Deploying to gh-pages from @ 78a47bf 🚀

Author: woodsp-ibm

Diff for: .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: 70c0bb5ae8d333ec48c9975fa2c2fbdc
+config: 9819ffc6e12194ec337e2a6db159e35c
 tags: 645f666f9bcd5a90fca523b33c5a78b7

Diff for: _images/tutorials_03_minimum_eigen_optimizer_31_0.png renamed to _images/tutorials_03_minimum_eigen_optimizer_31_1.png

@@ -95,8 +95,7 @@ Optional installs
 =================
 
 * **IBM CPLEX** may be installed using ``pip install 'qiskit-optimization[cplex]'`` to enable the reading of `LP` files and the usage of
-  the `CplexOptimizer`, wrapper for ``cplex.Cplex``. Currently there is no python 3.9 version of CPLEX. In this case, the CPLEX install
-  command will have no effect.
+  the `CplexOptimizer`, wrapper for ``cplex.Cplex``. CPLEX is a separate package and its support of Python versions is independent of Qiskit Optimization, where this CPLEX command will have no effect if there is no compatible version of CPLEX available (yet).
 
 * **CVXPY** may be installed using the command ``pip install 'qiskit-optimization[cvx]'``.
   CVXPY being installed will enable the usage of the Goemans-Williamson algorithm as an optimizer `GoemansWilliamsonOptimizer`.

Diff for: _images/tutorials_03_minimum_eigen_optimizer_39_0.png renamed to _images/tutorials_03_minimum_eigen_optimizer_39_1.png

@@ -16,7 +16,7 @@ A uniform interface as well as automatic conversion between different problem re
 allows users to solve problems using a large set of algorithms, from variational quantum algorithms,
 such as the Quantum Approximate Optimization Algorithm QAOA, to Grover Adaptive Search using the
 GroverOptimizer, leveraging fundamental algorithms provided by
-`Qiskit Algorithms <https://qiskit.org/ecosystem/algorithms/>`__. Furthermore, the modular design
+`Qiskit Algorithms <https://qiskit-community.github.io/qiskit-algorithms/>`__. Furthermore, the modular design
 of the optimization module allows it to be easily extended and facilitates rapid development and
 testing of new algorithms. Compatible classical optimizers are also provided for testing,
 validation, and benchmarking.

Diff for: _images/tutorials_06_examples_max_cut_and_tsp_16_1.png

@@ -16,7 +16,7 @@ Qiskit Optimization v0.6 supports only the new algorithms of Qiskit Algorithms.
 It is not the intention to provide detailed explanations of the
 new Qiskit Algorithms in this migration guide. We suggest that you read the
 `corresponding
-resources <https://qiskit.org/ecosystem/algorithms/index.html>`__
+resources <https://qiskit-community.github.io/qiskit-algorithms/index.html>`__
 of the Qiskit Algorithms documentation instead.
 
 We can basically use the existing codes by replacing ``qiskit.algorithms``

Diff for: _images/tutorials_06_examples_max_cut_and_tsp_19_1.png

@@ -15,7 +15,7 @@
    "source": [
     "## Introduction\n",
     "\n",
-    "This notebook shows how to use the Conditional Value at Risk (CVaR) objective function introduced in [1] within the variational quantum optimization algorithms provided by [Qiskit Algorithms](https://qiskit.org/ecosystem/algorithms/). Particularly, it is shown how to setup the `MinimumEigenOptimizer` using `SamplingVQE` accordingly. \n",
+    "This notebook shows how to use the Conditional Value at Risk (CVaR) objective function introduced in [1] within the variational quantum optimization algorithms provided by [Qiskit Algorithms](https://qiskit-community.github.io/qiskit-algorithms/). Particularly, it is shown how to setup the `MinimumEigenOptimizer` using `SamplingVQE` accordingly. \n",
     "For a given set of shots with corresponding objective values of the considered optimization problem, the CVaR with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots.\n",
     "Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, but still applying some averaging to smoothen the optimization landscape.\n",
     "\n",

Diff for: _images/tutorials_06_examples_max_cut_and_tsp_20_1.png

@@ -303,7 +303,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The [MinimumEigensolverResult](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.MinimumEigensolverResult.html) that results from performing VQE on the relaxed Hamiltonian is available:"
+    "The [MinimumEigensolverResult](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.MinimumEigensolverResult.html) that results from performing VQE on the relaxed Hamiltonian is available:"
    ]
   },
   {

