Computing quantum entanglement with machine learning

TL;DR

This paper introduces a machine learning approach using deep generative models to efficiently compute quantum entanglement measures, surpassing traditional Monte Carlo methods especially in high-dimensional lattice systems.

Contribution

It presents a novel machine learning framework that significantly improves the accuracy and efficiency of entanglement calculations in quantum field theories.

Findings

Deep generative models outperform Monte Carlo algorithms.

High-precision Renyi entropy estimates in 3D lattices.

New flow-based sampling paradigm for lattice defects.

Abstract

Entanglement calculations in quantum field theories are extremely challenging and typically rely on the replica trick, where the problem is rephrased in a study of defects. We demonstrate that the use of deep generative models drastically outperforms standard Monte Carlo algorithms. Remarkably, such a machine-learning method enables high-precision estimates of R\'enyi entropies in three dimensions for very large lattices. Moreover, we propose a new paradigm for studying lattice defects with flow-based sampling.