论文标题
Oracle指导的变异自回归网络的Worldline算法
Worldline algorithm by oracle-guided variational autoregressive network
论文作者
论文摘要
变异自回旋网络扩展到量子分区函数的欧几里得路径积分表示。一个必要的挑战是，由于路径积分中的周期性边界条件，通过自回归网络将样品生成的顺序过程转化为非局部约束。为此目的设计了一个附加甲骨文，该目的是在发生时准确地识别和失速不可行的配置。 Oracle启用符合周期性边界条件的无排斥采样。作为演示，应用了Oracle引导的自回归网络，以在有限温度下获得量子自旋链的变化解，该量子的系统尺寸相对较大，时间切片数和时间切片数，并有效地计算热力学数量。
The variational autoregressive network is extended to the Euclidean path integral representation of quantum partition function. An essential challenge is adapting the sequential process of sample generation by an autoregressive network to a nonlocal constraint due to the periodic boundary condition in path integral. An add-on oracle is devised for this purpose, which accurately identifies and stalls unviable configurations as soon as they occur. The oracle enables rejection-free sampling conforming to the periodic boundary condition. As a demonstration, the oracle-guided autoregressive network is applied to obtain variational solutions of quantum spin chains at finite temperatures with relatively large system sizes and numbers of time slicing, and to efficiently compute thermodynamic quantities.
