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‎.buildinfo

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# Sphinx build info version 1
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‎_sources/tutorials/01_quadratic_program.ipynb.txt

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}
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],
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"import qiskit.tools.jupyter\n",
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"import tutorial_magics\n",
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"\n",
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"%qiskit_version_table\n",
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‎_sources/tutorials/02_converters_for_quadratic_programs.ipynb.txt

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"import tutorial_magics\n",
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"\n",
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‎_sources/tutorials/03_minimum_eigen_optimizer.ipynb.txt

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"source": [
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"import qiskit.tools.jupyter\n",
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"import tutorial_magics\n",
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"\n",
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‎_sources/tutorials/04_grover_optimizer.ipynb.txt

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‎_sources/tutorials/05_admm_optimizer.ipynb.txt

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}
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"\n",
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‎_sources/tutorials/06_examples_max_cut_and_tsp.ipynb.txt

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"import numpy as np\n",
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"import networkx as nx\n",
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"\n",
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"from qiskit.tools.visualization import plot_histogram\n",
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"from qiskit.circuit.library import TwoLocal\n",
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"from qiskit_optimization.applications import Maxcut, Tsp\n",
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"from qiskit_algorithms import SamplingVQE, NumPyMinimumEigensolver\n",
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}
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"source": [
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"import qiskit.tools.jupyter\n",
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"metadata": {
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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‎_sources/tutorials/07_examples_vehicle_routing.ipynb.txt

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‎_sources/tutorials/08_cvar_optimization.ipynb.txt

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‎_sources/tutorials/09_application_classes.ipynb.txt

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‎_sources/tutorials/10_warm_start_qaoa.ipynb.txt

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"# Qiskit imports\n",
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"from qiskit_algorithms import QAOA, NumPyMinimumEigensolver\n",
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"from qiskit_algorithms.optimizers import COBYLA\n",
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"from qiskit_algorithms.utils import algorithm_globals\n",
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"from qiskit.primitives import Sampler\n",
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"from qiskit_optimization.algorithms import MinimumEigenOptimizer, CplexOptimizer\n",
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"from qiskit_optimization import QuadraticProgram\n",
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}
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‎_sources/tutorials/11_using_classical_optimization_solvers_and_models.ipynb.txt

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"import tutorial_magics\n",
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‎_sources/tutorials/12_quantum_random_access_optimizer.ipynb.txt

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‎apidocs/qiskit_optimization.html

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A uniform interface as well as automatic conversion between different problem representations
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allows users to solve problems using a large set of algorithms, from variational quantum algorithms,
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such as the Quantum Approximate Optimization Algorithm
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(<a class="reference external" href="https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.QAOA.html#qiskit_algorithms.QAOA" title="(in Qiskit Algorithms v0.2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">QAOA</span></code></a>), to
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(<a class="reference external" href="https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.QAOA.html#qiskit_algorithms.QAOA" title="(in Qiskit Algorithms v0.3.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">QAOA</span></code></a>), to
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<a class="reference external" href="https://arxiv.org/abs/quant-ph/9607014">Grover Adaptive Search</a>
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(<a class="reference internal" href="../stubs/qiskit_optimization.algorithms.GroverOptimizer.html#qiskit_optimization.algorithms.GroverOptimizer" title="qiskit_optimization.algorithms.GroverOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroverOptimizer</span></code></a>), leveraging
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fundamental <a class="reference external" href="https://qiskit-community.github.io/qiskit-algorithms/apidocs/qiskit_algorithms.html#minimum-eigensolvers">minimum eigensolvers</a>

