Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory

TL;DR

This paper introduces a deep reinforcement learning approach to perform contour deformations for analytic continuation of loop integrals in quantum field theory, aiding the computation of complex two-point functions.

Contribution

It presents a novel RL-based method for contour deformation in loop integrals, improving numerical analytic continuation in quantum field theory calculations.

Findings

RL agent successfully performs contour deformations on toy models.

Method shows potential for non-perturbative quantum field theory computations.

Promising results for integration in complex domains of loop integrals.

Abstract

In this paper we present a novel technique based on deep reinforcement learning that allows for numerical analytic continuation of integrals that are often encountered in one-loop diagrams in quantum field theory. In order to extract certain quantities of two-point functions, such as spectral densities, mass poles or multi-particle thresholds, it is necessary to perform an analytic continuation of the correlator in question. At one-loop level in Euclidean space, this results in the necessity to deform the integration contour of the loop integral in the complex plane of the square of the loop momentum, in order to avoid non-analyticities in the integration plane. Using a toy model for which an exact solution is known, we train a reinforcement learning agent to perform the required contour deformations. Our study shows great promise for an agent to be deployed in iterative numerical approaches used to compute non-perturbative 2-point functions, such as the quark propagator Dyson-Schwinger equation, or more generally, Fredholm equations of the second kind, in the complex domain.