mlsanityData to 7 Oct 2026

sQUlearn -- A Python Library for Quantum Machine Learning

arXiv 2311.08990 ↗First posted 15 Nov 2023quant-ph cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 63 references that are in the map. First posted on arXiv 15 Nov 2023.

Builds on

26 of 63 references.

References · 26

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2020Variational Quantum Algorithmscited by 687
  3. 2018Supervised learning with quantum enhanced feature spacescited by 654
  4. 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
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  10. 2021Supervised quantum machine learning models are kernel methodscited by 199
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  12. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 126
14 more
  1. 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 124
  2. 2021Quantum machine learning beyond kernel methodscited by 91
  3. 2020TensorFlow Quantum: A Software Framework for Quantum Machine Learningcited by 72
  4. 2021Training Quantum Embedding Kernels on Near-Term Quantum Computerscited by 69
  5. 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43
  6. 2021Capacity and quantum geometry of parametrized quantum circuitscited by 32
  7. 2023Towards provably efficient quantum algorithms for large-scale machine-learning modelscited by 32
  8. 2019Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithmscited by 31
  9. 2021Quantum machine learning of large datasets using randomized measurementscited by 30
  10. 2015Quantum assisted Gaussian process regressioncited by 17
  11. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  12. 2023Reduction of finite sampling noise in quantum neural networkscited by 12
  13. 2022Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learningcited by 11
  14. 2023Quantum Gaussian Process Regression for Bayesian Optimizationcited by 9

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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Reinforcement learning-based architecture search for quantum machine learningcited by 8
  2. 2024Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Imagescited by 5
  3. 2025AutoQML: A Framework for Automated Quantum Machine Learningcited by 0