinimumEigenOptimizer</span></code> <a class="reference external" href="https://qiskit.org/documentation/optimization/tutorials/03_minimum_eigen_optimizer.html">here</a>.</p>
+<p>It’s important to note that the resulting “relaxed” Hamiltonian, produced by this encoding, will not be diagonal. This differs from the standard workflow in <code class="docutils literal notranslate"><span class="pre">qiskit-optimization</span></code>, which typically generates a diagonal (Ising) Hamiltonian suitable for optimization using a <code class="docutils literal notranslate"><span class="pre">MinimumEigenOptimizer</span></code>. You can find a tutorial on the <code class="docutils literal notranslate"><span class="pre">MinimumEigenOptimizer</span></code> <a class="reference external" href="https://qiskit-community.github.io/qiskit-optimization/tutorials/03_minimum_eigen_optimizer.html">here</a>.</p>
 <p>In our encoding process, we employ a <span class="math notranslate nohighlight">\((3,1,p)-\)</span>QRAC, where each qubit can accommodate a maximum of 3 classical binary variables. The parameter <span class="math notranslate nohighlight">\(p\)</span> represents the bit recovery probability achieved through measurement. Depending on the nature of the problem, some qubits may have fewer than 3 classical variables assigned to them. To evaluate the compression achieved, we can examine the <code class="docutils literal notranslate"><span class="pre">compression_ratio</span></code> attribute of the encoding, which provides the ratio between the number of
 original binary variables and the number of qubits used (at best, a factor of 3).</p>
 <div class="nbinput docutils container">
@@ -617,7 +617,7 @@ <h2>Solve the problem using the <code class="docutils literal notranslate"><span
 <div class="highlight"><pre>
 The objective function value: 4.0
 x: [1 0 0 0 1 0]
-relaxed function value: 8.999999981905706
+relaxed function value: 8.999999940760864
 
 </pre></div></div>
 </div>
@@ -670,7 +670,7 @@ <h3>Inspect the results of subroutines<a class="headerlink" href="#Inspect-the-r
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-&lt;qiskit_algorithms.minimum_eigensolvers.vqe.VQEResult at 0x7ff4ccb3c190&gt;
+&lt;qiskit_algorithms.minimum_eigensolvers.vqe.VQEResult at 0x7f4d797f9670&gt;
 </pre></div></div>
 </div>
 <p>The result of the rounding scheme is also worth considering. In this example, we used the <code class="docutils literal notranslate"><span class="pre">SemideterministricRounding</span></code>. It’s important to note that with semi-deterministic rounding, a single sample is generated as the result, making it the optimal solution candidate.</p>
@@ -801,7 +801,7 @@ <h2>Solve the problem using the <code class="docutils literal notranslate"><span
 <div class="highlight"><pre>
 The objective function value: 9.0
 x: [1 0 1 0 0 1]
-relaxed function value: 8.99999793369987
+relaxed function value: 8.999995184895
 
 </pre></div></div>
 </div>
@@ -826,15 +826,15 @@ <h2>Solve the problem using the <code class="docutils literal notranslate"><span
 The number of distinct samples is 56.
 Top 10 samples with the largest fval:
 SolutionSample(x=array([1, 0, 1, 0, 0, 1]), fval=9.0, probability=0.0094, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([0, 1, 0, 1, 1, 0]), fval=9.0, probability=0.0111, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([0, 0, 0, 1, 1, 0]), fval=6.0, probability=0.0198, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([1, 1, 1, 0, 0, 1]), fval=6.0, probability=0.0209, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([0, 1, 1, 1, 1, 0]), fval=6.0, probability=0.0204, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([1, 0, 0, 0, 0, 1]), fval=6.0, probability=0.0213, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([1, 0, 1, 0, 0, 0]), fval=6.0, probability=0.0197, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([0, 1, 0, 1, 1, 1]), fval=6.0, probability=0.0208, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([1, 0, 1, 0, 1, 1]), fval=6.0, probability=0.0207, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
-SolutionSample(x=array([0, 1, 0, 1, 0, 0]), fval=6.0, probability=0.0217, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([0, 1, 0, 1, 1, 0]), fval=9.0, probability=0.011099999999999999, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([0, 0, 0, 1, 1, 0]), fval=6.0, probability=0.0212, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([1, 1, 1, 0, 0, 1]), fval=6.0, probability=0.0224, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([0, 1, 1, 1, 1, 0]), fval=6.0, probability=0.019, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([1, 0, 0, 0, 0, 1]), fval=6.0, probability=0.0202, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([1, 0, 1, 0, 0, 0]), fval=6.0, probability=0.0226, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([0, 1, 0, 1, 1, 1]), fval=6.0, probability=0.0238, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([1, 0, 1, 0, 1, 1]), fval=6.0, probability=0.0204, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
+SolutionSample(x=array([0, 1, 0, 1, 0, 0]), fval=6.0, probability=0.0214, status=&lt;OptimizationResultStatus.SUCCESS: 0&gt;)
 </pre></div></div>
 </div>
 </section>
@@ -848,7 +848,7 @@ <h2>Manually solve the relaxed problem.<a class="headerlink" href="#Manually-sol
 <p>Let’s start by invoking the <code class="docutils literal notranslate"><span class="pre">qrao.solve_relaxed()</span></code> method to directly solve the relaxed problem encoded by <code class="docutils literal notranslate"><span class="pre">QuantumRandomAccessEncoding</span></code>. This method allows us to focus solely on solving the relaxed problem without performing rounding.</p>
