# Deep autoregressive models for the efficient variational simulation of many-body quantum systems

## TL;DR

This paper introduces a novel neural network architecture for simulating many-body quantum systems that allows for efficient, exact sampling, overcoming the limitations of traditional Markov Chain Monte Carlo methods.

## Contribution

The authors propose a specialized autoregressive neural network model enabling efficient and exact sampling of quantum states, enhancing scalability and accuracy.

## Key findings

- Achieved accurate results on larger 2D spin models
- Overcame sampling limitations of traditional neural-network states
- Demonstrated improved scalability in quantum simulations

## Abstract

Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states. In practical applications, neural-network states inherit numerical schemes used in Variational Monte Carlo, most notably the use of Markov-Chain Monte-Carlo (MCMC) sampling to estimate quantum expectations. The local stochastic sampling in MCMC caps the potential advantages of neural networks in two ways: (i) Its intrinsic computational cost sets stringent practical limits on the width and depth of the networks, and therefore limits their expressive capacity; (ii) Its difficulty in generating precise and uncorrelated samples can result in estimations of observables that are very far from their true value. Inspired by the state-of-the-art generative models used in machine learning, we propose a specialized Neural Network architecture that supports efficient and exact sampling, completely circumventing the need for Markov Chain sampling. We demonstrate our approach for two-dimensional interacting spin models, showcasing the ability to obtain accurate results on larger system sizes than those currently accessible to neural-network quantum states.