Statistically induced topological phase transitions in a one-dimensional superlattice anyon-Hubbard model

TL;DR

This paper explores how varying the statistical angle in a one-dimensional superlattice anyon-Hubbard model induces topological phase transitions, revealing new insights into topological phases and their transitions.

Contribution

It introduces the concept of statistically induced topological phase transitions in a superlattice anyon-Hubbard model, supported by numerical analysis and theoretical explanation.

Findings

Identification of topological anyon-Mott insulator via topological invariant and edge modes

Demonstration of topological phase transition driven solely by statistical angle variation

Topological phases can appear in various superlattice anyon-Hubbard models

Abstract

We theoretically investigate topological properties of the one-dimensional superlattice anyon-Hubbard model, which can be mapped to a superlattice bose-Hubbard model with an occupation-dependent phase factor by fractional Jordan-Wigner transformation. The topological anyon-Mott insulator is identified by topological invariant and edge modes using exact diagonalization and density-matrix renormalization-group algorithm. When only the statistical angle is varied and all other parameters are fixed, a statistically induced topological phase transition can be realized, which provides new insights into the topological phase transitions. What's more, we give an explanation of the statistically induced topological phase transition. The topological anyon-Mott phases can also appear in a variety of superlattice anyon-Hubbard models.