Quantum Machine Learning for Material Synthesis and Hardware Security (Invited Paper)
Quantum Machine Learning for Material Synthesis and Hardware Security (Invited Paper)
复制标题
DOI：
10.1145/3508352.3561115
复制
发表时间：
2022-08
期刊：
影响因子：
--
通讯作者：
Collin Beaudoin;Satwik Kundu;R. Topaloglu;Swaroop Ghosh
中科院分区：
文献类型：
--
作者：
Collin Beaudoin;Satwik Kundu;R. Topaloglu;Swaroop Ghosh
利用量子计算，本文解决了两个科学紧迫和日常相关的问题，即化学逆合成，这是药物/材料发现和半导体供应链安全的重要步骤。我们表明，量子长短期记忆（QLSTM）是一个可行的工具，逆合成。我们使用QLSTM实现了65%的训练准确率，而经典LSTM可以达到100%。然而，在测试中，我们使用QLSTM达到了80%的准确率，而经典LSTM的准确率只有70%！我们还展示了量子神经网络（QNN）在硬件安全领域的应用，特别是在硬件木马（HT）检测使用一组电源和面积木马功能。QNN模型的检测准确率高达97.27%。
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 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%.