AI 中文总结
本研究将量子电路学习应用于MLIP，采用量子迁移学习架构再训练ANI模型，发现结合量子电路的MLIP在预训练模型精度有提升空间时，精度略高于全经典神经网络，可推进量子机器学习在MLIP中的应用。
AI 中文摘要
本研究将常用的混合量子-经典机器学习算法——量子电路学习应用于机器学习原子间势（MLIP），以预测分子数据集中分子的能量。我们采用量子迁移学习架构[Mari等人，Quantum，4：340，2020]对ANI模型进行了再训练，并使用量子电路模拟器评估了数值精度。评估结果证实，在某些条件下，将量子电路插入MLIP的经典神经网络中，比完全经典的神经网络精度略高；特别是当预训练模型的精度还有提升空间时，结合量子电路的模型更为有效。这些发现可能有助于推进量子机器学习在MLIP中的应用。
英文摘要
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.
Comments15 pages, 7 figures
