  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%.
