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IEEE Software

sQUlearn: A Python Library for Quantum Machine Learning [Focus: Quantum Software and Its Engineering]

David A. Kreplin, Moritz Willmann, Jan Schnabel, Frederic Rapp, Manuel Hagelüken, Marco Roth

Published Jan 14, 2025• 10 citations
Publisher

Abstract

sQUlearn introduces a user-friendly, noisy intermediate-scale quantum (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.

DOI
10.1109/ms.2025.3527736

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sQUlearn introduces a user-friendly, noisy intermediate-scale quantum (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 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.
Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervention. Quantum Machine Learning (QML) offers the potential to surpass classical machine learning (ML) capabilities by utilizing quantum computing. However, the complexity of QML presents substantial entry barriers. We introduce AutoQML, a novel framework that adapts the AutoML approach to QML, providing a modular and unified programming interface to facilitate the development of QML pipelines. AutoQML leverages the QML library sQUlearn to support a variety of QML algorithms. The framework is capable of constructing end-to-end pipelines for supervised learning tasks, ensuring accessibility and efficacy. We evaluate AutoQML across four industrial use cases, demonstrating its ability to generate high-performing QML pipelines that are competitive with both classical ML models and manually crafted quantum solutions.
Quantum Computing has attracted much research attention because of its potential to achieve fundamental speed and efficiency improvements in various domains. Among different quantum algorithms, Parameterized Quantum Circuits (PQC) for Quantum Machine Learning (QML) show promises to realize quantum advantages on the current Noisy Intermediate-Scale Quantum (NISQ) Machines. Therefore, to facilitate the QML and PQC research, a recent python library called TorchQuantum has been released. It can construct, simulate, and train PQC for machine learning tasks with high speed and convenient debugging supports. Besides quantum for ML, we want to raise the community's attention on the reversed direction: ML for quantum. Specifically, the TorchQuantum library also supports using data-driven ML models to solve problems in quantum system research, such as predicting the impact of quantum noise on circuit fidelity and improving the quantum circuit compilation efficiency.
Quantum Machine Learning (QML) leverages quantum mechanical properties to enhance computational capabilities. With its emergence, there is a need to integrate QML models into machine learning pipelines for real-life applications such as image processing. While standalone programs exist to demonstrate the performance of QML models, a well-defined model workflow is noticeably absent. We present piQture, an open-source Python and Qiskit-based library that streamlines the implementation and training of QML models tailored for image processing. Its design and structure prioritize usability among users familiar with classical machine learning without prior QML experience.
Quantum Machine Learning (QML) is a discipline of research at the intersection of quantum information and machine learning that leverages quantum mechanical properties to enhance computational capabilities. With its emergence, there is a need to integrate QML models into machine learning pipelines for real-life applications such as image processing. While standalone programs exist to demonstrate the performance of QML models, a well-defined model workflow is noticeably absent. This thesis thoroughly explores various existing QML models and their practical utility in image processing tasks, with the aim of constructing a robust QML library. Throughout this thesis, we develop piQture, an open-source Python and Qiskit-based library that streamlines the development, training, and evaluation of QML models. Its design and structure prioritize usability among users familiar with classical machine learning without prior QML experience. Further, piQture is augmented with automated building, testing, and packaging workflows that enhance software reliability and reproducibility. Finally, we provide strategies to facilitate model management and storage within piQture for practical adoption and future analysis of pre-trained QML models.
Abstract In the current Noisy Intermediate-Scale Quantum (NISQ) era, despite quantum computing is challenged by scale limitations and noise, it still holds immense potential to accelerate machine learning for specific problems. However, we have found that existing machine learning platforms do not adequately take into account the properties of quantum computing, resulting in a lack of an integrated quantum machine learning environment. Therefore, this article proposes a software architecture specifically designed for quantum machine learning platform, which aims to provide development tools that offer full life-cycle support for quantum machine learning. We first analyze the software requirements of quantum machine learning platform by using user story method, and then construct the platform architecture blueprint by using multi-view software architecture method. To validate the effectiveness of the architecture we proposed, we used this architecture as a specification to implement quantum reinforcement learning (QRL) and construct a quantum convolutional neural network (QNN). The results indicate that our architecture can effectively support the fusion of quantum computing and machine learning. We believe that this software architecture will propel the development of quantum machine learning in the NISQ era, bringing new opportunities to quantum computing.