Variational Corner Transfer Matrix Renormalization Group Method for Classical Statistical Models

作     者：X.F.Liu Y.F.Fu W.Q.Yu J.F.Yu Z.Y.Xie 刘雪飞;付阳峰;于伟强;余继锋;谢志远

作者机构：Department of PhysicsRenmin University of ChinaBeijing 100872China School of Physics and ElectronicsHunan UniversityChangsha 410082China 

出 版 物：《Chinese Physics Letters》 (中国物理快报（英文版）)

年 卷 期：2022年第39卷第6期

页      面：65-70页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 07[理学] 070205[理学-凝聚态物理] 08[工学] 0702[理学-物理学] 

基　　金：supported by the National R&D Program of China (Grant No. 2017YFA0302900) the National Natural Science Foundation of China (Grant Nos. 11774420 and 12134020) the Natural Science Foundation of Hunan Province (Grant No. 851204035) the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (Grant No. 20XNLG19) 

主　　题：tensor variational straight 

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 *** 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 *** 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 *** extension to three-dimensional tensor networks or quantum lattice models is straightforward,as also discussed briefly.