论文标题
量子的基态通过增强学习许多身体晶格模型
Ground States of Quantum Many Body Lattice Models via Reinforcement Learning
论文作者
论文摘要
我们介绍了在晶格上定义的多体量子机械模型的基态问题的加强学习（RL）公式。我们表明，没有迹象的哈密顿人（那些没有标志问题的人）自然地分解了随机动力学和代表奖励功能的潜力。基于以前情况下的广义feynman-kac公式以及后者的schrödinger方程的随机表示，映射到RL的连续和离散时间既是连续的和离散的。我们讨论了该映射到量子状态的神经表示的应用，并基于基于系统波函数的直接表示，阐明了优于方法的优势。
We introduce reinforcement learning (RL) formulations of the problem of finding the ground state of a many-body quantum mechanical model defined on a lattice. We show that stoquastic Hamiltonians - those without a sign problem - have a natural decomposition into stochastic dynamics and a potential representing a reward function. The mapping to RL is developed for both continuous and discrete time, based on a generalized Feynman-Kac formula in the former case and a stochastic representation of the Schrödinger equation in the latter. We discuss the application of this mapping to the neural representation of quantum states, spelling out the advantages over approaches based on direct representation of the wavefunction of the system.
