Topological Order in Deep State

TL;DR

This paper demonstrates that attention-based deep neural networks can effectively discover and characterize topologically ordered quantum states, such as fractional Chern insulators, through energy minimization and wavefunction analysis.

Contribution

It introduces a neural network variational Monte Carlo method capable of identifying topological order and extracting ground state degeneracy without prior knowledge.

Findings

Neural networks accurately find fractional Chern insulator ground states.

Method extracts topological degeneracy from a single wavefunction.

Neural network approach is versatile for strongly correlated phases.

Abstract

Topologically ordered states are among the most interesting quantum phases of matter that host emergent quasi-particles having fractional charge and obeying fractional quantum statistics. Theoretical study of such states is however challenging owing to their strong-coupling nature that prevents conventional mean-field treatment. Here, we demonstrate that an attention-based deep neural network provides an expressive variational wavefunction that discovers fractional Chern insulator ground states purely through energy minimization without prior knowledge and achieves remarkable accuracy. We introduce an efficient method to extract ground state topological degeneracy -- a hallmark of topological order -- from a single optimized real-space wavefunction in translation-invariant systems by decomposing it into different many-body momentum sectors. Our results establish neural network variational Monte Carlo as a versatile tool for discovering strongly correlated topological phases.