Quantum machine learning interatomic potential: Application of variational quantum algorithm
arXiv:2607.27841
The paper integrates a variational quantum circuit into a classical neural network for interatomic potentials, retraining the ANI model via quantum transfer learning and showing modest accuracy gains in molecular energy predictions.
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.
15 pages, 7 figures