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  • Letter
  • Open Access

Lattice real-time simulations with learned optimal kernels

Daniel Alvestad1, Alexander Rothkopf1, and Dénes Sexty2

  • 1Department of Mathematics and Physics, University of Stavanger, 4021 Stavanger, Norway
  • 2Institute of Physics, NAWI Graz, University of Graz, Universitätsplatz 5, Graz, Austria

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Phys. Rev. D 109, L031502 – Published 5 February, 2024

DOI: https://doi.org/10.1103/PhysRevD.109.L031502

Abstract

We present a simulation strategy for the real-time dynamics of quantum fields, inspired by reinforcement learning. It builds on the complex Langevin approach, which it amends with system-specific prior information, a necessary prerequisite to overcome this exceptionally severe sign problem. The optimization process underlying our machine-learning approach is made possible by deploying inherently stable solvers of the complex Langevin stochastic process and a novel optimality criterion derived from insight into so-called boundary terms. This conceptual and technical progress allows us to both significantly extend the range of real-time simulations in 1+1d scalar field theory beyond the state of the art and to avoid discretization artifacts that plagued previous real-time field theory simulations. Limitations of and promising future directions are discussed.

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Phys. Rev. D 109, L031502– Published 5 February, 2024
  • Received 20 October 2023
  • Accepted 4 January 2024
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