 <p>By invoking <code class="docutils literal notranslate"><span class="pre">qrao.solve_relaxed()</span></code>, we obtain two essential outputs:</p>
 <ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">MinimumEigensolverResult</span></code>: This object contains the results of running the minimum eigen optimizer such as the VQE on the relaxed problem. It provides information about the eigenvalue, and other relevant details. You can refer to the Qiskit Algorithms <a class="reference external" href="https://qiskit.org/documentation/stubs/qiskit.algorithms.MinimumEigensolverResult.html">documentation</a> for a comprehensive explanation of the entries within this object.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">MinimumEigensolverResult</span></code>: This object contains the results of running the minimum eigen optimizer such as the VQE on the relaxed problem. It provides information about the eigenvalue, and other relevant details. You can refer to the Qiskit Algorithms <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.MinimumEigensolverResult">documentation</a> for a comprehensive explanation of the entries within this object.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">RoundingContext</span></code>: This object encapsulates essential information about the encoding and the solution of the relaxed problem in a form that is ready for consumption by the rounding schemes.</p></li>
 </ul>
 <div class="nbinput nblast docutils container">
@@ -879,10 +879,10 @@ <h2>Manually solve the relaxed problem.<a class="headerlink" href="#Manually-sol
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-aux_operators_evaluated: [(0.010926743078980372, {&#39;variance&#39;: 0.9999999999974761, &#39;shots&#39;: 1000}), (0.02598343694328881, {&#39;variance&#39;: 0.999999999997472, &#39;shots&#39;: 1000}), (0.01044933784106082, {&#39;variance&#39;: 0.9999999999999999, &#39;shots&#39;: 1000}), (-0.04120945001189341, {&#39;variance&#39;: 0.9999999999999999, &#39;shots&#39;: 1000}), (0.02855644212815068, {&#39;variance&#39;: 0.9999999946088998, &#39;shots&#39;: 1000}), (0.014189027484473498, {&#39;variance&#39;: 0.9999999946088955, &#39;shots&#39;: 1000})]
-combine: &lt;bound method AlgorithmResult.combine of &lt;qiskit_algorithms.minimum_eigensolvers.vqe.VQEResult object at 0x7ff4a1f3ebe0&gt;&gt;
-cost_function_evals: 116
-eigenvalue: -4.499996924994738
+aux_operators_evaluated: [(0.01077701410810778, {&#39;variance&#39;: 0.9999999771030237, &#39;shots&#39;: 1000}), (0.026133164198470896, {&#39;variance&#39;: 0.9999999771030168, &#39;shots&#39;: 1000}), (0.010449337841060821, {&#39;variance&#39;: 1.0000000000000002, &#39;shots&#39;: 1000}), (-0.04120945001189342, {&#39;variance&#39;: 1.0000000000000002, &#39;shots&#39;: 1000}), (0.028630004855762115, {&#39;variance&#39;: 0.999999999999981, &#39;shots&#39;: 1000}), (0.014115442055980103, {&#39;variance&#39;: 0.9999999999999742, &#39;shots&#39;: 1000})]
+combine: &lt;bound method AlgorithmResult.combine of &lt;qiskit_algorithms.minimum_eigensolvers.vqe.VQEResult object at 0x7f4da9eed1c0&gt;&gt;
+cost_function_evals: 150
+eigenvalue: -4.499991361691686
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -895,19 +895,19 @@ <h2>Manually solve the relaxed problem.<a class="headerlink" href="#Manually-sol
      │  RealAmplitudes(θ[0],θ[1],θ[2],θ[3],θ[4],θ[5],θ[6],θ[7]) │
 q_1: ┤1                                                         ├
      └──────────────────────────────────────────────────────────┘
-optimal_parameters: {ParameterVectorElement(θ[0]): 2.0471560643273947, ParameterVectorElement(θ[1]): 1.4129824997448401, ParameterVectorElement(θ[2]): -0.7765382357072699, ParameterVectorElement(θ[3]): 1.9443520433144783, ParameterVectorElement(θ[4]): 2.5720037072214055, ParameterVectorElement(θ[5]): -4.069849316982238, ParameterVectorElement(θ[6]): -1.9345584128886406, ParameterVectorElement(θ[7]): 0.1995660862499935}
-optimal_point: [ 2.04715606  1.4129825  -0.77653824  1.94435204  2.57200371 -4.06984932
- -1.93455841  0.19956609]
-optimal_value: -4.499996924994738
+optimal_parameters: {ParameterVectorElement(θ[0]): 1.931845269903189, ParameterVectorElement(θ[1]): -0.3381899861254701, ParameterVectorElement(θ[2]): 0.9789155650474981, ParameterVectorElement(θ[3]): -0.8716894716741508, ParameterVectorElement(θ[4]): -0.29312237633987, ParameterVectorElement(θ[5]): 0.19902685121557584, ParameterVectorElement(θ[6]): -2.5519558808017244, ParameterVectorElement(θ[7]): 2.3715141402586086}
+optimal_point: [ 1.93184527 -0.33818999  0.97891557 -0.87168947 -0.29312238  0.19902685
+ -2.55195588  2.37151414]
+optimal_value: -4.499991361691686
 optimizer_evals: None
-optimizer_result: {   &#39;fun&#39;: -4.499996924994738,
+optimizer_result: {   &#39;fun&#39;: -4.499991361691686,
     &#39;jac&#39;: None,
-    &#39;nfev&#39;: 116,
+    &#39;nfev&#39;: 150,
     &#39;nit&#39;: None,
     &#39;njev&#39;: None,
-    &#39;x&#39;: array([ 2.04715606,  1.4129825 , -0.77653824,  1.94435204,  2.57200371,
-       -4.06984932, -1.93455841,  0.19956609])}
-optimizer_time: 0.3176090717315674
+    &#39;x&#39;: array([ 1.93184527, -0.33818999,  0.97891557, -0.87168947, -0.29312238,
+        0.19902685, -2.55195588,  2.37151414])}
+optimizer_time: 0.40552330017089844
 </pre></div></div>
 </div>
 </section>
@@ -942,7 +942,7 @@ <h2>Manually perform rounding on the relaxed problem results<a class="headerlink
 <div class="highlight"><pre>
 The objective function value: 3.0
 x: [0 0 0 1 0 0]
-relaxed function value: -8.999996924994738
+relaxed function value: -8.999991361691686
 The number of distinct samples is 1.
