Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses
It addresses security risks in QML, a growing field with potential for high-impact applications, but the work appears incremental as it surveys and exposes issues without presenting new methods or results.
The paper tackles the lack of studies on security aspects of quantum machine learning (QML) by exploring its future applications in hardware security, exposing vulnerabilities and attack models, and proposing countermeasures.
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.