Approximating quantum many-body wave functions using artificial neural networks
Approximating quantum many-body wave functions using artificial neural networks
复制标题
使用人工神经网络逼近量子多体波函数
DOI：
10.1103/physrevb.97.035116
复制
发表时间：
2018-01-09
影响因子：
3.7
通讯作者：
Liu, Jinguo
中科院分区：
文献类型：
--
作者：
Cai, Zi;Liu, Jinguo
In this paper, we demonstrate the expressibility of artificial neural networks (ANNs) in quantum many-body physics by showing that a feed-forward neural network with a small number of hidden layers can be trained to approximate with high precision the ground states of some notable quantum many-body systems. We consider the one-dimensional free bosons and fermions, spinless fermions on a square lattice away from half-filling, as well as frustrated quantum magnetism with a rapidly oscillating ground-state characteristic function. In the latter case, an ANN with a standard architecture fails, while that with a slightly modified one successfully learns the frustration-induced complex sign rule in the ground state and approximates the ground states with high precisions. As an example of practical use of our method, we also perform the variational method to explore the ground state of an anti-ferromagnetic $J_1-J_2$ Heisenberg model.
