Exploring accurate potential energy surfaces via integrating variational quantum eigensovler with machine learning

TL;DR

This paper proposes a novel approach combining variational quantum eigensolvers with machine learning to efficiently generate accurate potential energy surfaces, reducing computational costs for small molecules.

Contribution

It introduces encoding molecular geometry into a neural network to parameterize VQE, enabling faster PES evaluation without complex variational optimization.

Findings

DNN-parameterized VQE accurately reproduces PESs for small molecules.

The method significantly accelerates PES calculations compared to traditional VQE.

Numerical results validate the approach's effectiveness for chemical systems.

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) 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.