 </pre></div></div>
 </div>
@@ -972,7 +972,7 @@ <h2>Manually perform rounding on the relaxed problem results<a class="headerlink
 <div class="highlight"><pre>
 The objective function value: 9.0
 x: [1 0 1 0 0 1]
-relaxed function value: -8.999996924994738
+relaxed function value: -8.999991361691686
 The number of distinct samples is 56.
 </pre></div></div>
 </div>
@@ -1018,13 +1018,6 @@ <h3>How to verify correctness of your encoding<a class="headerlink" href="#How-t
 
   Binary variables (6)
     x_0 x_1 x_2 x_3 x_4 x_5
-</pre></div></div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
 
 </pre></div></div>
 </div>
@@ -1032,7 +1025,7 @@ <h3>How to verify correctness of your encoding<a class="headerlink" href="#How-t
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/tutorials_12_quantum_random_access_optimizer_35_2.png" src="../_images/tutorials_12_quantum_random_access_optimizer_35_2.png" />
+<img alt="../_images/tutorials_12_quantum_random_access_optimizer_35_1.png" src="../_images/tutorials_12_quantum_random_access_optimizer_35_1.png" />
 </div>
 </div>
 <p>As before, we <code class="docutils literal notranslate"><span class="pre">encode()</span></code> the problem using the QuantumRandomAccessEncoding class:</p>
@@ -1101,7 +1094,7 @@ <h3>How to verify correctness of your encoding<a class="headerlink" href="#How-t
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-/tmp/ipykernel_20758/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
+/tmp/ipykernel_20805/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0
   import qiskit.tools.jupyter
 </pre></div></div>
 </div>
@@ -1109,7 +1102,7 @@ <h3>How to verify correctness of your encoding<a class="headerlink" href="#How-t
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area rendered_html docutils container">
-<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Fri Feb 09 16:54:34 2024 UTC</td></tr></table></div>
+<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:09:28 2024 UTC</td></tr></table></div>
 </div>
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@@ -1126,7 +1119,7 @@ <h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr
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diff --git a/tutorials/12_quantum_random_access_optimizer.ipynb b/tutorials/12_quantum_random_access_optimizer.ipynb
index c94a69ee..f007898e 100644
--- a/tutorials/12_quantum_random_access_optimizer.ipynb
+++ b/tutorials/12_quantum_random_access_optimizer.ipynb
@@ -31,10 +31,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:30.767557Z",
-     "iopub.status.busy": "2024-02-09T16:54:30.767349Z",
-     "iopub.status.idle": "2024-02-09T16:54:31.684841Z",
-     "shell.execute_reply": "2024-02-09T16:54:31.684080Z"
+     "iopub.execute_input": "2024-02-14T16:09:24.960803Z",
+     "iopub.status.busy": "2024-02-14T16:09:24.960604Z",
+     "iopub.status.idle": "2024-02-14T16:09:25.912664Z",
+     "shell.execute_reply": "2024-02-14T16:09:25.911988Z"
     }
    },
    "outputs": [],
@@ -58,7 +58,7 @@
     "To begin, we utilize the `Maxcut` class from Qiskit Optimization's application module. It allows us to generate a `QuadraticProgram` representation of the given graph.\n",
     "\n",
     "Note that once our problem has been represented as a `QuadraticProgram`, it will need to be converted to the correct type, a [quadratic unconstrained binary optimization (QUBO)](https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization) problem, so that it is compatible with QRAO.\n",
-    "A `QuadraticProgram` generated by `Maxcut` is already a QUBO, but if you define your own problem be sure you convert it to a QUBO before proceeding. Here is [a tutorial](https://qiskit.org/documentation/optimization/tutorials/02_converters_for_quadratic_programs.html) on converting `QuadraticPrograms`."
+    "A `QuadraticProgram` generated by `Maxcut` is already a QUBO, but if you define your own problem be sure you convert it to a QUBO before proceeding. Here is [a tutorial](https://qiskit-community.github.io/qiskit-optimization/tutorials/02_converters_for_quadratic_programs.html) on converting `QuadraticPrograms`."
