Quantum Loop Topography for Machine Learning

TL;DR

This paper introduces quantum loop topography (QLT), a method to convert quantum many-body states into images for neural networks, enabling effective identification of topological phases like Chern insulators.

Contribution

The paper presents QLT as a novel approach for extracting non-local topological information from quantum states for machine learning classification.

Findings

Successfully distinguishes topological from trivial insulators

First to map topological phase diagrams using machine learning

High fidelity in classifying Chern insulators

Abstract

Despite rapidly growing interest in harnessing machine learning in the study of quantum many-body systems, training neural networks to identify quantum phases is a nontrivial challenge. The key challenge is in efficiently extracting essential information from the many-body Hamiltonian or wave function and turning the information into an image that can be fed into a neural network. When targeting topological phases, this task becomes particularly challenging as topological phases are defined in terms of non-local properties. Here we introduce quantum loop topography (QLT): a procedure of constructing a multi-dimensional image from the "sample" Hamiltonian or wave function by evaluating two-point operators that form loops at independent Monte Carlo steps. The loop configuration is guided by characteristic response for defining the phase, which is Hall conductivity for the cases at hand. Feeding QLT to a fully-connected neural network with a single hidden layer, we demonstrate that the architecture can be effectively trained to distinguish Chern insulator and fractional Chern insulator from trivial insulators with high fidelity. In addition to establishing the first case of obtaining a phase diagram with topological quantum phase transition with machine learning, the perspective of bridging traditional condensed matter theory with machine learning will be broadly valuable.