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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.08455 |
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| _version_ | 1866912422446497792 |
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| author | Berberich, Julian Fellner, Tobias Holm, Christian |
| author_facet | Berberich, Julian Fellner, Tobias Holm, Christian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08455 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The interplay of robustness and generalization in quantum machine learning Berberich, Julian Fellner, Tobias Holm, Christian Quantum Physics Machine Learning Systems and Control 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. |
| title | The interplay of robustness and generalization in quantum machine learning |
| topic | Quantum Physics Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2506.08455 |