小量子团簇的神经网络求解器
Neural Network Solver for Small Quantum Clusters
摘要：
机器学习方法最近已被应用于研究物理学中的各种问题。大多数研究都集中在解释传统的数值方法或现有的数据库产生的数据。一个有趣的问题是，是否有可能使用机器学习方法，特别是神经网络，来解决多体问题。本文提出了一种基于神经网络的小团簇相互作用量子问题的求解方法。我们研究了模拟单杂质Anderson模型的小量子团簇。我们表明，基于神经网络的求解器提供定量准确的结果相比，精确对角化方法的谱函数。这为利用神经网络方法作为其他多体数值方法（如动力学平均场论）的杂质求解器开辟了可能性。
英文摘要：
Machine learning approaches have recently been applied to the study of various problems in physics. Most of the studies are focused on interpreting the data generated by conventional numerical methods or an existing database. An interesting question is whether it is possible to use a machine learning approach, in particular a neural network, for solving the many-body problem. In this paper, we present a solver for interacting quantum problem for small clusters based on the neural network. We study the small quantum cluster which mimics the single impurity Anderson model. We demonstrate that the neural network based solver provides quantitatively accurate results for the spectral function as compared to the exact diagonalization method. This opens the possibility of utilizing the neural network approach as an impurity solver for other many body numerical approaches, such as dynamical mean field theory.
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。
