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.

翻译：我们提出了一种受强化学习启发的量子场实时动力学模拟策略。该方法建立在复朗之万方法的基础上，通过添加系统特定的先验信息来修正该方法，这是克服极端符号问题的必要前提。我们机器学习方法背后的优化过程之所以可行，是因为使用了本质上稳定的复朗之万随机过程求解器，以及从所谓的边界项洞察中推导出的新颖最优性准则。这一概念和技术上的进步使我们既能将1+1维标量场理论中实时模拟的范围显著扩展到现有技术水平之上，又能避免之前实时场论模拟中困扰研究的离散化伪影。文中还讨论了该方法的局限性及有前景的未来方向。