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

Autor:	Kundu, Satwik, Ghosh, Swaroop
Rok vydání:	2022
Předmět:	Computer Science - Cryptography and Security Computer Science - Machine Learning Quantum Physics
Druh dokumentu:	Working Paper
DOI:	10.1145/3526241.3530833
Popis:	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. Comment: 6 pages, GLSVLSI'22 Special Session
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.03625 Zobrazit plný text záznamu View this record from Arxiv