    ]
   },
   {
@@ -66,10 +66,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:31.687998Z",
-     "iopub.status.busy": "2024-02-09T16:54:31.687628Z",
-     "iopub.status.idle": "2024-02-09T16:54:32.126702Z",
-     "shell.execute_reply": "2024-02-09T16:54:32.125950Z"
+     "iopub.execute_input": "2024-02-14T16:09:25.915894Z",
+     "iopub.status.busy": "2024-02-14T16:09:25.915357Z",
+     "iopub.status.idle": "2024-02-14T16:09:26.370611Z",
+     "shell.execute_reply": "2024-02-14T16:09:26.369817Z"
     }
    },
    "outputs": [
@@ -127,7 +127,7 @@
     "\n",
     "Once we have appropriately configured our problem, we proceed to encode it using the `QuantumRandomAccessEncoding` class from the `qrao` module. This encoding step allows us to generate a quantum Hamiltonian operator that represents our problem. In particular, we employ a Quantum Random Access Code (QRAC) to encode multiple classical binary variables (corresponding to the nodes of our max-cut graph) into each qubit.\n",
     "\n",
-    "It's important to note that the resulting \"relaxed\" Hamiltonian, produced by this encoding, will not be diagonal. This differs from the standard workflow in `qiskit-optimization`, which typically generates a diagonal (Ising) Hamiltonian suitable for optimization using a `MinimumEigenOptimizer`. You can find a tutorial on the `MinimumEigenOptimizer` [here](https://qiskit.org/documentation/optimization/tutorials/03_minimum_eigen_optimizer.html).\n",
+    "It's important to note that the resulting \"relaxed\" Hamiltonian, produced by this encoding, will not be diagonal. This differs from the standard workflow in `qiskit-optimization`, which typically generates a diagonal (Ising) Hamiltonian suitable for optimization using a `MinimumEigenOptimizer`. You can find a tutorial on the `MinimumEigenOptimizer` [here](https://qiskit-community.github.io/qiskit-optimization/tutorials/03_minimum_eigen_optimizer.html).\n",
     "\n",
     "In our encoding process, we employ a $(3,1,p)-$QRAC, where each qubit can accommodate a maximum of 3 classical binary variables. The parameter $p$ represents the bit recovery probability achieved through measurement. Depending on the nature of the problem, some qubits may have fewer than 3 classical variables assigned to them. To evaluate the compression achieved, we can examine the `compression_ratio` attribute of the encoding, which provides the ratio between the number of original binary variables and the number of qubits used (at best, a factor of 3)."
    ]
@@ -137,10 +137,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:32.129662Z",
-     "iopub.status.busy": "2024-02-09T16:54:32.129130Z",
-     "iopub.status.idle": "2024-02-09T16:54:32.137700Z",
-     "shell.execute_reply": "2024-02-09T16:54:32.137129Z"
+     "iopub.execute_input": "2024-02-14T16:09:26.373706Z",
+     "iopub.status.busy": "2024-02-14T16:09:26.373134Z",
+     "iopub.status.idle": "2024-02-14T16:09:26.382341Z",
+     "shell.execute_reply": "2024-02-14T16:09:26.381765Z"
     }
    },
    "outputs": [
@@ -197,10 +197,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:32.140199Z",
-     "iopub.status.busy": "2024-02-09T16:54:32.139789Z",
-     "iopub.status.idle": "2024-02-09T16:54:32.145465Z",
-     "shell.execute_reply": "2024-02-09T16:54:32.144875Z"
+     "iopub.execute_input": "2024-02-14T16:09:26.384991Z",
+     "iopub.status.busy": "2024-02-14T16:09:26.384606Z",
+     "iopub.status.idle": "2024-02-14T16:09:26.390231Z",
+     "shell.execute_reply": "2024-02-14T16:09:26.389596Z"
     }
    },
    "outputs": [],
@@ -251,10 +251,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:32.147931Z",
-     "iopub.status.busy": "2024-02-09T16:54:32.147565Z",
-     "iopub.status.idle": "2024-02-09T16:54:32.520406Z",
-     "shell.execute_reply": "2024-02-09T16:54:32.519658Z"
+     "iopub.execute_input": "2024-02-14T16:09:26.392804Z",
+     "iopub.status.busy": "2024-02-14T16:09:26.392417Z",
+     "iopub.status.idle": "2024-02-14T16:09:26.842679Z",
+     "shell.execute_reply": "2024-02-14T16:09:26.841992Z"
     }
    },
    "outputs": [
@@ -264,7 +264,7 @@
      "text": [
       "The objective function value: 4.0\n",
       "x: [1 0 0 0 1 0]\n",
-      "relaxed function value: 8.999999981905706\n",
+      "relaxed function value: 8.999999940760864\n",
       "\n"
      ]
     }
@@ -299,10 +299,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:32.523203Z",
-     "iopub.status.busy": "2024-02-09T16:54:32.522792Z",
-     "iopub.status.idle": "2024-02-09T16:54:32.628250Z",
-     "shell.execute_reply": "2024-02-09T16:54:32.627479Z"
+     "iopub.execute_input": "2024-02-14T16:09:26.845515Z",
+     "iopub.status.busy": "2024-02-14T16:09:26.845110Z",
+     "iopub.status.idle": "2024-02-14T16:09:26.981007Z",
