Quantum Machine Learning for Material Synthesis and Hardware Security
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Abstract (arXiv)
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%.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 32 references that are in the map. First posted on arXiv 16 Aug 2022.
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11 of 32 references.
References · 11
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