Interpretable machine-learning identification of the crossover from subradiance to superradiance in an atomic array

TL;DR

This paper uses interpretable machine learning to identify the transition between subradiance and superradiance in an atomic array, revealing the significance of next-nearest-neighbor interactions in quantum optical systems.

Contribution

It introduces an explainable machine learning approach to analyze long-range correlations and crossover phenomena in quantum many-body systems.

Findings

Next-nearest-neighbor couplings are as influential as nearest-neighbor ones.

Machine learning effectively identifies the subradiance-superradiance crossover.

The approach enhances understanding of correlations in quantum optical systems.

Abstract

Light-matter interacting quantum systems manifest strong correlations that lead to distinct cooperative spontaneous emissions of subradiance or superradiance. To demonstrate the essence of long-range correlations in such systems, we consider an atomic array under the resonant dipole-dipole interactions (RDDI) and apply an interpretable machine learning with the integrated gradients to identify the crossover between the subradiant and superradiant sectors. The machine shows that the next nearest-neighbor couplings in RDDI play as much as the roles of nearest-neighbor ones in determining the whole eigenspectrum within the training sets. Our results present the advantage of machine learning approach with explainable ability to reveal the underlying mechanism of correlations in quantum optical systems, which can be potentially applied to investigate many other strongly interacting quantum many-body systems.