Neural Network Solver for Small Quantum Clusters
Neural Network Solver for Small Quantum Clusters
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DOI：
10.3390/cryst12091269
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发表时间：
2020-08
期刊：
影响因子：
2.7
通讯作者：
Nicholas Walker;Samuel Kellar;Yi Zhang;Ka-Ming Tam
中科院分区：
文献类型：
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作者：
Nicholas Walker;Samuel Kellar;Yi Zhang;Ka-Ming Tam
机器学习方法最近已被应用于研究物理学中的各种问题。这些研究大多集中在解释传统数值方法产生的数据或现有实验数据库上的数据。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。在本文中，我们提出了一个神经网络求解单杂质安德森模型，在小集群相互作用量子问题的范例。我们证明了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。这打开了利用神经网络方法作为杂质求解器的其他多体数值方法，如动力学平均场理论的可能性。
Machine learning approaches have recently been applied to the study of various problems in physics. Most of these studies are focused on interpreting the data generated by conventional numerical methods or the data on an existing experimental 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 neural network solver for the single impurity Anderson model, the paradigm of an interacting quantum problem in small clusters. We demonstrate that the neural-network-based solver provides quantitative 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 the dynamical mean field theory.