Quantum neural networks force fields generation

TL;DR

This paper introduces a quantum neural network architecture for generating molecular force fields, demonstrating its potential to outperform classical models and offering a promising avenue for quantum advantage in scientific applications.

Contribution

It establishes a novel quantum neural network approach for learning molecular force fields, connecting classical and quantum solutions in this domain.

Findings

Quantum models show larger effective dimension than classical ones.

Quantum neural networks achieve competitive performance on molecular data.

Potential quantum advantages indicated for scientific applications.

Abstract

Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning methods have demonstrated impressive performances in predicting accurate values for energy and forces when trained on finite size ensembles generated with ab initio techniques. At the same time, quantum computers have recently started to offer new viable computational paradigms to tackle such problems. On the one hand, quantum algorithms may notably be used to extend the reach of electronic structure calculations. On the other hand, quantum machine learning is also emerging as an alternative and promising path to quantum advantage. Here we follow this second route and establish a direct connection between classical and quantum solutions for learning neural network potentials. To this end, we design a quantum neural network architecture and apply it successfully to different molecules of growing complexity. The quantum models exhibit larger effective dimension with respect to classical counterparts and can reach competitive performances, thus pointing towards potential quantum advantages in natural science applications via quantum machine learning.