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

TL;DR

This paper explores the security challenges and defenses in quantum machine learning, highlighting its potential applications in hardware security and identifying vulnerabilities and attack models.

Contribution

It is the first comprehensive analysis of security issues in quantum machine learning, proposing countermeasures against emerging threats.

Findings

QML can be applied to hardware security tasks

Identified security vulnerabilities in QML models

Suggested countermeasures for QML security threats

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.