+     "shell.execute_reply": "2024-02-14T16:09:26.980286Z"
     }
    },
    "outputs": [
@@ -353,17 +353,17 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:32.631162Z",
-     "iopub.status.busy": "2024-02-09T16:54:32.630724Z",
-     "iopub.status.idle": "2024-02-09T16:54:32.635645Z",
-     "shell.execute_reply": "2024-02-09T16:54:32.634849Z"
+     "iopub.execute_input": "2024-02-14T16:09:26.983957Z",
+     "iopub.status.busy": "2024-02-14T16:09:26.983464Z",
+     "iopub.status.idle": "2024-02-14T16:09:26.988070Z",
+     "shell.execute_reply": "2024-02-14T16:09:26.987411Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<qiskit_algorithms.minimum_eigensolvers.vqe.VQEResult at 0x7ff4ccb3c190>"
+       "<qiskit_algorithms.minimum_eigensolvers.vqe.VQEResult at 0x7f4d797f9670>"
       ]
      },
      "execution_count": 7,
@@ -390,10 +390,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:32.638149Z",
-     "iopub.status.busy": "2024-02-09T16:54:32.637940Z",
-     "iopub.status.idle": "2024-02-09T16:54:32.642665Z",
-     "shell.execute_reply": "2024-02-09T16:54:32.641957Z"
+     "iopub.execute_input": "2024-02-14T16:09:26.990605Z",
+     "iopub.status.busy": "2024-02-14T16:09:26.990228Z",
+     "iopub.status.idle": "2024-02-14T16:09:26.994521Z",
+     "shell.execute_reply": "2024-02-14T16:09:26.993862Z"
     }
    },
    "outputs": [
@@ -427,10 +427,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:32.645349Z",
-     "iopub.status.busy": "2024-02-09T16:54:32.644936Z",
-     "iopub.status.idle": "2024-02-09T16:54:32.668944Z",
-     "shell.execute_reply": "2024-02-09T16:54:32.668352Z"
+     "iopub.execute_input": "2024-02-14T16:09:26.997277Z",
+     "iopub.status.busy": "2024-02-14T16:09:26.996745Z",
+     "iopub.status.idle": "2024-02-14T16:09:27.020138Z",
+     "shell.execute_reply": "2024-02-14T16:09:27.019480Z"
     }
    },
    "outputs": [
@@ -468,10 +468,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:32.671345Z",
-     "iopub.status.busy": "2024-02-09T16:54:32.670953Z",
-     "iopub.status.idle": "2024-02-09T16:54:32.674593Z",
-     "shell.execute_reply": "2024-02-09T16:54:32.673923Z"
+     "iopub.execute_input": "2024-02-14T16:09:27.022839Z",
+     "iopub.status.busy": "2024-02-14T16:09:27.022371Z",
+     "iopub.status.idle": "2024-02-14T16:09:27.026163Z",
+     "shell.execute_reply": "2024-02-14T16:09:27.025494Z"
     }
    },
    "outputs": [
@@ -512,10 +512,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:32.677177Z",
-     "iopub.status.busy": "2024-02-09T16:54:32.676781Z",
-     "iopub.status.idle": "2024-02-09T16:54:33.161187Z",
-     "shell.execute_reply": "2024-02-09T16:54:33.160425Z"
+     "iopub.execute_input": "2024-02-14T16:09:27.028677Z",
+     "iopub.status.busy": "2024-02-14T16:09:27.028300Z",
+     "iopub.status.idle": "2024-02-14T16:09:27.521815Z",
+     "shell.execute_reply": "2024-02-14T16:09:27.521190Z"
     }
    },
    "outputs": [],
@@ -551,10 +551,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:33.164486Z",
-     "iopub.status.busy": "2024-02-09T16:54:33.164050Z",
-     "iopub.status.idle": "2024-02-09T16:54:33.167833Z",
-     "shell.execute_reply": "2024-02-09T16:54:33.167199Z"
+     "iopub.execute_input": "2024-02-14T16:09:27.524891Z",
+     "iopub.status.busy": "2024-02-14T16:09:27.524467Z",
+     "iopub.status.idle": "2024-02-14T16:09:27.528523Z",
+     "shell.execute_reply": "2024-02-14T16:09:27.527820Z"
     }
    },
    "outputs": [
@@ -564,7 +564,7 @@
      "text": [
       "The objective function value: 9.0\n",
       "x: [1 0 1 0 0 1]\n",
-      "relaxed function value: 8.99999793369987\n",
+      "relaxed function value: 8.999995184895\n",
       "\n"
      ]
     }
@@ -592,10 +592,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:33.170147Z",
-     "iopub.status.busy": "2024-02-09T16:54:33.169942Z",
-     "iopub.status.idle": "2024-02-09T16:54:33.174295Z",
-     "shell.execute_reply": "2024-02-09T16:54:33.173627Z"
+     "iopub.execute_input": "2024-02-14T16:09:27.530871Z",
+     "iopub.status.busy": "2024-02-14T16:09:27.530661Z",
+     "iopub.status.idle": "2024-02-14T16:09:27.534983Z",
+     "shell.execute_reply": "2024-02-14T16:09:27.534295Z"
     }
    },
    "outputs": [
@@ -606,15 +606,15 @@
       "The number of distinct samples is 56.\n",
       "Top 10 samples with the largest fval:\n",
