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Applications of Maxent to Quantum Monte Carlo

Richard N. Silver, Devinderjit Singh Sivia, James E. Gubernatis, Mark Jarrell

Published Jan 1, 1991• 0 citations
Publisher

Abstract

We consider the application of maximum entropy methods to the analysis of data produced by computer simulations. The focus is the calculation of the dynamical properties of quantum many-body systems by Monte Carlo methods, which is termed the "Analytic Continuation Problem." For the Anderson model of dilute magnetic impurities in metals, we obtain spectral functions and transport coefficients which obey "Kondo Universality."

DOI
10.1007/978-94-011-3460-6_10

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We consider the application of maximum entropy methods to the analysis of data produced by computer simulations. The focus is the calculation of the dynamical properties of quantum many-body systems by Monte Carlo methods, which is termed the "Analytic Continuation Problem." For the Anderson model of dilute magnetic impurities in metals, we obtain spectral functions and transport coefficients which obey "Kondo Universality."
An outstanding problem in the simulation of condensed matter phenomena is how to obtain dynamical information. We consider the numerical analytic continuation of imaginary time Quantum Monte Carlo data to obtain real frequency spectral functions. We suggest an image reconstruction approach which has been widely applied to data analysis in experimental research, the Maximum Entropy Method (MaxEnt). We report encouraging preliminary results for the Fano-Anderson model of an impurity state in a continuum. The incorporation of additional prior information, such as sum rules and asymptotic behavior, can be expected to significantly improve results. We also compare MaxEnt to alternative methods. 17 refs., 4 figs.
An outstanding problem in the simulation of condensed-matter phenomena is how to obtain dynamical information. We consider the numerical analytic continuation of imaginary-time quantum Monte Carlo data to obtain real-frequency spectral functions. This is an extremely ill-posed problem similar to the inversion of a Laplace transform. We suggest an image-reconstruction approach, which has been widely applied to data analysis in experimental research. Specifically, we apply the maximum-entropy method (ME) to the analytic continuation of quantum Monte Carlo data. We report encouraging preliminary results for the Fano-Anderson model of an impurity state in a continuum. The incorporation of additional prior information, such as sum rules and asymptotic behavior, can be expected to significantly improve results. We compare (ME) to alternative methods. We also discuss statistical error propagation for the analytic continuation problem via the likelihood function, which is independent of the choice of image-reconstruction method. This includes the sensitivity of the data to structure in the spectral function, the optimization of Monte Carlo simulations, and how to incorporate covariance in the statistical errors of the Monte Carlo method.
We report the details of an application of the method of maximum entropy to the extraction of spectral and transport properties from the imaginary-time correlation functions generated from quantum Monte Carlo simulations of the nondegenerate, symmetric, single-impurity Anderson model. We find that these physical properties are approximately universal functions of temperature and frequency when these parameters are scaled by the Kondo temperature. We also found that important details for successful extractions included the generation of statistically independent, Gaussian-distributed data, and a good choice of a default model to represent the state of our prior knowledge about the result in the absence of data. We suggest that our techniques are not restricted to the Hamiltonian and quantum Monte Carlo algorithm used here, but that maximum entropy and these techniques lay the general groundwork for the extraction of dynamical information from imaginary-time data generated by other quantum Monte Carlo simulations.
A new algorithm for analytic continuation of noisy quantum Monte Carlo (QMC) data from the Matsubara domain to real frequencies is proposed. Unlike the widely used maximum-entropy (MaxEnt) procedure, our method is linear with respect to input data and can therefore be applied to off-diagonal components of a thermal Green's function, or to a self-energy function. The latter possibility is used to analyze QMC results for the half-filled single-band Hubbard model on a Bethe lattice at a low temperature. Our method qualitatively resolves peaks near the inner edges of the Hubbard bands in the vicinity of a Mott transition, whereas a MaxEnt procedure does not. An existence of such structures has been clearly established before in a high-precision D-DMRG calculation by Karski et al. We also analyze a stability of the new method subject to changes of adjustable parameters.
A new method of analytic continuation from a Matsubara single-particle Green's function to a spectral function is presented. We recast this problem onto a new one where we dynamically minimize a suitably defined potential. Our method allows the imposition of physical constraints such as smoothness and adherence to the sum rule. The method is applied to the symmetric Anderson impurity model. We show how the spectral function changes with the Kondo temperature TK{T}_{K}TK​, the hybridization witdth \ensuremath{\Delta}, and the Coulomb potential U.
This thesis is mainly a computational work studying the analytical continuation of Green’s functions using the Maximum Entropy method. A strongly correlated electron system is described with the single-band Hubbard model and paramagnetic solutions are studied using Dynamic Mean Field Theory on a Bethe lattice. Continuous Time Quantum Monte Carlo is used as Impurity solver, for the infinite Anderson model at a finite temperature, to obtain the Matsubara single-particle Green’s function propagator. Both metallic and insulating spectral functions are obtained using the Maximum Entropy Method. General properties of the Maximum Entropy Method as an analytic continuation method from imaginary to real time are also discussed.