Detecting and identifying 2D symmetry-protected topological, symmetry-breaking and intrinsic topological phases with modular matrices via tensor-network methods

TL;DR

This paper uses tensor-network methods to compute modular matrices, enabling the detection and classification of 2D symmetry-protected topological, symmetry-breaking, and intrinsic topological phases, including phase transitions.

Contribution

It introduces a tensor-network approach to compute modular matrices for identifying and distinguishing various 2D topological and SPT phases, unifying their characterization.

Findings

Modular matrices identify nontrivial SPT phases.

Detection of phase transitions between SPT and symmetry-breaking phases.

Unified characterization of gapped phases using modular matrices.

Abstract

Symmetry-protected topological (SPT) phases exhibit nontrivial order if symmetry is respected but are adiabatically connected to the trivial product phase if symmetry is not respected. However, unlike the symmetry-breaking phase, there is no local order parameter for SPT phases. Here we employ a tensor-network method to compute the topological invariants characterized by the simulated modular $S$ and $T$ matrices to study transitions in a few families of two-dimensional (2D) wavefunctions which are $\mathbb{Z}_N$ ($N=2\, \&3$) symmetric. We find that in addition to the topologically ordered phases, the modular matrices can be used to identify nontrivial SPT phases and detect transitions between different SPT phases as well as between symmetric and symmetry-breaking phases. Therefore, modular matrices can be used to characterize various types of gapped phases in a unifying way.