       "SolutionSample(x=array([1, 0, 1, 0, 0, 1]), fval=9.0, probability=0.0094, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0, 1, 0, 1, 1, 0]), fval=9.0, probability=0.0111, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0, 0, 0, 1, 1, 0]), fval=6.0, probability=0.0198, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1, 1, 1, 0, 0, 1]), fval=6.0, probability=0.0209, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0, 1, 1, 1, 1, 0]), fval=6.0, probability=0.0204, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1, 0, 0, 0, 0, 1]), fval=6.0, probability=0.0213, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1, 0, 1, 0, 0, 0]), fval=6.0, probability=0.0197, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0, 1, 0, 1, 1, 1]), fval=6.0, probability=0.0208, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([1, 0, 1, 0, 1, 1]), fval=6.0, probability=0.0207, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
-      "SolutionSample(x=array([0, 1, 0, 1, 0, 0]), fval=6.0, probability=0.0217, status=<OptimizationResultStatus.SUCCESS: 0>)\n"
+      "SolutionSample(x=array([0, 1, 0, 1, 1, 0]), fval=9.0, probability=0.011099999999999999, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0, 0, 0, 1, 1, 0]), fval=6.0, probability=0.0212, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1, 1, 1, 0, 0, 1]), fval=6.0, probability=0.0224, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0, 1, 1, 1, 1, 0]), fval=6.0, probability=0.019, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1, 0, 0, 0, 0, 1]), fval=6.0, probability=0.0202, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1, 0, 1, 0, 0, 0]), fval=6.0, probability=0.0226, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0, 1, 0, 1, 1, 1]), fval=6.0, probability=0.0238, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([1, 0, 1, 0, 1, 1]), fval=6.0, probability=0.0204, status=<OptimizationResultStatus.SUCCESS: 0>)\n",
+      "SolutionSample(x=array([0, 1, 0, 1, 0, 0]), fval=6.0, probability=0.0214, status=<OptimizationResultStatus.SUCCESS: 0>)\n"
      ]
     }
    ],
@@ -643,7 +643,7 @@
     "\n",
     "By invoking `qrao.solve_relaxed()`, we obtain two essential outputs:\n",
     "\n",
-    "- `MinimumEigensolverResult`: This object contains the results of running the minimum eigen optimizer such as the VQE on the relaxed problem. It provides information about the eigenvalue, and other relevant details. You can refer to the Qiskit Algorithms [documentation](https://qiskit.org/documentation/stubs/qiskit.algorithms.MinimumEigensolverResult.html) for a comprehensive explanation of the entries within this object.\n",
+    "- `MinimumEigensolverResult`: This object contains the results of running the minimum eigen optimizer such as the VQE on the relaxed problem. It provides information about the eigenvalue, and other relevant details. You can refer to the Qiskit Algorithms [documentation](https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.MinimumEigensolverResult) for a comprehensive explanation of the entries within this object.\n",
     "- `RoundingContext`: This object encapsulates essential information about the encoding and the solution of the relaxed problem in a form that is ready for consumption by the rounding schemes."
    ]
   },
@@ -652,10 +652,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:33.176817Z",
-     "iopub.status.busy": "2024-02-09T16:54:33.176439Z",
-     "iopub.status.idle": "2024-02-09T16:54:33.515085Z",
-     "shell.execute_reply": "2024-02-09T16:54:33.514479Z"
+     "iopub.execute_input": "2024-02-14T16:09:27.537504Z",
+     "iopub.status.busy": "2024-02-14T16:09:27.537111Z",
+     "iopub.status.idle": "2024-02-14T16:09:27.962569Z",
+     "shell.execute_reply": "2024-02-14T16:09:27.961920Z"
     }
    },
    "outputs": [],
@@ -673,10 +673,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:33.518216Z",
-     "iopub.status.busy": "2024-02-09T16:54:33.517656Z",
-     "iopub.status.idle": "2024-02-09T16:54:33.736791Z",
-     "shell.execute_reply": "2024-02-09T16:54:33.736121Z"
+     "iopub.execute_input": "2024-02-14T16:09:27.965526Z",
+     "iopub.status.busy": "2024-02-14T16:09:27.965306Z",
+     "iopub.status.idle": "2024-02-14T16:09:28.188216Z",
+     "shell.execute_reply": "2024-02-14T16:09:28.187495Z"
     }
    },
    "outputs": [
@@ -684,10 +684,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "aux_operators_evaluated: [(0.010926743078980372, {'variance': 0.9999999999974761, 'shots': 1000}), (0.02598343694328881, {'variance': 0.999999999997472, 'shots': 1000}), (0.01044933784106082, {'variance': 0.9999999999999999, 'shots': 1000}), (-0.04120945001189341, {'variance': 0.9999999999999999, 'shots': 1000}), (0.02855644212815068, {'variance': 0.9999999946088998, 'shots': 1000}), (0.014189027484473498, {'variance': 0.9999999946088955, 'shots': 1000})]\n",
-      "combine: <bound method AlgorithmResult.combine of <qiskit_algorithms.minimum_eigensolvers.vqe.VQEResult object at 0x7ff4a1f3ebe0>>\n",
-      "cost_function_evals: 116\n",
-      "eigenvalue: -4.499996924994738\n"
