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Variational Corner Transfer Matrix Renormalization Group Method for Classical Statistical Models
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作者 X.F.Liu y.f.fu +2 位作者 W.Q.Yu J.F.Yu Z.Y.Xie 《Chinese Physics Letters》 SCIE EI CAS CSCD 2022年第6期65-70,共6页
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 optimizatio... 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. 展开更多
关键词 TENSOR VARIATIONAL STRAIGHT
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