Lattice real-time simulations with learned optimal kernels
- URL: http://arxiv.org/abs/2310.08053v1
- Date: Thu, 12 Oct 2023 06:01:01 GMT
- Title: Lattice real-time simulations with learned optimal kernels
- Authors: Daniel Alvestad, Alexander Rothkopf, D\'enes Sexty
- Abstract summary: 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.
- Score: 49.1574468325115
- License: http://creativecommons.org/licenses/by/4.0/
- 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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