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arXiv · 2607.27841

Quantum machine learning interatomic potential: Application of variational quantum algorithm

Kohei Numata·Wataru Mizukami·Kosuke Mitarai·Keisuke Fujii·Yutaka Imamura
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Abstract

This study applied quantum circuit learning, a commonly used hybrid quantum-classical machine learning algorithm, to a machine learning interatomic potential (MLIP) for predicting the energies of molecules in molecular datasets. We retrained the ANI model using the quantum transfer learning architecture [Mari et al., Quantum, 4:340, 2020] and evaluated numerical accuracy with a quantum circuit simulator. The evaluation confirmed that inserting a quantum circuit into the classical neural network of the MLIP yielded slightly higher accuracy than the fully classical neural network under certain conditions. In particular, the model incorporating a quantum circuit was more effective when the pretraining model had room for improvement in accuracy. These findings may contribute to advancing the application of quantum machine learning for MLIPs.

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Kohei Numata, Wataru Mizukami, Kosuke Mitarai, Keisuke Fujii, Yutaka Imamura. 2026-07-30. Quantum machine learning interatomic potential: Application of variational quantum algorithm. https://arxiv.org/abs/2607.27841

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