Exploring Accurate Potential Energy Surfaces via Integrating Variational Quantum Eigensolver with Machine LearningClick to copy article linkArticle link copied!
- Yanxian TaoYanxian TaoHefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, ChinaMore by Yanxian Tao
- Xiongzhi ZengXiongzhi ZengHefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, ChinaMore by Xiongzhi Zeng
- Yi FanYi FanHefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, ChinaMore by Yi Fan
- Jie Liu*Jie Liu*E-mail: [email protected]Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, ChinaMore by Jie Liu
- Zhenyu LiZhenyu LiHefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, ChinaHefei National Laboratory, University of Science and Technology of China, Hefei 230088, ChinaMore by Zhenyu Li
- Jinlong Yang*Jinlong Yang*E-mail: [email protected]Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, ChinaHefei National Laboratory, University of Science and Technology of China, Hefei 230088, ChinaMore by Jinlong Yang
Abstract
The potential energy surface (PES) is crucial for interpreting a variety of chemical reaction processes. However, predicting accurate PESs with high-level electronic structure methods is a challenging task due to the high computational cost. As an appealing application of quantum computing, we show in this work that variational quantum algorithms can be integrated with machine learning (ML) techniques as a promising scheme for exploring accurate PESs. Different from using a ML model to represent the potential energy, we encode the molecular geometry information into a deep neural network (DNN) to represent parameters of the variational quantum eigensolver (VQE), leaving the PES to the wave function ansatz. Once the DNN model is trained, the variational optimization procedure that hinders the application of the VQE to complex systems is avoided, and thus the evaluation of PESs is significantly accelerated. Numerical results demonstrate that a simple DNN model is able to reproduce accurate PESs for small molecules.
Cited By
This article is cited by 4 publications.
- Qianjun Yao, Qun Ji, Xiaopeng Li, Yehui Zhang, Xinyu Chen, Ming-Gang Ju, Jie Liu, Jinlan Wang. Machine Learning Accelerates Precise Excited-State Potential Energy Surface Calculations on a Quantum Computer. The Journal of Physical Chemistry Letters 2024, 15
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- Kalpak Ghosh, Sumit Kumar, Nirmal Mammavalappil Rajan, Sharma S. R. K. C. Yamijala. Deep Neural Network Assisted Quantum Chemistry Calculations on Quantum Computers. ACS Omega 2023, 8
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- Silvan Käser, Luis Itza Vazquez-Salazar, Markus Meuwly, Kai Töpfer. Neural network potentials for chemistry: concepts, applications and prospects. Digital Discovery 2023, 2
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, 28-58. https://doi.org/10.1039/D2DD00102K
- Zhenyu Li, Jie Liu, Xiangjian Shen, Feixue Gao. Challenges and opportunities of quantum-computational chemistry. SCIENTIA SINICA Chimica 2023, 53
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