Learning the dynamics of Markovian open quantum systems from experimental data

TL;DR

This paper introduces a Bayesian Markov Chain Monte Carlo algorithm to identify Lindblad master equation models of open quantum systems from experimental data, enabling automated and interpretable system characterization.

Contribution

The authors develop a novel MCMC-based method for extracting compatible quantum dynamical models from experimental data, improving model fitting and interpretability.

Findings

Successfully benchmarks on quantum optics experiments with single and paired emitters.

Retrieves minimal models that fit experimental data better than previous approaches.

Enables automated, diverse, and tomographically incomplete data analysis for quantum systems.

Abstract

We present a Bayesian algorithm to identify generators of open quantum system dynamics, described by a Lindblad master equation, that are compatible with measured experimental data. The algorithm, based on a Markov Chain Monte Carlo approach, assumes the energy levels of the system are known and outputs a ranked list of interpretable master equation models that produce predicted measurement traces that closely match experimental data. We benchmark our algorithm on quantum optics experiments performed on single and pairs of quantum emitters. The latter case opens the possibility of cooperative emission effects and additional complexity due to the possible interplay between photon and phonon influences on the dynamics. Our algorithm retrieves various minimal models that are consistent with the experimental data, and which can provide a closer fit to measured data than previously suggested and physically expected approximate models. Our results represent an important step towards automated systems characterisation with an approach that is capable of working with diverse and tomographically incomplete input data. This may help with the development of theoretical models for unknown quantum systems as well as providing scientists with alternative interpretations of the data that they might not have originally envisioned and enabling them to challenge their original hypotheses.