  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.
