Flow-based sampling for multimodal and extended-mode distributions in lattice field theory

TL;DR

This paper develops flow-based generative models tailored for sampling from multimodal and extended-mode distributions in lattice field theory, improving the efficiency of configuration generation in complex quantum systems.

Contribution

It introduces new training and architecture methods for flow models targeting multimodal and extended distributions, applied to scalar field theories in symmetry-broken phases.

Findings

Flow-based models effectively sample multimodal distributions.

Composite algorithms improve sampling efficiency.

Methods are demonstrated on 2D scalar field theories.

Abstract

Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In this paper, we present a set of training- and architecture-based methods to construct flow models for targets with multiple separated modes (i.e.~vacua) as well as targets with extended/continuous modes. We demonstrate the application of these methods to modeling two-dimensional real and complex scalar field theories in their symmetry-broken phases. In this context we investigate different flow-based sampling algorithms, including a composite sampling algorithm where flow-based proposals are occasionally augmented by applying updates using traditional algorithms like HMC.