+      "aux_operators_evaluated: [(0.01077701410810778, {'variance': 0.9999999771030237, 'shots': 1000}), (0.026133164198470896, {'variance': 0.9999999771030168, 'shots': 1000}), (0.010449337841060821, {'variance': 1.0000000000000002, 'shots': 1000}), (-0.04120945001189342, {'variance': 1.0000000000000002, 'shots': 1000}), (0.028630004855762115, {'variance': 0.999999999999981, 'shots': 1000}), (0.014115442055980103, {'variance': 0.9999999999999742, 'shots': 1000})]\n",
+      "combine: <bound method AlgorithmResult.combine of <qiskit_algorithms.minimum_eigensolvers.vqe.VQEResult object at 0x7f4da9eed1c0>>\n",
+      "cost_function_evals: 150\n",
+      "eigenvalue: -4.499991361691686\n"
      ]
     },
     {
@@ -699,19 +699,19 @@
       "     │  RealAmplitudes(θ[0],θ[1],θ[2],θ[3],θ[4],θ[5],θ[6],θ[7]) │\n",
       "q_1: ┤1                                                         ├\n",
       "     └──────────────────────────────────────────────────────────┘\n",
-      "optimal_parameters: {ParameterVectorElement(θ[0]): 2.0471560643273947, ParameterVectorElement(θ[1]): 1.4129824997448401, ParameterVectorElement(θ[2]): -0.7765382357072699, ParameterVectorElement(θ[3]): 1.9443520433144783, ParameterVectorElement(θ[4]): 2.5720037072214055, ParameterVectorElement(θ[5]): -4.069849316982238, ParameterVectorElement(θ[6]): -1.9345584128886406, ParameterVectorElement(θ[7]): 0.1995660862499935}\n",
-      "optimal_point: [ 2.04715606  1.4129825  -0.77653824  1.94435204  2.57200371 -4.06984932\n",
-      " -1.93455841  0.19956609]\n",
-      "optimal_value: -4.499996924994738\n",
+      "optimal_parameters: {ParameterVectorElement(θ[0]): 1.931845269903189, ParameterVectorElement(θ[1]): -0.3381899861254701, ParameterVectorElement(θ[2]): 0.9789155650474981, ParameterVectorElement(θ[3]): -0.8716894716741508, ParameterVectorElement(θ[4]): -0.29312237633987, ParameterVectorElement(θ[5]): 0.19902685121557584, ParameterVectorElement(θ[6]): -2.5519558808017244, ParameterVectorElement(θ[7]): 2.3715141402586086}\n",
+      "optimal_point: [ 1.93184527 -0.33818999  0.97891557 -0.87168947 -0.29312238  0.19902685\n",
+      " -2.55195588  2.37151414]\n",
+      "optimal_value: -4.499991361691686\n",
       "optimizer_evals: None\n",
-      "optimizer_result: {   'fun': -4.499996924994738,\n",
+      "optimizer_result: {   'fun': -4.499991361691686,\n",
       "    'jac': None,\n",
-      "    'nfev': 116,\n",
+      "    'nfev': 150,\n",
       "    'nit': None,\n",
       "    'njev': None,\n",
-      "    'x': array([ 2.04715606,  1.4129825 , -0.77653824,  1.94435204,  2.57200371,\n",
-      "       -4.06984932, -1.93455841,  0.19956609])}\n",
-      "optimizer_time: 0.3176090717315674\n"
+      "    'x': array([ 1.93184527, -0.33818999,  0.97891557, -0.87168947, -0.29312238,\n",
+      "        0.19902685, -2.55195588,  2.37151414])}\n",
+      "optimizer_time: 0.40552330017089844\n"
      ]
     }
    ],
@@ -738,10 +738,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:33.739816Z",
-     "iopub.status.busy": "2024-02-09T16:54:33.739169Z",
-     "iopub.status.idle": "2024-02-09T16:54:33.746591Z",
-     "shell.execute_reply": "2024-02-09T16:54:33.745941Z"
+     "iopub.execute_input": "2024-02-14T16:09:28.191224Z",
+     "iopub.status.busy": "2024-02-14T16:09:28.190692Z",
+     "iopub.status.idle": "2024-02-14T16:09:28.198071Z",
+     "shell.execute_reply": "2024-02-14T16:09:28.197405Z"
     }
    },
    "outputs": [
@@ -751,7 +751,7 @@
      "text": [
       "The objective function value: 3.0\n",
       "x: [0 0 0 1 0 0]\n",
-      "relaxed function value: -8.999996924994738\n",
+      "relaxed function value: -8.999991361691686\n",
       "The number of distinct samples is 1.\n"
      ]
     }
@@ -777,10 +777,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:33.749008Z",
-     "iopub.status.busy": "2024-02-09T16:54:33.748623Z",
-     "iopub.status.idle": "2024-02-09T16:54:33.887406Z",
-     "shell.execute_reply": "2024-02-09T16:54:33.886829Z"
+     "iopub.execute_input": "2024-02-14T16:09:28.200665Z",
+     "iopub.status.busy": "2024-02-14T16:09:28.200300Z",
+     "iopub.status.idle": "2024-02-14T16:09:28.341385Z",
+     "shell.execute_reply": "2024-02-14T16:09:28.340756Z"
     }
    },
    "outputs": [
@@ -790,7 +790,7 @@
      "text": [
       "The objective function value: 9.0\n",
       "x: [1 0 1 0 0 1]\n",
-      "relaxed function value: -8.999996924994738\n",
+      "relaxed function value: -8.999991361691686\n",
       "The number of distinct samples is 56.\n"
      ]
     }
@@ -827,10 +827,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:33.889712Z",
-     "iopub.status.busy": "2024-02-09T16:54:33.889507Z",
-     "iopub.status.idle": "2024-02-09T16:54:34.057767Z",
-     "shell.execute_reply": "2024-02-09T16:54:34.057108Z"