‎release_notes.html

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</ul>
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<ul class="simple">
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<li><p>The <a class="reference internal" href="stubs/qiskit_optimization.algorithms.MinimumEigenOptimizer.html#qiskit_optimization.algorithms.MinimumEigenOptimizer" title="qiskit_optimization.algorithms.MinimumEigenOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">MinimumEigenOptimizer</span></code></a> class takes the primitives-based algorithms
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(<a class="reference external" href="https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.SamplingMinimumEigensolver.html#qiskit_algorithms.SamplingMinimumEigensolver" title="(in Qiskit Algorithms v0.2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit_algorithms.SamplingMinimumEigensolver</span></code></a> and
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<a class="reference external" href="https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.NumPyMinimumEigensolver.html#qiskit_algorithms.NumPyMinimumEigensolver" title="(in Qiskit Algorithms v0.2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit_algorithms.NumPyMinimumEigensolver</span></code></a>)
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(<a class="reference external" href="https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.SamplingMinimumEigensolver.html#qiskit_algorithms.SamplingMinimumEigensolver" title="(in Qiskit Algorithms v0.3.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit_algorithms.SamplingMinimumEigensolver</span></code></a> and
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<a class="reference external" href="https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.NumPyMinimumEigensolver.html#qiskit_algorithms.NumPyMinimumEigensolver" title="(in Qiskit Algorithms v0.3.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit_algorithms.NumPyMinimumEigensolver</span></code></a>)
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of <a class="reference external" href="https://qiskit.org/ecosystem/algorithms/">Qiskit Algorithms</a>
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as <code class="docutils literal notranslate"><span class="pre">min_eigen_solver</span></code> argument.
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<span id="id3"></span><h2>0.5.0<a class="headerlink" href="#release-notes-0-5-0" title="Permalink to this heading">#</a></h2>
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<section id="release-notes-0-5-0-prelude">
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<span id="id4"></span><h3>Prelude<a class="headerlink" href="#release-notes-0-5-0-prelude" title="Permalink to this heading">#</a></h3>
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<p>Qiskit Optimization 0.5 supports the new algorithms introduced in Qiskit Terra 0.22 which in turn rely on the <a class="reference external" href="https://qiskit.org/documentation/apidoc/primitives.html">Qiskit Primitives</a>. Qiskit Optimization 0.5 still supports the former algorithms based on <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.utils.QuantumInstance" title="(in Qiskit v0.46)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.utils.QuantumInstance</span></code></a>, but they will be deprecated and then removed, along with the support here, in future releases.</p>
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<p>Qiskit Optimization 0.5 supports the new algorithms introduced in Qiskit Terra 0.22 which in turn rely on the <a class="reference external" href="https://qiskit.org/documentation/apidoc/primitives.html">Qiskit Primitives</a>. Qiskit Optimization 0.5 still supports the former algorithms based on <code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.utils.QuantumInstance</span></code>, but they will be deprecated and then removed, along with the support here, in future releases.</p>
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</section>
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<section id="release-notes-0-5-0-new-features">
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<span id="id5"></span><h3>New Features<a class="headerlink" href="#release-notes-0-5-0-new-features" title="Permalink to this heading">#</a></h3>
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<ul class="simple">
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<li><p>The <a class="reference internal" href="stubs/qiskit_optimization.algorithms.MinimumEigenOptimizer.html#qiskit_optimization.algorithms.MinimumEigenOptimizer" title="qiskit_optimization.algorithms.MinimumEigenOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">MinimumEigenOptimizer</span></code></a> class takes the primitives-based algorithms
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(<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver" title="(in Qiskit v0.46)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver</span></code></a> and
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<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver" title="(in Qiskit v0.46)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver</span></code></a>)
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(<code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver</span></code> and
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<code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver</span></code>)
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as <code class="docutils literal notranslate"><span class="pre">min_eigen_solver</span></code> argument.
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The former algorithm <code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.MinimumEigensolver</span></code>
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is pending deprecation and will be deprecated and subsequently removed in future releases.
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<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.VQE" title="(in Qiskit v0.46)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.VQE</span></code></a> for <a class="reference internal" href="stubs/qiskit_optimization.algorithms.MinimumEigenOptimizer.html#qiskit_optimization.algorithms.MinimumEigenOptimizer" title="qiskit_optimization.algorithms.MinimumEigenOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">MinimumEigenOptimizer</span></code></a>.
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<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver" title="(in Qiskit v0.46)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver</span></code></a> also supersedes
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<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.NumPyMinimumEigensolver" title="(in Qiskit v0.46)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.NumPyMinimumEigensolver</span></code></a>.</p></li>
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Note that <code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.minimum_eigensolvers.SamplingVQE</span></code> supersedes
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<code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.VQE</span></code> for <a class="reference internal" href="stubs/qiskit_optimization.algorithms.MinimumEigenOptimizer.html#qiskit_optimization.algorithms.MinimumEigenOptimizer" title="qiskit_optimization.algorithms.MinimumEigenOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">MinimumEigenOptimizer</span></code></a>.
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<code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.minimum_eigensolvers.NumPyMinimumEigensolver</span></code> also supersedes
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<code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.NumPyMinimumEigensolver</span></code>.</p></li>
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</ul>
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<ul class="simple">
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<li><p>The <a class="reference internal" href="stubs/qiskit_optimization.algorithms.WarmStartQAOAOptimizer.html#qiskit_optimization.algorithms.WarmStartQAOAOptimizer" title="qiskit_optimization.algorithms.WarmStartQAOAOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">WarmStartQAOAOptimizer</span></code></a> class takes the primitives-based QAOA
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(<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.minimum_eigensolvers.QAOA" title="(in Qiskit v0.46)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.minimum_eigensolvers.QAOA</span></code></a>) as <code class="docutils literal notranslate"><span class="pre">qaoa</span></code> argument.
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The former algorithm <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.QAOA" title="(in Qiskit v0.46)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.QAOA</span></code></a>
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(<code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.minimum_eigensolvers.QAOA</span></code>) as <code class="docutils literal notranslate"><span class="pre">qaoa</span></code> argument.
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The former algorithm <code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.algorithms.QAOA</span></code>
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is pending deprecation and will be deprecated and subsequently removed in future releases.</p></li>
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</ul>
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<ul class="simple">
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<li><p>The <a class="reference internal" href="stubs/qiskit_optimization.algorithms.GroverOptimizer.html#qiskit_optimization.algorithms.GroverOptimizer" title="qiskit_optimization.algorithms.GroverOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroverOptimizer</span></code></a> class has a new keyword argument, <code class="docutils literal notranslate"><span class="pre">sampler</span></code> which is
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used to run the algorithm using an instance of the <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseSampler" title="(in Qiskit v0.46)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.primitives.BaseSampler</span></code></a>
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used to run the algorithm using an instance of the <code class="xref py py-class docutils literal notranslate"><span class="pre">qiskit.primitives.BaseSampler</span></code>
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interface to calculate the results. This new argument supersedes the
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the <code class="docutils literal notranslate"><span class="pre">quantum_instance</span></code> argument and accordingly, <code class="docutils literal notranslate"><span class="pre">quantum_instance</span></code>
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is pending deprecation and will be deprecated and subsequently removed in
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into a constraint of Docplex without any error.</p></li>
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</ul>
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<ul class="simple">
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<li><p>Allow Qiskit’s <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.optimizers.Optimizer" title="(in Qiskit v0.46)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> classes as input for
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<li><p>Allow Qiskit’s <code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code> classes as input for
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the <code class="docutils literal notranslate"><span class="pre">optimizer</span></code> in the <code class="xref py py-class docutils literal notranslate"><span class="pre">VQEProgram</span></code> and
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<code class="xref py py-class docutils literal notranslate"><span class="pre">QAOAProgram</span></code> instead of only
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dictionaries.</p></li>

