Hybrid quantum-classical neural network for calculating ground state energies of molecules

TL;DR

This paper introduces a hybrid quantum-classical neural network that accurately calculates molecular ground state energies and potential energy surfaces, combining quantum circuits with classical training.

Contribution

The paper presents a novel hybrid quantum-classical neural network approach for electronic structure calculations, enabling efficient generation of molecular potential energy curves.

Findings

Accurately computed ground state potential energy curves for H₂, LiH, and BeH₂.

The method demonstrates potential for generating complex molecular potential energy surfaces.

The approach shows promise for advancing quantum chemistry simulations.

Abstract

We present a hybrid quantum classical neural network that can be trained to perform electronic structure calculation and generate potential energy curves of simple molecules. The method is based on the combination of parameterized quantum circuits and measurements. With unsupervised training, the neural network can generate electronic potential energy curves based on training at certain bond lengths. To demonstrate the power of the proposed new method, we present results of using the quantum-classical hybrid neural network to calculate ground state potential energy curves of simple molecules such as H$_2$, LiH and BeH$_2$. The results are very accurate and the approach could potentially be used to generate complex molecular potential energy surfaces.