Let quantum neural networks choose their own frequencies

Jaderberg, Ben; Gentile, Antonio A.; Berrada, Youssef Achari; Shishenina, Elvira; Elfving, Vincent E.

doi:10.1103/physreva.109.042421

Phys. Rev. A

2024

DOI: 10.1103/physreva.109.042421

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Let quantum neural networks choose their own frequencies

Ben Jaderberg,

Antonio A. Gentile,

Youssef Achari Berrada

et al.

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2024

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Cited by 2 publications

References 35 publications

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Potential of quantum scientific machine learning applied to weather modeling

Jaderberg,

Gentile,

Ghosh

et al. 2024

Phys. Rev. A

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Potential of quantum scientific machine learning applied to weather modeling

Jaderberg,

Gentile,

Ghosh

et al. 2024

Phys. Rev. A

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Training robust and generalizable quantum models

Berberich,

Fink,

Pranjić

et al. 2024

Phys. Rev. Research

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Adversarial robustness and generalization are both crucial properties of reliable machine learning models. In this paper, we study these properties in the context of quantum machine learning based on Lipschitz bounds. We derive parameter-dependent Lipschitz bounds for quantum models with trainable encoding, showing that the norm of the data encoding has a crucial impact on the robustness against data perturbations. Further, we derive a bound on the generalization error which explicitly involves the parameters of the data encoding. Based on these theoretical results, we propose a practical strategy for training robust and generalizable quantum models by regularizing the Lipschitz bound in the cost. Moreover, we show that, for fixed and nontrainable encodings, as those frequently employed in quantum machine learning, the Lipschitz bound cannot be influenced by tuning the parameters. Thus trainable encodings are crucial for systematically adapting robustness and generalization during training. The practical implications of our theoretical findings are illustrated with numerical results. Published by the American Physical Society 2024

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Let quantum neural networks choose their own frequencies

Cited by 2 publications

References 35 publications

Potential of quantum scientific machine learning applied to weather modeling

Potential of quantum scientific machine learning applied to weather modeling

Training robust and generalizable quantum models

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