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The interplay of robustness and generalization in quantum machine learning

arXiv 2506.08455 ↗First posted 10 Jun 2025quant-ph cs.LG cs.SYcited by 0

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Abstract (arXiv)

While adversarial robustness and generalization have individually received substantial attention in the recent literature on quantum machine learning, their interplay is much less explored. In this chapter, we address this interplay for variational quantum models, which were recently proposed as function approximators in supervised learning. We discuss recent results quantifying both robustness and generalization via Lipschitz bounds, which explicitly depend on model parameters. Thus, they give rise to a regularization-based training approach for robust and generalizable quantum models, highlighting the importance of trainable data encoding strategies. The practical implications of the theoretical results are demonstrated with an application to time series analysis.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 54 references that are in the map. First posted on arXiv 10 Jun 2025.

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References · 32

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20 more
  1. 2021Encoding-dependent generalization bounds for parametrized quantum circuitscited by 82
  2. 2020Quantum noise protects quantum classifiers against adversariescited by 68
  3. 2019Quantum Adversarial Machine Learningcited by 67
  4. 2019Stochastic gradient descent for hybrid quantum-classical optimizationcited by 59
  5. 2022Out-of-distribution generalization for learning quantum dynamicscited by 49
  6. 2023Understanding quantum machine learning also requires rethinking generalizationcited by 49
  7. 2023Shadows of quantum machine learningcited by 31
  8. 2022Benchmarking Adversarially Robust Quantum Machine Learning at Scalecited by 29
  9. 2023Towards quantum enhanced adversarial robustness in machine learningcited by 28
  10. 2020Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testingcited by 25
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  12. 2023Let Quantum Neural Networks Choose Their Own Frequenciescited by 22
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  15. 2024A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Modelscited by 12
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  18. 2024Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularizationcited by 4
  19. 2024Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysiscited by 3
  20. 2025On the Generalization of Adversarially Trained Quantum Classifierscited by 1

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