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

• URL: http://arxiv.org/abs/2204.03625v1
• Date: Thu, 7 Apr 2022 17:44:22 GMT
• Title: Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses
• Authors: Satwik Kundu and Swaroop Ghosh
• Abstract summary: Quantum machine learning (QML) can exploit the high dimensional Hilbert space to learn richer representations from limited data. We explore the possible future applications of QML in the hardware security domain. We expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures.
• Score: 5.444459446244819
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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