Dual formulation of the maximum entropy method applied to analytic continuation of quantum Monte Carlo data

TL;DR

This paper introduces a dual Newton optimization algorithm for the maximum entropy method (MEM) to improve the analytic continuation of quantum Monte Carlo data, providing better estimates and error bounds especially in noisy conditions.

Contribution

The paper develops a new dual Newton optimization algorithm for MEM that overcomes theoretical issues of Bryan's method and demonstrates improved performance on complex quantum physics data.

Findings

Dual Newton algorithm yields more accurate estimates with noise.

Improved error bounds for the analytic continuation process.

Application to quantum Monte Carlo data confirms physical features.

Abstract

Many fields of physics use quantum Monte Carlo techniques, but struggle to estimate dynamic spectra via the analytic continuation of imaginary-time quantum Monte Carlo data. One of the most ubiquitous approaches to analytic continuation is the maximum entropy method (MEM). We supply a dual Newton optimization algorithm to be used within the MEM and provide analytic bounds for the algorithm's error. The MEM is typically used with Bryan's controversial algorithm [Rothkopf, "Bryan's Maximum Entropy Method" Data 5.3 (2020)]. We present new theoretical issues that are not yet in the literature. Our algorithm has all the theoretical benefits of Bryan's algorithm without these theoretical issues. We compare the MEM with Bryan's optimization to the MEM with our dual Newton optimization on test problems from lattice quantum chromodynamics and plasma physics. These comparisons show that in the presence of noise the dual Newton algorithm produces better estimates and error bars; this indicates the limits of Bryan's algorithm's applicability. We use the MEM to investigate authentic quantum Monte Carlo data for the uniform electron gas at warm dense matter conditions and further substantiate the roton-type feature in the dispersion relation.