Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses

Satwik Kundu, Swaroop Ghosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data and thus can efficiently solve complex learning tasks. Despite the increased interest in QML, there have not been many studies that discuss the security aspects of QML. In this work, we explored the possible future applications of QML in the hardware security domain. We also expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures.

Original languageEnglish (US)
Title of host publicationGLSVLSI 2022 - Proceedings of the Great Lakes Symposium on VLSI 2022
PublisherAssociation for Computing Machinery
Pages463-468
Number of pages6
ISBN (Electronic)9781450393225
DOIs
StatePublished - Jun 6 2022
Event32nd Great Lakes Symposium on VLSI, GLSVLSI 2022 - Irvine, United States
Duration: Jun 6 2022Jun 8 2022

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Conference

Conference32nd Great Lakes Symposium on VLSI, GLSVLSI 2022
Country/TerritoryUnited States
CityIrvine
Period6/6/226/8/22

All Science Journal Classification (ASJC) codes

  • General Engineering

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