Machine Learning Topological States

TL;DR

This paper demonstrates that artificial neural networks can efficiently and exactly represent topological quantum states, including ground and excited states, across various models, and can identify topological phase transitions.

Contribution

It introduces a method to represent topological states with short-range neural networks and applies reinforcement learning to study complex topological phases.

Findings

Neural networks can exactly represent topological ground states with linearly scaling parameters.

Short-range neural networks can describe excited states with anyons and their statistics.

Neural networks can find topological states in non-integrable models and detect phase transitions.

Abstract

Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics--- the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases we show rigorously that the topological ground states can be represented by short-range neural networks in an \textit{exact} and \textit{efficient} fashion---the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only \textit{linearly} with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with abelain anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of non-integrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.