Diff for: _images/tutorials_06_examples_max_cut_and_tsp_5_0.png

@@ -440,11 +440,12 @@ <h2>Qiskit optimization module (<a class="reference internal" href="#module-qisk
 A uniform interface as well as automatic conversion between different problem representations
 allows users to solve problems using a large set of algorithms, from variational quantum algorithms,
 such as the Quantum Approximate Optimization Algorithm
-(<a class="reference external" href="https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.QAOA.html#qiskit_algorithms.QAOA" title="(in Qiskit Algorithms v0.2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">QAOA</span></code></a>), to
+(<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
 <a class="reference external" href="https://arxiv.org/abs/quant-ph/9607014">Grover Adaptive Search</a>
 (<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
-fundamental <a class="reference external" href="https://qiskit.org/ecosystem/algorithms/apidocs/qiskit_algorithms.html#minimum-eigensolvers">minimum eigensolvers</a> provided by
-<a class="reference external" href="https://qiskit.org/ecosystem/algorithms/">Qiskit Algorithms</a>.
+fundamental <a class="reference external" href="https://qiskit-community.github.io/qiskit-algorithms/apidocs/qiskit_algorithms.html#minimum-eigensolvers">minimum eigensolvers</a>
+provided by
+<a class="reference external" href="https://qiskit-community.github.io/qiskit-algorithms/">Qiskit Algorithms</a>.
 Furthermore, the modular design
 of the optimization module allows it to be easily extended and facilitates rapid development and
 testing of new algorithms. Compatible classical optimizers are also provided for testing,

Diff for: _images/tutorials_06_examples_max_cut_and_tsp_8_1.png

@@ -506,8 +506,7 @@ <h2>Installing Qiskit Optimization from Source</h2><p>Using the same development
 <span id="id1"></span><h2>Optional installs<a class="headerlink" href="#optional-installs" title="Permalink to this heading">#</a></h2>
 <ul class="simple">
 <li><p><strong>IBM CPLEX</strong> may be installed using <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">'qiskit-optimization[cplex]'</span></code> to enable the reading of <cite>LP</cite> files and the usage of
-the <cite>CplexOptimizer</cite>, wrapper for <code class="docutils literal notranslate"><span class="pre">cplex.Cplex</span></code>. Currently there is no python 3.9 version of CPLEX. In this case, the CPLEX install
-command will have no effect.</p></li>
+the <cite>CplexOptimizer</cite>, wrapper for <code class="docutils literal notranslate"><span class="pre">cplex.Cplex</span></code>. CPLEX is a separate package and its support of Python versions is independent of Qiskit Optimization, where this CPLEX command will have no effect if there is no compatible version of CPLEX available (yet).</p></li>
 <li><p><strong>CVXPY</strong> may be installed using the command <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">'qiskit-optimization[cvx]'</span></code>.
 CVXPY being installed will enable the usage of the Goemans-Williamson algorithm as an optimizer <cite>GoemansWilliamsonOptimizer</cite>.</p></li>
 <li><p><strong>Matplotlib</strong> may be installed using the command <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">'qiskit-optimization[matplotlib]'</span></code>.

Diff for: _images/tutorials_12_quantum_random_access_optimizer_13_1.png

@@ -442,7 +442,7 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 allows users to solve problems using a large set of algorithms, from variational quantum algorithms,
 such as the Quantum Approximate Optimization Algorithm QAOA, to Grover Adaptive Search using the
 GroverOptimizer, leveraging fundamental algorithms provided by
-<a class="reference external" href="https://qiskit.org/ecosystem/algorithms/">Qiskit Algorithms</a>. Furthermore, the modular design
+<a class="reference external" href="https://qiskit-community.github.io/qiskit-algorithms/">Qiskit Algorithms</a>. Furthermore, the modular design
 of the optimization module allows it to be easily extended and facilitates rapid development and
 testing of new algorithms. Compatible classical optimizers are also provided for testing,
 validation, and benchmarking.</p>

Diff for: _images/tutorials_12_quantum_random_access_optimizer_35_1.png renamed to _images/tutorials_12_quantum_random_access_optimizer_35_2.png

@@ -440,7 +440,7 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <p>Qiskit Optimization v0.6 supports only the new algorithms of Qiskit Algorithms.</p>
 <p>It is not the intention to provide detailed explanations of the
 new Qiskit Algorithms in this migration guide. We suggest that you read the
-<a class="reference external" href="https://qiskit.org/ecosystem/algorithms/index.html">corresponding
+<a class="reference external" href="https://qiskit-community.github.io/qiskit-algorithms/index.html">corresponding
 resources</a>
 of the Qiskit Algorithms documentation instead.</p>
 <p>We can basically use the existing codes by replacing <code class="docutils literal notranslate"><span class="pre">qiskit.algorithms</span></code>