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arXiv 2012.07063quant-phcond-mat.dis-nn
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基于强化学习的量子多体晶格模型的基态

Ground States of Quantum Many Body Lattice Models via Reinforcement Learning

Willem Gispen, Austen Lamacraft

摘要:

我们介绍了强化学习(RL)公式的问题,找到基态的多体量子力学模型定义在一个格子。我们表明,随机哈密顿-那些没有一个符号的问题-有一个自然的分解成随机动力学和潜在的奖励函数。映射到RL的开发为连续和离散时间,在前一种情况下的广义Feynman-Kac公式和随机表示的薛定谔方程在后者的基础上。我们讨论了这种映射的神经表示的量子态的应用,拼写出的优势,基于直接表示的系统的波函数的方法。

英文摘要:

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.

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