Analysis of autocorrelation times in Neural Markov Chain Monte Carlo simulations

TL;DR

This paper investigates autocorrelation times in Neural MCMC simulations, proposing new training methods and symmetry considerations to improve sampling efficiency, with insights gained from small system analyses and applications to larger models.

Contribution

It introduces a new loss function inspired by analytical results, explores symmetry effects, and incorporates partial heat-bath updates to enhance Neural MCMC performance.

Findings

New loss function impacts autocorrelation times.

Imposing symmetries affects sampling efficiency.

Partial heat-bath updates improve training quality.

Abstract

We provide a deepened study of autocorrelations in Neural Markov Chain Monte Carlo (NMCMC) simulations, a version of the traditional Metropolis algorithm which employs neural networks to provide independent proposals. We illustrate our ideas using the two-dimensional Ising model. We discuss several estimates of autocorrelation times in the context of NMCMC, some inspired by analytical results derived for the Metropolized Independent Sampler (MIS). We check their reliability by estimating them on a small system where analytical results can also be obtained. Based on the analytical results for MIS we propose a new loss function and study its impact on the autocorelation times. Although, this function's performance is a bit inferior to the traditional Kullback-Leibler divergence, it offers two training algorithms which in some situations may be beneficial. By studying a small, $4 \times 4$, system we gain access to the dynamics of the training process which we visualize using several observables. Furthermore, we quantitatively investigate the impact of imposing global discrete symmetries of the system in the neural network training process on the autocorrelation times. Eventually, we propose a scheme which incorporates partial heat-bath updates which considerably improves the quality of the training. The impact of the above enhancements is discussed for a $16 \times 16$ spin system. The summary of our findings may serve as a guidance to the implementation of Neural Markov Chain Monte Carlo simulations for more complicated models.