+     "iopub.execute_input": "2024-02-14T16:09:28.343916Z",
+     "iopub.status.busy": "2024-02-14T16:09:28.343701Z",
+     "iopub.status.idle": "2024-02-14T16:09:28.516969Z",
+     "shell.execute_reply": "2024-02-14T16:09:28.516153Z"
     }
    },
    "outputs": [
@@ -849,13 +849,7 @@
       "  No constraints\n",
       "\n",
       "  Binary variables (6)\n",
-      "    x_0 x_1 x_2 x_3 x_4 x_5\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    x_0 x_1 x_2 x_3 x_4 x_5\n",
       "\n"
      ]
     },
@@ -896,10 +890,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:34.060421Z",
-     "iopub.status.busy": "2024-02-09T16:54:34.060168Z",
-     "iopub.status.idle": "2024-02-09T16:54:34.069370Z",
-     "shell.execute_reply": "2024-02-09T16:54:34.068726Z"
+     "iopub.execute_input": "2024-02-14T16:09:28.520050Z",
+     "iopub.status.busy": "2024-02-14T16:09:28.519796Z",
+     "iopub.status.idle": "2024-02-14T16:09:28.528806Z",
+     "shell.execute_reply": "2024-02-14T16:09:28.528217Z"
     }
    },
    "outputs": [
@@ -940,10 +934,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:34.071808Z",
-     "iopub.status.busy": "2024-02-09T16:54:34.071399Z",
-     "iopub.status.idle": "2024-02-09T16:54:34.215758Z",
-     "shell.execute_reply": "2024-02-09T16:54:34.215228Z"
+     "iopub.execute_input": "2024-02-14T16:09:28.531334Z",
+     "iopub.status.busy": "2024-02-14T16:09:28.531117Z",
+     "iopub.status.idle": "2024-02-14T16:09:28.677289Z",
+     "shell.execute_reply": "2024-02-14T16:09:28.676664Z"
     }
    },
    "outputs": [],
@@ -975,10 +969,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-02-09T16:54:34.218216Z",
-     "iopub.status.busy": "2024-02-09T16:54:34.217963Z",
-     "iopub.status.idle": "2024-02-09T16:54:34.396931Z",
-     "shell.execute_reply": "2024-02-09T16:54:34.396100Z"
+     "iopub.execute_input": "2024-02-14T16:09:28.680269Z",
+     "iopub.status.busy": "2024-02-14T16:09:28.680055Z",
+     "iopub.status.idle": "2024-02-14T16:09:28.875526Z",
+     "shell.execute_reply": "2024-02-14T16:09:28.874932Z"
     },
     "scrolled": false
    },
@@ -987,14 +981,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_20758/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
+      "/tmp/ipykernel_20805/1492642725.py:1: DeprecationWarning: qiskit.tools.jupyter is deprecated and will be removed in Qiskit 1.0.0\n",
       "  import qiskit.tools.jupyter\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Fri Feb 09 16:54:34 2024 UTC</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.46.0</td></tr><tr><td><code>qiskit_optimization</code></td><td>0.6.0</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.2.2</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.8.18</td></tr><tr><td>Python compiler</td><td>GCC 11.4.0</td></tr><tr><td>Python build</td><td>default, Aug 28 2023 08:27:22</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>15.60690689086914</td></tr><tr><td colspan='2'>Wed Feb 14 16:09:28 2024 UTC</td></tr></table>"
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       "text/plain": [
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@@ -1055,25 +1049,7 @@
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
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-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "HTMLStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
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-       "font_size": null,
-       "text_color": null
-      }
-     },
-     "534c07035b4447c6a6cef590a68fd765": {
+     "21e88dfb559f438c91fdd2982b14053d": {
       "model_module": "@jupyter-widgets/base",
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@@ -1126,7 +1102,25 @@
        "width": null
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-     "7543f23d85494b4ca095bc91aee0fc6b": {
+     "6441b2bd41a04a6991e4ea7400902a13": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
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+     "d7d38cf0e515431d9dd45266939c2589": {
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       "model_module_version": "2.0.0",
       "model_name": "HTMLModel",
@@ -1141,9 +1135,9 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_allow_html": false,
-       "layout": "IPY_MODEL_534c07035b4447c6a6cef590a68fd765",
+       "layout": "IPY_MODEL_21e88dfb559f438c91fdd2982b14053d",
        "placeholder": "​",
-       "style": "IPY_MODEL_1023e565698e4ca4942a008255d9e76c",
+       "style": "IPY_MODEL_6441b2bd41a04a6991e4ea7400902a13",
        "tabbable": null,
        "tooltip": null,
        "value": "<p style='font-family: IBM Plex Sans, Arial, Helvetica, sans-serif; font-size: 20px; font-weight: medium;'>Circuit Properties</p>"