A quantum active learning workflow using quantum Gaussian process regression with projected and fidelity quantum kernels finds the global minimum of 4Al@Si11, but with no clear advantage over classical active learning and a search budget covering most of the isomer space.
sQUlearn -- A Python Library for Quantum Machine Learning
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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 end-to-end automation, sQUlearn aims to bridge the gap between current quantum computing capabilities and practical machine learning applications. The library provides substantial flexibility, enabling quick transitions between the underlying quantum frameworks Qiskit and PennyLane, as well as between simulation and running on actual hardware.
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quant-ph 1years
2024 1verdicts
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Quantum Active Learning for Structural Determination of Doped Nanoparticles -- a Case Study of 4Al@Si$_{11}$
A quantum active learning workflow using quantum Gaussian process regression with projected and fidelity quantum kernels finds the global minimum of 4Al@Si11, but with no clear advantage over classical active learning and a search budget covering most of the isomer space.