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

• URL: http://arxiv.org/abs/2508.04098v1
• Date: Wed, 06 Aug 2025 05:30:25 GMT
• Title: Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits
• Authors: Soohaeng Yoo Willow, D. ChangMo Yang, Chang Woo Myung,
• Abstract summary: Hybrid quantum-classical algorithms combine conventional neural networks with variational quantum circuits (VQCs) running on today's noisy intermediate-scale quantum (NISQ) hardware.<n>Here we benchmark a purely classical E(3)-equi-variant message-passing machine learning potential (MLP) against a hybrid quantum-classical algorithm for predicting density functional theory (DFT) properties of liquid silicon.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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