Learning disordered topological phases by statistical recovery of symmetry

Abstract

Understanding the phases of a model usually requires knowledge of their characteristic features, which are nonlocal in topologically ordered systems. Here, the authors reframe the phase classification problem in disordered topological superconductors as a data-driven task, motivated by the recent surge of interest in the application of machine-learning techniques including deep learning. It is demonstrated that an artificial neural network learns to extract the essence of the clean system and successfully distinguishes phases even under disorder by statistical recovery of translational symmetry.