Generalization Bounds for Quantum Learning via Rényi Divergences
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866914480692133888 |
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| author | Warsi, Naqueeb Ahmad Dasgupta, Ayanava Hayashi, Masahito |
| author_facet | Warsi, Naqueeb Ahmad Dasgupta, Ayanava Hayashi, Masahito |
| contents | This work advances the theoretical understanding of quantum learning by establishing a new family of upper bounds on the expected generalization error of quantum learning algorithms, leveraging the framework introduced by Caro et al. (2024) and a new definition for the expected true loss. Our primary contribution is the derivation of these bounds in terms of quantum and classical Rényi divergences, utilizing a variational approach for evaluating quantum Rényi divergences, specifically the Petz and a newly introduced modified sandwich quantum Rényi divergence. Analytically and numerically, we demonstrate the superior performance of the bounds derived using the modified sandwich quantum Rényi divergence compared to those based on the Petz divergence. Furthermore, we provide probabilistic generalization error bounds using two distinct techniques: one based on the modified sandwich quantum Rényi divergence and classical Rényi divergence, and another employing smooth max Rényi divergence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11025 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalization Bounds for Quantum Learning via Rényi Divergences Warsi, Naqueeb Ahmad Dasgupta, Ayanava Hayashi, Masahito Quantum Physics Information Theory Machine Learning This work advances the theoretical understanding of quantum learning by establishing a new family of upper bounds on the expected generalization error of quantum learning algorithms, leveraging the framework introduced by Caro et al. (2024) and a new definition for the expected true loss. Our primary contribution is the derivation of these bounds in terms of quantum and classical Rényi divergences, utilizing a variational approach for evaluating quantum Rényi divergences, specifically the Petz and a newly introduced modified sandwich quantum Rényi divergence. Analytically and numerically, we demonstrate the superior performance of the bounds derived using the modified sandwich quantum Rényi divergence compared to those based on the Petz divergence. Furthermore, we provide probabilistic generalization error bounds using two distinct techniques: one based on the modified sandwich quantum Rényi divergence and classical Rényi divergence, and another employing smooth max Rényi divergence. |
| title | Generalization Bounds for Quantum Learning via Rényi Divergences |
| topic | Quantum Physics Information Theory Machine Learning |
| url | https://arxiv.org/abs/2505.11025 |