论文标题
ACFLOW：用于量子蒙特卡洛数据分析延续的开源工具包
ACFlow: An open source toolkit for analytical continuation of quantum Monte Carlo data
论文作者
论文摘要
分析延续的目的是建立单粒子或两粒子相关函数（例如Green的功能，自我能源函数和动态敏感性）的实际频谱表示，从有限温度量子Monte Carlo模拟中产生的嘈杂数据。它需要第一类Fredholm积分方程家族的数值解决方案，这确实是一项具有挑战性的任务。在本文中，提出了一个开源工具包（称为ACFLOW），用于量子蒙特卡洛数据的分析延续。我们首先简要介绍了分析延续问题。接下来，回顾了三种主要的分析延续算法，包括最大熵方法，随机分析延续和随机优化方法，如本工具包中所实现的。然后，我们详细阐述了此工具包的主要功能，实现细节和基本用法。最后，显示四个代表性示例证明了Acflow工具包的有用性和灵活性。
The purpose of analytical continuation is to establish a real frequency spectral representation of single-particle or two-particle correlation function (such as Green's function, self-energy function, and dynamical susceptibilities) from noisy data generated in finite temperature quantum Monte Carlo simulations. It requires numerical solutions of a family of Fredholm integral equations of the first kind, which is indeed a challenging task. In this paper, an open source toolkit (dubbed ACFlow) for analytical continuation of quantum Monte Carlo data is presented. We at first give a short introduction to the analytical continuation problem. Next, three primary analytical continuation algorithms, including maximum entropy method, stochastic analytical continuation, and stochastic optimization method, as implemented in this toolkit are reviewed. And then we elaborate major features, implementation details, and basic usage of this toolkit. Finally, four representative examples are shown to demonstrate usefulness and flexibility of the ACFlow toolkit.
