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sQUlearn -- A Python Library for Quantum Machine Learning
April 22, 2024, 4:43 a.m. | David A. Kreplin, Moritz Willmann, Jan Schnabel, Frederic Rapp, Manuel Hagel\"uken, Marco Roth
cs.LG updates on arXiv.org arxiv.org
Abstract: sQUlearn introduces 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 toolset 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 …
abstract architecture arxiv cs.lg enabling experimentation integration layer learn learning tools library machine machine learning nisq prototyping python qml quant-ph quantum researchers scikit scikit-learn seamless integration tools type
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