ACFlow: An open source toolkit for analytical continuation of quantum Monte Carlo data

TL;DR

ACFlow is an open source toolkit that implements multiple algorithms for analytical continuation of noisy quantum Monte Carlo data, facilitating the extraction of real-frequency spectral functions.

Contribution

This paper introduces ACFlow, a comprehensive open source toolkit that integrates three primary analytical continuation algorithms for quantum Monte Carlo data.

Findings

Demonstrates the toolkit's effectiveness through four example applications.

Shows flexibility and ease of use of the ACFlow toolkit.

Provides detailed implementation and usage instructions.

Abstract

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.