Loop-Free Tensor Networks for High-Energy Physics

TL;DR

This paper reviews loop-free tensor network methods and their application to high-energy physics, especially lattice gauge theories, offering alternatives to Monte Carlo methods hindered by the sign problem.

Contribution

It specializes in applying loop-free tensor network techniques to high-energy physics problems, expanding their use beyond condensed matter and quantum information.

Findings

Tensor networks can effectively study lattice gauge theories.

Loop-free tensor networks avoid the sign problem in certain regimes.

The approach broadens computational tools in high-energy physics.

Abstract

This brief review introduces the reader to tensor network methods, a powerful theoretical and numerical paradigm spawning from condensed matter physics and quantum information science and increasingly exploited in different fields of research, from artificial intelligence to quantum chemistry. Here, we specialise our presentation on the application of loop-free tensor network methods to the study of High-Energy Physics (HEP) problems and, in particular, to the study of lattice gauge theories where tensor networks can be applied in regimes where Monte Carlo methods are hindered by the sign problem.