循环神经网络波函数
Recurrent Neural Network Wave Functions
摘要：
人工智能革命中出现的一项核心技术是循环神经网络（RNN）。其独特的基于序列的架构提供了一个易于处理的似然估计和稳定的训练范式，这种组合在自然语言处理和神经机器翻译方面取得了许多惊人的进步。这种架构也成为变分波函数的良好候选者，其中RNN参数被调谐以学习量子哈密顿量的近似基态。在本文中，我们证明了RNN表示多体波函数的能力，使用随机方法优化变分参数。在这些变分波函数的其他吸引人的特征中，它们的自回归性质允许通过提供独立样本来有效地计算物理估计。我们证明了RNN波函数的有效性，通过计算基态能量，关联函数，和纠缠熵的几个量子自旋模型感兴趣的凝聚态物理学家在一个和两个空间维度。
英文摘要：
A core technology that has emerged from the artificial intelligence revolution is the recurrent neural network (RNN). Its unique sequence-based architecture provides a tractable likelihood estimate with stable training paradigms, a combination that has precipitated many spectacular advances in natural language processing and neural machine translation. This architecture also makes a good candidate for a variational wave function, where the RNN parameters are tuned to learn the approximate ground state of a quantum Hamiltonian. In this paper, we demonstrate the ability of RNNs to represent several many-body wave functions, optimizing the variational parameters using a stochastic approach. Among other attractive features of these variational wave functions, their autoregressive nature allows for the efficient calculation of physical estimators by providing independent samples. We demonstrate the effectiveness of RNN wave functions by calculating ground state energies, correlation functions, and entanglement entropies for several quantum spin models of interest to condensed matter physicists in one and two spatial dimensions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
