量子神经网络力场生成
Quantum neural networks force fields generation
摘要：
精确的分子力场对于大尺度分子动力学技术的有效实施至关重要。在过去的十年中，机器学习方法在预测能量和力的准确值方面表现出了令人印象深刻的性能，这些能量和力是在使用从头算技术生成的有限大小集合上训练的。与此同时，量子计算机最近开始提供新的可行的计算范式来解决这些问题。一方面，量子算法可以显著地用于扩展电子结构计算的范围。另一方面，量子机器学习也正在成为量子优势的替代和有前途的道路。在这里，我们遵循第二条路线，并建立了学习神经网络势的经典和量子解决方案之间的直接联系。为此，我们设计了一个量子神经网络架构，并成功地将其应用于日益复杂的不同分子。量子模型相对于经典模型表现出更大的有效维数，并且可以达到具有竞争力的性能，从而指出通过量子机器学习在自然科学应用中的潜在量子优势。
英文摘要：
Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning methods have demonstrated impressive performances in predicting accurate values for energy and forces when trained on finite size ensembles generated with ab initio techniques. At the same time, quantum computers have recently started to offer new viable computational paradigms to tackle such problems. On the one hand, quantum algorithms may notably be used to extend the reach of electronic structure calculations. On the other hand, quantum machine learning is also emerging as an alternative and promising path to quantum advantage. Here we follow this second route and establish a direct connection between classical and quantum solutions for learning neural network potentials. To this end, we design a quantum neural network architecture and apply it successfully to different molecules of growing complexity. The quantum models exhibit larger effective dimension with respect to classical counterparts and can reach competitive performances, thus pointing towards potential quantum advantages in natural science applications via quantum machine learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
