Estimation of the reduced density matrix and entanglement entropies using autoregressive networks

TL;DR

This paper introduces a neural network-based method to directly estimate reduced density matrices and entanglement entropies in quantum spin chains, demonstrating accuracy with the Ising model.

Contribution

The authors develop an autoregressive neural network architecture that efficiently computes density matrix elements and entanglement entropies from Monte Carlo simulations.

Findings

Successfully applied to the Ising chain for ground state entanglement entropy estimation.

Single training suffices for fixed discretization and lattice volume.

Method adaptable to other spin chains and thermal states.

Abstract

We present an application of autoregressive neural networks to Monte Carlo simulations of quantum spin chains using the correspondence with classical two-dimensional spin systems. We use a hierarchy of neural networks capable of estimating conditional probabilities of consecutive spins to evaluate elements of reduced density matrices directly. Using the Ising chain as an example, we calculate the continuum limit of the ground state's von Neumann and R\'enyi bipartite entanglement entropies of an interval built of up to 5 spins. We demonstrate that our architecture is able to estimate all the needed matrix elements with just a single training for a fixed time discretization and lattice volume. Our method can be applied to other types of spin chains, possibly with defects, as well as to estimating entanglement entropies of thermal states at non-zero temperature.