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<h1>Welcome to the sQUlearn documentation!<a class="headerlink" href="#welcome-to-the-squlearn-documentation" title="Link to this heading"></a></h1>
<p>sQUlearn is a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library’s dual-layer architecture serves both QML researchers and practitioners, enabling efficient prototyping, experimentation, and pipelining. sQUlearn provides a comprehensive tool set that includes both quantum kernel methods and quantum neural networks, along with features like customizable data encoding strategies, automated execution handling, and specialized kernel regularization techniques. By focusing on NISQ-compatibility and end-to-end automation, sQUlearn aims to bridge the gap between current quantum computing capabilities and practical machine learning applications.</p>
<p>sQUlearn offers scikit-learn compatible high-level interfaces for various kernel methods, QNNs and quantum reservoir computing. They build on top of the low-level interfaces of the QNN engine and the quantum kernel engine. The executor is used to run experiments on simulated and real backends of the PennyLane, Qiskit and Qulacs frameworks.</p>
<p align="center">
<img width=600px alt="sQUlearn schematic" src="https://raw.githubusercontent.com/sQUlearn/squlearn/main/docs/_static/schematic.png" />
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<hr class="docutils" />
<section id="prerequisites">
<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading"></a></h2>
<p>The package requires <strong>at least Python 3.9</strong>.</p>
</section>
<section id="install-squlearn">
<h2>Install sQUlearn<a class="headerlink" href="#install-squlearn" title="Link to this heading"></a></h2>
<section id="stable-release">
<h3>Stable Release<a class="headerlink" href="#stable-release" title="Link to this heading"></a></h3>
<p>To install the stable release version of sQUlearn, run the following command:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>squlearn
</pre></div>
</div>
<p>Alternatively, you can install sQUlearn directly from GitHub via</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>git+ssh://[email protected]:sQUlearn/squlearn.git
</pre></div>
</div>
</section>
</section>
<section id="examples">
<h2>Examples<a class="headerlink" href="#examples" title="Link to this heading"></a></h2>
<p>There are several more elaborate examples available in the folder <code class="docutils literal notranslate"><span class="pre">./examples</span></code> which display the features of this package.
Tutorials for beginners can be found at <code class="docutils literal notranslate"><span class="pre">./examples/tutorials</span></code>.</p>
<p>To install the required packages, run</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>.<span class="o">[</span>examples<span class="o">]</span>
</pre></div>
</div>
</section>
<section id="contribute-to-squlearn">
<h2>Contribute to sQUlearn<a class="headerlink" href="#contribute-to-squlearn" title="Link to this heading"></a></h2>
<p>Thanks for considering contributing to sQUlearn! Please read our <a class="reference external" href="https://github.com/sQUlearn/squlearn/blob/main/.github/CONTRIBUTING.md">contribution guidelines</a> before you submit a pull request.</p>
</section>
<hr class="docutils" />
<section id="license">
<h2>License<a class="headerlink" href="#license" title="Link to this heading"></a></h2>
<p>sQUlearn is released under the <a class="reference external" href="https://github.com/sQUlearn/squlearn/blob/main/LICENSE.txt">Apache License 2.0</a></p>
</section>
<section id="cite-squlearn">
<h2>Cite sQUlearn<a class="headerlink" href="#cite-squlearn" title="Link to this heading"></a></h2>
<p>If you use sQUlearn in your work, please cite our paper:</p>
<blockquote>
<div><p>Kreplin, D. A., Willmann, M., Schnabel, J., Rapp, F., Hagelüken, M., & Roth, M. (2025). sQUlearn: A Python Library for Quantum Machine Learning. <em>IEEE Software, 42</em>(5), 65–72. <a class="reference external" href="https://doi.org/10.1109/MS.2025.3527736">https://doi.org/10.1109/MS.2025.3527736</a></p>
</div></blockquote>
</section>
<section id="contact">
<h2>Contact<a class="headerlink" href="#contact" title="Link to this heading"></a></h2>
<p>This project is maintained by the quantum computing group at the Fraunhofer Institute for Manufacturing Engineering and Automation IPA.</p>
<p><a class="reference external" href="http://www.ipa.fraunhofer.de/quantum">http://www.ipa.fraunhofer.de/quantum</a></p>
<p>For general questions regarding sQUlearn, use the <a class="reference external" href="https://github.com/sQUlearn/squlearn/discussions">GitHub Discussions</a> or feel free to contact <a class="reference external" href="mailto:sQUlearn%40gmail.com">sQUlearn<span>@</span>gmail<span>.</span>com</a>.</p>
</section>
<hr class="docutils" />
<section id="acknowledgements">
<h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Link to this heading"></a></h2>
<p>This project was supported by the German Federal Ministry of Economic Affairs and Climate Action through the projects AutoQML (grant no. 01MQ22002A) and AQUAS (grant no. 01MQ22003D), as well as the German Federal Ministry of Education and Research through the project H2Giga Degrad-EL3 (grant no. 03HY110D).</p>
</section>
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<li class="toctree-l1"><a class="reference internal" href="install/install.html">Installation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="install/install.html#prerequisites">Prerequisites</a></li>
<li class="toctree-l2"><a class="reference internal" href="install/install.html#stable-release">Stable Release</a></li>
<li class="toctree-l2"><a class="reference internal" href="install/install.html#bleeding-edge-version">Bleeding-edge version</a></li>
<li class="toctree-l2"><a class="reference internal" href="install/install.html#development-version">Development version</a></li>
<li class="toctree-l2"><a class="reference internal" href="install/install.html#installation-with-optional-dependencies">Installation with optional dependencies:</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="user_guide/user_guide_index.html">User Guide</a><ul>
<li class="toctree-l2"><a class="reference internal" href="user_guide/executor.html">The Executor Class</a></li>
<li class="toctree-l2"><a class="reference internal" href="user_guide/observables.html">Observables for expectation values</a></li>
<li class="toctree-l2"><a class="reference internal" href="user_guide/encoding_circuits.html">Quantum Encoding Circuits</a></li>
<li class="toctree-l2"><a class="reference internal" href="user_guide/kernel_methods.html">Quantum Kernel Methods</a></li>
<li class="toctree-l2"><a class="reference internal" href="user_guide/quantum_neural_networks.html">Quantum Neural Networks</a></li>
<li class="toctree-l2"><a class="reference internal" href="user_guide/quantum_reservoir_computing.html">Quantum Reservoir Computing</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/classes.html">API Reference</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#high-level-api">High Level API</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#circuit-design">Circuit Design</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#execution-tools">Execution Tools</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#core">Core</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="examples/examples_index.html">Examples</a><ul>
<li class="toctree-l2"><a class="reference internal" href="examples/example_kernel_digit_classification.html">Handwritten Digit Recognition with Projected Quantum Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="examples/example_kernel_grid_search.html">Hyperparameter Optimization and Pipelines</a></li>
<li class="toctree-l2"><a class="reference internal" href="examples/example_quantum_bayesian_optimization.html">Bayesian Optimization using a Quantum Gaussian Process Surrogate Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="examples/example_qnn_backend_mitigation.html">Error Mitigation for Quantum Neural Networks on IBM Quantum Devices</a></li>
<li class="toctree-l2"><a class="reference internal" href="examples/example_ode_solver.html">Solving a First-order Ordinary Differential Equation (ODE) using QML methods</a></li>
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