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.

翻译：sQUlearn 推出了一款用户友好、兼容NISQ的Python库，专为量子机器学习（QML）设计，能够与scikit-learn等经典机器学习工具无缝集成。该库采用双层架构，兼顾QML研究人员与实践者的需求，支持高效的原型开发、实验与流程构建。sQUlearn 提供全面的工具集，涵盖量子核方法与量子神经网络，并具备可自定义的数据编码策略、自动化执行管理以及专门的核正则化技术等功能。通过聚焦NISQ兼容性与端到端自动化，sQUlearn 致力于弥合当前量子计算能力与实用机器学习应用之间的鸿沟。