典型文献
Green's function Monte Carlo method combined with restricted Boltzmann machine approach to the frustrated J1-J2 Heisenberg model
文献摘要：
Restricted Boltzmann machine(RBM)has been proposed as a powerful variational ansatz to represent the ground state of a given quantum many-body system.On the other hand,as a shallow neural network,it is found that the RBM is still hardly able to capture the characteristics of systems with large sizes or complicated interactions.In order to find a way out of the dilemma,here,we propose to adopt the Green's function Monte Carlo(GFMC)method for which the RBM is used as a guiding wave function.To demonstrate the implementation and effectiveness of the proposal,we have applied the proposal to study the frustrated J1-J2 Heisenberg model on a square lattice,which is considered as a typical model with sign problem for quantum Monte Carlo simulations.The calculation results demonstrate that the GFMC method can significantly further reduce the relative error of the ground-state energy on the basis of the RBM variational results.This encourages to combine the GFMC method with other neural networks like convolutional neural networks for dealing with more models with sign problem in the future.
文献关键词：
中图分类号：
作者姓名：
He-Yu Lin;Rong-Qiang He;Zhong-Yi Lu
作者机构：
Department of Physics,Renmin University of China,Beijing 100872,China
文献出处：
引用格式：
[1]He-Yu Lin;Rong-Qiang He;Zhong-Yi Lu-.Green's function Monte Carlo method combined with restricted Boltzmann machine approach to the frustrated J1-J2 Heisenberg model)[J].中国物理B（英文版）,2022(08):230-234
A类：
GFMC
B类：
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AB值：
0.523935
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