Open AccessA random-sampling method as an efficient alternative to variational Monte Carlo for solving Gutzwiller wavefunctions
Author(s) -
Feng Zhang,
Zhuo Ye,
Yongxin Yao,
CaiZhuang Wang,
KaiMing Ho
Publication year - 2021
Publication title -
journal of physics communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.407
H-Index - 17
ISSN - 2399-6528
DOI - 10.1088/2399-6528/ac3c32
Subject(s) - variational monte carlo , wave function , monte carlo method , quantum monte carlo , hubbard model , statistical physics , limit (mathematics) , importance sampling , physics , hybrid monte carlo , monte carlo method in statistical physics , sampling (signal processing) , mathematics , quantum mechanics , markov chain monte carlo , mathematical analysis , statistics , superconductivity , optics , detector
We present a random-sampling (RS) method for evaluating expectation values of physical quantities using the variational approach. We demonstrate that the RS method is computationally more efficient than the variational Monte Carlo method using the Gutzwiller wavefunctions applied on single-band Hubbard models as an example. Non-local constraints can also been easily implemented in the current scheme that capture the essential physics in the limit of strong on-site repulsion. In addition, we extend the RS method to study the antiferromagnetic states with multiple variational parameters for 1D and 2D Hubbard models.