Probing non-Markovian quantum dynamics with data-driven analysis: Beyond "black-box" machine learning models

TL;DR

This paper introduces a data-driven method to analyze non-Markovian quantum dynamics, enabling the extraction of environmental characteristics and predictive modeling from experimental data, surpassing traditional heuristic or computationally intensive approaches.

Contribution

The authors develop a novel data-driven framework that captures key features of open quantum systems and reconstructs models of non-Markovian dynamics, improving analysis and prediction capabilities.

Findings

Effective in modeling various open quantum systems

Able to denoise quantum trajectories

Reconstructs system-environment interaction spectra

Abstract

A precise understanding of the influence of a quantum system's environment on its dynamics, which is at the heart of the theory of open quantum systems, is crucial for further progress in the development of controllable large-scale quantum systems. However, existing approaches to account for complex system-environment interaction in the presence of memory effects are either based on heuristic and oversimplified principles or give rise to computational difficulties. In practice, one can leverage on available experimental data and replace first-principles simulations with a data-driven analysis that is often much simpler. Inspired by recent advances in data analysis and machine learning, we propose a data-driven approach to the analysis of the non-Markovian dynamics of open quantum systems. Our method allows, on the one hand, capturing the most important characteristics of open quantum systems such as the effective dimension of the environment and the spectrum of the joint system-environment quantum dynamics, and, on the other hand, reconstructing a predictive model of non-Markovian quantum dynamics, and denoising the measured quantum trajectories. We demonstrate the performance of the proposed approach with various models of open quantum systems, including a qubit coupled with a finite environment, a spin-boson model, and the damped Jaynes-Cummings model.