2016
DOI: 10.1103/physrevb.94.205123
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Detecting topological order with ribbon operators

Abstract: We introduce a numerical method for identifying topological order in two-dimensional models based on one-dimensional bulk operators. The idea is to identify approximate symmetries supported on thin strips through the bulk that behave as string operators associated to an anyon model. We can express these ribbon operators in matrix product form and define a cost function that allows us to efficiently optimize over this ansatz class. We test this method on spin models with abelian topological order by finding rib… Show more

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Cited by 18 publications
(24 citation statements)
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“…One would first need to obtain the MPOs that create anyons on the PEPS, possibly using a generalization of the algorithm introduced in Ref. [36]. Given these anyon MPOs, one could attempt to find symmetry MPOs that permute them appropriately.…”
Section: Discussionmentioning
confidence: 99%
“…One would first need to obtain the MPOs that create anyons on the PEPS, possibly using a generalization of the algorithm introduced in Ref. [36]. Given these anyon MPOs, one could attempt to find symmetry MPOs that permute them appropriately.…”
Section: Discussionmentioning
confidence: 99%
“…Although the algorithms we discuss here are designed for finding MPS ground states, they can be adaped to simulate time evolution [49,50], find Gibbs states [51], or optimise other operators acting on a statespace of interest [52].…”
Section: Tensor Network Algorithmsmentioning
confidence: 99%
“…Section 5 is devoted to discussion of future directions for this work that add the additional constraint that the Hamiltonian is local, and we discuss the relationship to the notions of topological order and topological quantum codes. In particular we show how recent numerical methods for studying quantum many-body systems [35] could leverage the bounds presented here to provide certificates of the topological degeneracy of certain quantum systems.…”
Section: Resultsmentioning
confidence: 98%