Optimal provable robustness of quantum classification via quantum hypothesis testing

Abstract Quantum machine learning models have the potential to offer speedups and better predictive accuracy compared to their classical counterparts. However, these quantum algorithms, like their classical counterparts, have been shown to also be vulnerable to input perturbations, in particular for...

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Auteurs principaux: Maurice Weber, Nana Liu, Bo Li, Ce Zhang, Zhikuan Zhao
Format: article
Langue:EN
Publié: Nature Portfolio 2021
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Accès en ligne:https://doaj.org/article/4da0084235ce432aaa2a4943af0014d9
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