Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits

TL;DR

This paper explores a hybrid quantum-classical machine learning approach using variational quantum circuits to predict properties of liquid silicon, showing potential for near-term quantum advantage in materials modeling.

Contribution

It introduces a hybrid quantum-classical message-passing neural network architecture and benchmarks its performance against a classical model for DFT property prediction.

Findings

HQC-MLP accurately reproduces high-temperature properties of liquid silicon.

VQCs provide additional non-linearity and expressivity in the model.

Hybrid approach shows promise for near-term quantum advantage in materials science.

Abstract

Quantum algorithms for simulating large and complex molecular systems are still in their infancy, and surpassing state-of-the-art classical techniques remains an ever-receding goal post. A promising avenue of inquiry in the meanwhile is to seek practical advantages through hybrid quantum-classical algorithms, which combine conventional neural networks with variational quantum circuits (VQCs) running on today's noisy intermediate-scale quantum (NISQ) hardware. Such hybrids are well suited to NISQ hardware. The classical processor performs the bulk of the computation, while the quantum processor executes targeted sub-tasks that supply additional non-linearity and expressivity. Here, we benchmark a purely classical E(3)-equivariant message-passing machine learning potential (MLP) against a hybrid quantum-classical MLP for predicting density functional theory (DFT) properties of liquid silicon. In our hybrid architecture, every readout in the message-passing layers is replaced by a VQC. Molecular dynamics simulations driven by the HQC-MLP reveal that an accurate reproduction of high-temperature structural and thermodynamic properties is achieved with VQCs. These findings demonstrate a concrete scenario in which NISQ-compatible HQC algorithm could deliver a measurable benefit over the best available classical alternative, suggesting a viable pathway toward near-term quantum advantage in materials modeling.