Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics

TL;DR

This paper reviews recent advances in machine learning-based sampling techniques motivated by lattice quantum chromodynamics, highlighting challenges and potential for transformative scientific calculations in fundamental physics.

Contribution

It discusses the development of ML algorithms tailored for lattice QCD, addressing unique scientific challenges and demonstrating potential benefits for first-principles physics computations.

Findings

ML models are being adapted for complex symmetries in lattice QCD

Scaling ML algorithms to supercomputers is a key challenge

ML-based sampling promises to revolutionize intractable physics calculations

Abstract

Sampling from known probability distributions is a ubiquitous task in computational science, underlying calculations in domains from linguistics to biology and physics. Generative machine-learning (ML) models have emerged as a promising tool in this space, building on the success of this approach in applications such as image, text, and audio generation. Often, however, generative tasks in scientific domains have unique structures and features -- such as complex symmetries and the requirement of exactness guarantees -- that present both challenges and opportunities for ML. This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics. Enabling calculations of the structure and interactions of matter from our most fundamental understanding of particle physics, lattice quantum chromodynamics is one of the main consumers of open-science supercomputing worldwide. The design of ML algorithms for this application faces profound challenges, including the necessity of scaling custom ML architectures to the largest supercomputers, but also promises immense benefits, and is spurring a wave of development in ML-based sampling more broadly. In lattice field theory, if this approach can realize its early promise it will be a transformative step towards first-principles physics calculations in particle, nuclear and condensed matter physics that are intractable with traditional approaches.