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...
Main Authors: | Maurice Weber, Nana Liu, Bo Li, Ce Zhang, Zhikuan Zhao |
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Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2021-05-01
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Series: | npj Quantum Information |
Online Access: | https://doi.org/10.1038/s41534-021-00410-5 |
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