TY - GEN
T1 - Security Aspects of Quantum Machine Learning
T2 - 32nd Great Lakes Symposium on VLSI, GLSVLSI 2022
AU - Kundu, Satwik
AU - Ghosh, Swaroop
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/6/6
Y1 - 2022/6/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85131690834&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85131690834&partnerID=8YFLogxK
U2 - 10.1145/3526241.3530833
DO - 10.1145/3526241.3530833
M3 - Conference contribution
AN - SCOPUS:85131690834
T3 - Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
SP - 463
EP - 468
BT - GLSVLSI 2022 - Proceedings of the Great Lakes Symposium on VLSI 2022
PB - Association for Computing Machinery
Y2 - 6 June 2022 through 8 June 2022
ER -