Yao Qianjun, Ji Qun, Li Xiaopeng, Zhang Yehui, Chen Xinyu, Ju Ming-Gang, Liu Jie, Wang Jinlan
Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing 211189, China.
Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China.
J Phys Chem Lett. 2024 Jul 11;15(27):7061-7068. doi: 10.1021/acs.jpclett.4c01445. Epub 2024 Jul 1.
Electronically excited-state problems represent a crucial research field in quantum chemistry, closely related to numerous practical applications in photophysics and photochemistry. The emerging of quantum computing provides a promising computational paradigm to solve the Schrödinger equation for predicting potential energy surfaces (PESs). Here, we present a deep neural network model to predict parameters of the quantum circuits within the framework of variational quantum deflation and subspace search variational quantum eigensolver, which are two popular excited-state algorithms to implement on a quantum computer. The new machine learning-assisted algorithm is employed to study the excited-state PESs of small molecules, achieving highly accurate predictions. We then apply this algorithm to study the excited-state properties of the ArF system, which is essential to a gas laser. Through this study, we believe that with future advancements in hardware capabilities, quantum computing could be harnessed to solve excited-state problems for a broad range of systems.
电子激发态问题是量子化学中的一个关键研究领域，与光物理和光化学中的众多实际应用密切相关。量子计算的出现为求解薛定谔方程以预测势能面（PES）提供了一种很有前景的计算范式。在此，我们提出一种深度神经网络模型，用于在变分量子消去和子空间搜索变分量子特征求解器框架内预测量子电路的参数，这是两种在量子计算机上实现的常用激发态算法。这种新的机器学习辅助算法被用于研究小分子的激发态势能面，实现了高精度预测。然后我们应用该算法研究ArF系统的激发态性质，这对气体激光器至关重要。通过这项研究，我们相信随着未来硬件能力的进步，量子计算可用于解决广泛系统的激发态问题。
