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题名: Renormalization of tensor-network states
作者: Zhao, H. H. ;  Xie, Z. Y. ;  Chen, Q. N. ;  Wei, Z. C. ;  Cai, J. W. ;  Xiang, T.
刊名: PHYSICAL REVIEW B
出版日期: 2010
卷号: 81, 期号:17, 页码:-
关键词: DENSITY-MATRIX RENORMALIZATION ;  BOND GROUND-STATES ;  SYSTEMS ;  ANTIFERROMAGNETS ;  FORMULATION ;  HONEYCOMB ;  LATTICE ;  CHAINS ;  MODELS
学科分类: Physics
通讯作者: Zhao, HH , Chinese Acad Sci, Inst Phys, POB 603, Beijing 100190, Peoples R China
部门归属: [Zhao, HH; Wei, ZC; Cai, JW; Xiang, T] Chinese Acad Sci, Inst Phys, Beijing 100190, Peoples R China; [Xie, ZY; Chen, QN; Xiang, T] Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China
英文摘要: We have discussed the tensor-network representation of classical statistical or interacting quantum lattice models, and given a comprehensive introduction to the numerical methods we recently proposed for studying the tensor-network states/models in two dimensions. A second renormalization scheme is introduced to take into account the environment contribution in the calculation of the partition function of classical tensornetwork models or the expectation values of quantum tensor-network states. It improves significantly the accuracy of the coarse-grained tensor renormalization-group method. In the study of the quantum tensornetwork states, we point out that the renormalization effect of the environment can be efficiently and accurately described by the bond vector. This, combined with the imaginary-time evolution of the wave function, provides an accurate projection method to determine the tensor-network wave function. It reduces significantly the truncation error and enables a tensor-network state with a large bond dimension, which is difficult to be accessed by other methods, to be accurately determined.
资助者: NSF-China; MOST, China
收录类别: SCI
原文出处: 查看原文
WOS记录号: WOS:000278141600069
Citation statistics: 
内容类型: 期刊论文
URI标识: http://ir.itp.ac.cn/handle/311006/5112
Appears in Collections:理论物理所1978-2010年知识产出_期刊论文

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Recommended Citation:
Zhao, H. H.,Xie, Z. Y.,Chen, Q. N.,et al. Renormalization of tensor-network states[J]. PHYSICAL REVIEW B,2010,81(17):-.
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