论文标题
加固学习的量子基态
Quantum Ground States from Reinforcement Learning
论文作者
论文摘要
找到量子机械系统的基态可以作为最佳控制问题。在此公式中，选择了最佳控制过程的漂移，以匹配假想时间schrödinger方程解的Feynman-KAC（FK）表示中的路径分布。这提供了一个变异原理，可用于增强漂移神经表示。我们的方法是替换路径积分蒙特卡洛，学习FK轨迹的最佳重要性采样器。我们证明了我们的方法适用于几个单粒物理学的几个问题。
Finding the ground state of a quantum mechanical system can be formulated as an optimal control problem. In this formulation, the drift of the optimally controlled process is chosen to match the distribution of paths in the Feynman--Kac (FK) representation of the solution of the imaginary time Schrödinger equation. This provides a variational principle that can be used for reinforcement learning of a neural representation of the drift. Our approach is a drop-in replacement for path integral Monte Carlo, learning an optimal importance sampler for the FK trajectories. We demonstrate the applicability of our approach to several problems of one-, two-, and many-particle physics.
