Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning

TL;DR

This paper introduces a transfer learning approach that combines classical and quantum data to enable efficient molecular dynamics simulations on quantum hardware, reducing quantum resource requirements while maintaining high accuracy.

Contribution

It presents a novel method integrating transfer learning with machine-learned potential energy surfaces to mitigate quantum hardware limitations in molecular dynamics simulations.

Findings

Reduces quantum energy evaluations by training on classical data first.

Achieves high-accuracy potential energy predictions with fewer quantum datasets.

Demonstrates effective use of neural networks for quantum chemistry simulations.

Abstract

The ability to perform ab initio molecular dynamics simulations using potential energies calculated on quantum computers would allow virtually exact dynamics for chemical and biochemical systems, with substantial impacts on the fields of catalysis and biophysics. However, noisy hardware, the costs of computing gradients, and the number of qubits required to simulate large systems present major challenges to realizing the potential of dynamical simulations using quantum hardware. Here, we demonstrate that some of these issues can be mitigated by recent advances in machine learning. By combining transfer learning with techniques for building machine-learned potential energy surfaces, we propose a new path forward for molecular dynamics simulations on quantum hardware. We use transfer learning to reduce the number of energy evaluations that use quantum hardware by first training models on larger, less accurate classical datasets and then refining them on smaller, more accurate quantum datasets. We demonstrate this approach by training machine learning models to predict a molecule's potential energy using Behler-Parrinello neural networks. When successfully trained, the model enables energy gradient predictions necessary for dynamics simulations that cannot be readily obtained directly from quantum hardware. To reduce the quantum resources needed, the model is initially trained with data derived from low-cost techniques, such as Density Functional Theory, and subsequently refined with a smaller dataset obtained from the optimization of the Unitary Coupled Cluster ansatz. We show that this approach significantly reduces the size of the quantum training dataset while capturing the high accuracies needed for quantum chemistry simulations.