Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems

TL;DR

This paper introduces a deep learning approach to identify and analyze various quantum phases and phase transitions in disordered two-dimensional electron systems, addressing the challenge of characterizing eigenfunctions in such complex systems.

Contribution

The study presents a novel deep learning method to classify quantum phases and detect phase transitions in random electron systems, enhancing understanding of disordered quantum matter.

Findings

Deep learning successfully captures eigenfunction features.

Identification of localization-delocalization transitions.

Analysis of disordered Chern insulator and Anderson insulator transitions.

Abstract

Random electron systems show rich phases such as Anderson insulator, diffusive metal, quantum and anomalous quantum Hall insulator, Weyl semimetal, as well as strong/weak topological insulators. Eigenfunctions of each matter phase have specific features, but due to the random nature of systems, judging the matter phase from eigenfunctions is difficult. Here we propose the deep learning algorithm to capture the features of eigenfunctions. Localization-delocalization transition as well as disordered Chern insulator-Anderson insulator transition is discussed.