Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Benchmarking Adversarially Robust Quantum Machine Learning at Scale
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Ithaca: Cornell University Library, arXiv.org
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English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Contents
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophistication, neural networks can be easily fooled by carefully designed malicious inputs known as adversarial attacks. While such vulnerabilities remain a serious challenge for cl...
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Full title
Benchmarking Adversarially Robust Quantum Machine Learning at Scale
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TN_cdi_proquest_journals_2739579244
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2739579244
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E-ISSN
2331-8422
DOI
10.48550/arxiv.2211.12681
