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

02/11/2019
by   Or Sharir, et al.
0

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 a two-dimensional interacting spin model, showcasing the ability to obtain accurate results on larger system sizes than those currently accessible to neural-network quantum states.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/24/2021

Overcoming barriers to scalability in variational quantum Monte Carlo

The variational quantum Monte Carlo (VQMC) method received significant a...
research
09/30/2021

Variational learning of quantum ground states on spiking neuromorphic hardware

Recent research has demonstrated the usefulness of neural networks as va...
research
05/12/2021

Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks

Efficient sampling of complex high-dimensional probability densities is ...
research
03/21/2022

Hierarchical autoregressive neural networks for statistical systems

It was recently proposed that neural networks could be used to approxima...
research
03/19/2023

Provable Convergence of Variational Monte Carlo Methods

The Variational Monte Carlo (VMC) is a promising approach for computing ...
research
06/29/2023

NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry

Neural network quantum state (NNQS) has emerged as a promising candidate...
research
11/24/2016

Quantum Enhanced Inference in Markov Logic Networks

Markov logic networks (MLNs) reconcile two opposing schools in machine l...

Please sign up or login with your details

Forgot password? Click here to reset