Quantum machine learning interatomic potential: Application of variational quantum algorithm

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.

arXiv:2607.27841 [quant-ph]

(or arXiv:2607.27841v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2607.27841 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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