Optimal representation of the bath response function & fast calculation of influence functional coefficients in open quantum systems with BATHFIT 1

TL;DR

This paper introduces BATHFIT 1, an open-source MATLAB tool that efficiently fits bath response functions in open quantum systems, enabling accurate and computationally efficient simulations of system dynamics.

Contribution

The paper presents a novel MATLAB program that optimally fits bath response functions and calculates influence functional coefficients using advanced decomposition techniques.

Findings

Accurately fits bath response functions with minimal parameters.

Efficiently computes influence functional coefficients for various spectral distributions.

Utilizes Pade decomposition for improved initial parameter estimation.

Abstract

Today's most popular techniques for accurately calculating the dynamics of the reduced density operator in an open quantum system, either require, or gain great computational benefits, from representing the bath response function a(t) in the form a(t)={\Sigma}_k^K p_k e^{O_k t} . For some of these techniques, the number of terms in the series K plays the lead role in the computational cost of the calculation, and is therefore often a limiting factor in simulating open quantum system dynamics. We present an open source MATLAB program called BATHFIT 1, whose input is any spectral distribution functions J(w) or bath response function, and whose output attempts to be the set of parameters {p_k,w_k}_k=1^K such that for a given value of K, the series {\Sigma}_k^k p_k e^{O_k t} is as close as possible to a(t). This should allow the user to represent a(t) as accurately as possible with as few parameters as possible. The program executes non-linear least squares fitting, and for a very wide variety of spectral distribution functions, competent starting parameters are used for these fits. For most forms of J(w), these starting parameters, and the exact a(t) corresponding to the given J(w), are calculated using the recent Pade decomposition technique - therefore this program can also be used to merely implement the Pade decomposition for these spectral distribution functions; and it can also be used just to efficiently and accurately calculate a(t) for any given J(w) . The program also gives the J(w) corresponding to a given a(t), which may allow one to assess the quality (in the w-domain) of a representation of a(t) being used. Finally, the program can calculate the discretized influence functional coefficients for any J(w), and this is computed very efficiently for most forms of J(w) by implementing the recent technique published in [Quantum Physics Letters (2012) 1 (1) pg. 35].