Modular matrices as topological order parameter by a gauge-symmetry-preserved tensor renormalization approach

Modular matrices as topological order parameter by a gauge-symmetry-preserved tensor renormalization approach
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通过保规范对称性张量重正化方法将模矩阵作为拓扑序参数

DOI:
10.1103/physrevb.90.205114
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发表时间:
2014-01
期刊:
影响因子:
3.7
通讯作者:
Wen, Xiao-Gang
Wen, Xiao-Gang
中科院分区:
物理与天体物理2区
文献类型:
--
作者:
He, Huan;Moradi, Heidar;Wen, Xiao-Gang

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二十多年来,人们一直在提出拓扑序超越朗道对称破缺理论。但如何在一般多体状态下对其进行普遍检测仍然是一个具有挑战性的问题。本文将介绍一种基于张量网络的二维系统中模矩阵计算的系统数值方法,该方法可以完全识别边缘有缺口的拓扑顺序。此外,数值研究表明,模矩阵(包括S矩阵和T矩阵)是描述拓扑有序态和平凡态之间相变的鲁棒表征,可以作为拓扑有序参数。该方法只需要一个基态的局部信息以张量网络的形式存在,直接提供通用数据(S矩阵和T矩阵),没有任何非通用贡献。此外,它可以推广到更高的维度。与外推计算拓扑纠缠熵的数值复杂度呈指数级高不同,该方法以更低的数值成本提取了更完整的拓扑数据集(模矩阵)。
Topological order has been proposed to go beyond Landau symmetry breaking theory for more than twenty years. But it is still a challenging problem to generally detect it in a generic many-body state. In this paper, we will introduce a systematic numerical method based on tensor network to calculate modular matrices in 2D systems, which can fully identify topological order with gapped edge. Moreover, it is shown numerically that modular matrices, including S and T matrices, are robust characterization to describe phase transitions between topologically ordered states and trivial states, which can work as topological order parameters. This method only requires local information of one ground state in the form of a tensor network, and directly provides the universal data (S and T matrices), without any non-universal contributions. Furthermore it is generalizable to higher dimensions. Unlike calculating topological entanglement entropy by extrapolating, which numerical complexity is exponentially high, this method extracts a much more complete set of topological data (modular matrices) with much lower numerical cost.
DOI: --
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期刊: --
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