论文标题
通过将变量量子特征夫勒与机器学习整合到准确的势能表面
Exploring accurate potential energy surfaces via integrating variational quantum eigensovler with machine learning
论文作者
论文摘要
势能表面（PE）对于解释各种化学反应过程至关重要。但是，由于高计算成本，使用高级电子结构方法预测准确的佩斯是一项艰巨的任务。作为量子计算的吸引力应用，我们在这项工作中表明，可以将变异量子算法与机器学习（ML）技术集成为探索准确的伙伴的有希望的方案。不同于使用ML模型代表势能，我们将分子几何信息编码到代表变异量子eigensolver（VQE）的参数的深神经网络（DNN）中，将PES留在波函数ANSATZ中。一旦训练了DNN模型，就可以避免阻碍VQE应用于复杂系统的变化优化程序，因此，对PESS的评估得到了显着加速。数值结果表明，一个简单的DNN模型能够重现小分子的准确佩斯。
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) for representing 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.
