Symmetry-invariant quantum machine learning force fields

TL;DR

This paper introduces symmetry-invariant quantum neural networks for molecular force fields, improving accuracy and scalability by embedding physical symmetries, demonstrated on complex molecules and water dimers.

Contribution

It presents a novel quantum machine learning model that explicitly incorporates physical symmetries, enhancing performance over generic models in molecular force field predictions.

Findings

Invariant quantum models outperform generic ones on complex molecules.

The approach is versatile, demonstrated on water dimer systems.

Results indicate potential for larger-scale molecular simulations.

Abstract

Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate quantum computational methods, making use of variational quantum learning models to predict potential energy surfaces and atomic forces from ab initio training data. However, the trainability and scalability of such models are still limited, due to both theoretical and practical barriers. Inspired by recent developments in geometric classical and quantum machine learning, here we design quantum neural networks that explicitly incorporate, as a data-inspired prior, an extensive set of physically relevant symmetries. We find that our invariant quantum learning models outperform their more generic counterparts on individual molecules of growing complexity. Furthermore, we study a water dimer as a minimal example of a system with multiple components, showcasing the versatility of our proposed approach and opening the way towards larger simulations. Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning, and that chemical systems represent, in fact, an interesting and rich playground for the development and application of advanced quantum machine learning tools.