Using quantum computing, this paper addresses two scientifically pressing and day-to-day relevant problems, namely, chemical retrosynthesis which is an important step in drug/material discovery and security of the semiconductor supply chain. We show that 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%.
@article{arxiv.2208.08273,
title = {Quantum Machine Learning for Material Synthesis and Hardware Security},
author = {Collin Beaudoin and Satwik Kundu and Rasit Onur Topaloglu and Swaroop Ghosh},
journal= {arXiv preprint arXiv:2208.08273},
year = {2022}
}