Quantum Machine Learning for Material Synthesis and Hardware Security
It addresses incremental improvements in material synthesis and hardware security for researchers and engineers in quantum computing and related domains.
This paper tackles chemical retrosynthesis for drug/material discovery and hardware Trojan detection for semiconductor security, achieving 80% testing accuracy with Quantum LSTM (outperforming classical LSTM at 70%) and 97.27% detection accuracy with a Quantum Neural Network.
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%.