典型文献
Variational Corner Transfer Matrix Renormalization Group Method for Classical Statistical Models
文献摘要:
In the context of tensor network states,we for the first time reformulate the corner transfer matrix renormalization group(CTMRG)method into a variational bilevel optimization algorithm.The solution of the optimization problem corresponds to the fixed-point environment pursued in the conventional CTMRG method,from which the partition function of a classical statistical model,represented by an infinite tensor network,can be efficiently evaluated.The validity of this variational idea is demonstrated by the high-precision calculation of the residual entropy of the dimer model,and is further verified by investigating several typical phase transitions in classical spin models,where the obtained critical points and critical exponents all agree with the best known results in literature.Its extension to three-dimensional tensor networks or quantum lattice models is straightforward,as also discussed briefly.
文献关键词:
中图分类号:
作者姓名:
X.F.Liu;Y.F.Fu;W.Q.Yu;J.F.Yu;Z.Y.Xie
作者机构:
Department of Physics,Renmin University of China,Beijing 100872,China;School of Physics and Electronics,Hunan University,Changsha 410082,China
文献出处:
引用格式:
[1]X.F.Liu;Y.F.Fu;W.Q.Yu;J.F.Yu;Z.Y.Xie-.Variational Corner Transfer Matrix Renormalization Group Method for Classical Statistical Models)[J].中国物理快报(英文版),2022(06):54-59
A类:
Renormalization,CTMRG
B类:
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AB值:
0.698072
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