  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.
