TY - GEN
T1 - Quantum machine learning for material synthesis and hardware security
AU - Beaudoin, Collin
AU - Kundu, Satwik
AU - Topaloglu, Rasit Onur
AU - Ghosh, Swaroop
N1 - Publisher Copyright:
© 2022 Copyright held by the owner/author(s).
PY - 2022/10/30
Y1 - 2022/10/30
N2 - Using quantum computing, this paper addresses two scientificallypressing and day to day-relevant problems, namely, chemical retrosynthesis which is an important step in drug/material discovery and security of semiconductor supply chain.We showthat Quantum Long Short-Term Memory (QLSTM) is a viable tool for retrosynthesis. We achieve 65% training accuracy with QLSTM whereas classical LSTM can achieve 100%. However, in testing we achieve 80% accuracy with the QLSTM while classical LSTM peaks at only 70% accuracy! We also demonstrate an application of Quantum Neural Network (QNN) in the hardware security domain, specifically in Hardware Trojan (HT) detection using a set of power and area Trojan features. The QNN model achieves detection accuracy as high as 97.27%.
AB - Using quantum computing, this paper addresses two scientificallypressing and day to day-relevant problems, namely, chemical retrosynthesis which is an important step in drug/material discovery and security of semiconductor supply chain.We showthat Quantum Long Short-Term Memory (QLSTM) is a viable tool for retrosynthesis. We achieve 65% training accuracy with QLSTM whereas classical LSTM can achieve 100%. However, in testing we achieve 80% accuracy with the QLSTM while classical LSTM peaks at only 70% accuracy! We also demonstrate an application of Quantum Neural Network (QNN) in the hardware security domain, specifically in Hardware Trojan (HT) detection using a set of power and area Trojan features. The QNN model achieves detection accuracy as high as 97.27%.
UR - http://www.scopus.com/inward/record.url?scp=85145651268&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145651268&partnerID=8YFLogxK
U2 - 10.1145/3508352.3561115
DO - 10.1145/3508352.3561115
M3 - Conference contribution
AN - SCOPUS:85145651268
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
BT - Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
Y2 - 30 October 2022 through 4 November 2022
ER -