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AutoQML: A Framework for Automated Quantum Machine Learning

arXiv 2502.21025 ↗First posted 28 Feb 2025quant-ph cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 53 references that are in the map. First posted on arXiv 28 Feb 2025.

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References · 27

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  4. 2018Supervised learning with quantum enhanced feature spacescited by 654
  5. 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
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  7. 2023Challenges and Opportunities in Quantum Machine Learningcited by 202
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  9. 2021The Inductive Bias of Quantum Kernelscited by 90
  10. 2024Better than classical? The subtle art of benchmarking quantum machine learning modelscited by 82
  11. 2021Training Quantum Embedding Kernels on Near-Term Quantum Computerscited by 69
  12. 2023Towards provably efficient quantum algorithms for large-scale machine-learning modelscited by 32
15 more
  1. 2021Quantum machine learning of large datasets using randomized measurementscited by 30
  2. 2021Automatic design of quantum feature mapscited by 27
  3. 2024Quantum Kernel Methods under Scrutiny: A Benchmarking Studycited by 22
  4. 2021How Much Automation Does a Data Scientist Want?cited by 20
  5. 2023Reduction of finite sampling noise in quantum neural networkscited by 12
  6. 2024Quantum Machine Learning Architecture Search via Deep Reinforcement Learningcited by 12
  7. 2022Automatic and effective discovery of quantum kernelscited by 10
  8. 2023Quantum Gaussian Process Regression for Bayesian Optimizationcited by 9
  9. 2024Reinforcement learning-based architecture search for quantum machine learningcited by 8
  10. 2022Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Frameworkcited by 7
  11. 2021Incremental Search Space Construction for Machine Learning Pipeline Synthesiscited by 6
  12. 2022AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communicationscited by 6
  13. 2024Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Imagescited by 5
  14. 2022AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Imagescited by 4
  15. 2023sQUlearn -- A Python Library for Quantum Machine Learningcited by 3

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