Optimal identification of non-Markovian environments for spin chains

TL;DR

This paper introduces a gradient-based method to identify environment correlations in non-Markovian spin chain systems by optimizing damping rate functions through system measurements, improving over differential approaches.

Contribution

It presents a novel gradient algorithm for identifying environment correlations in non-Markovian quantum systems using measurable system observables.

Findings

The algorithm effectively identifies damping rate functions.

Comparison shows improved performance over differential methods.

Applicable to two-qubit spin chains.

Abstract

Correlations of an environment are crucial for the dynamics of non-Markovian quantum systems, which may not be known in advance. In this paper, we propose a gradient algorithm for identifying the correlations in terms of time-varying damping rate functions in a time-convolution-less master equation for spin chains. By measuring time trace observables of the system, the identification procedure can be formulated as an optimization problem. The gradient algorithm is designed based on a calculation of the derivative of an objective function with respect to the damping rate functions, whose effectiveness is shown in a comparison to a differential approach for a two-qubit spin chain.