Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Q...
Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Quantum machine learning (QML) is emerging as an application of quantum computing with the potential to deliver quantum advantage, but its realisation for practical applications remains impeded by challenges. Amongst those, a key barrier is the computationally expensive task of encoding classical data into a quantum state, which could erase any pro...
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Full title
Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning
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TN_cdi_proquest_journals_2866253475
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2866253475
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E-ISSN
2331-8422
DOI
10.48550/arxiv.2309.09424
