Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory
This addresses a bottleneck in quantum field theory calculations for physicists, but it is incremental as it builds on existing reinforcement learning methods applied to a new domain.
The paper tackles the problem of performing analytic continuation for one-loop diagrams in quantum field theory, which is necessary to extract quantities like spectral densities, by using deep reinforcement learning to deform integration contours, showing promise for applications in non-perturbative 2-point function computations.
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.