sQUlearn -- A Python Library for Quantum Machine Learning
This library addresses the problem of bridging quantum computing capabilities with practical machine learning applications for QML researchers and practitioners, though it is incremental as it builds on existing quantum frameworks.
The authors tackled the challenge of making quantum machine learning (QML) accessible and practical by developing sQUlearn, a Python library that integrates with classical tools like scikit-learn and supports quantum kernel methods and neural networks, resulting in a user-friendly, NISQ-ready tool with features such as automated execution and compatibility with multiple quantum frameworks.
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.