论文标题
材料合成和硬件安全性的量子机学习
Quantum Machine Learning for Material Synthesis and Hardware Security
论文作者
论文摘要
使用量子计算，本文解决了两个科学压制和日常相关问题，即化学逆转录，这是药物/材料发现和半导体供应链安全性的重要一步。我们表明，量子长的短期内存（QLSTM）是可行合成的可行工具。我们使用QLSTM实现了65％的训练精度，而经典的LSTM可以达到100％。但是，在测试中，我们使用QLSTM实现了80％的精度，而经典LSTM的峰值仅为70％的精度！我们还展示了量子神经网络（QNN）在硬件安全域中的应用，特别是使用一组功率和区域特洛伊木马功能在硬件特洛伊木马（HT）检测中。 QNN模型的检测准确性高达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 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%.