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‎stubs/qiskit_optimization.algorithms.GroverOptimizer.html

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<a class="reference internal" href="qiskit_optimization.converters.QuadraticProgramToQubo.html#qiskit_optimization.converters.QuadraticProgramToQubo" title="qiskit_optimization.converters.QuadraticProgramToQubo"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuadraticProgramToQubo</span></code></a> will be used.</p></li>
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<li><p><strong>penalty</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em>) – The penalty factor used in the default
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<a class="reference internal" href="qiskit_optimization.converters.QuadraticProgramToQubo.html#qiskit_optimization.converters.QuadraticProgramToQubo" title="qiskit_optimization.converters.QuadraticProgramToQubo"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuadraticProgramToQubo</span></code></a> converter</p></li>
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<li><p><strong>sampler</strong> (<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseSampler" title="(in Qiskit v0.46)"><em>BaseSampler</em></a><em> | </em><em>None</em>) – A Sampler to use for sampling the results of the circuits.</p></li>
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<li><p><strong>sampler</strong> (<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseSamplerV1" title="(in Qiskit v1.0)"><em>BaseSamplerV1</em></a><em> | </em><em>None</em>) – A Sampler to use for sampling the results of the circuits.</p></li>
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</ul>
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</dd>
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<dt class="field-even">Raises<span class="colon">:</span></dt>

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