We develop machine learning models for the automated characterization of quantum noise spectroscopy for non-Hermitian two-level systems. We use the Random Forest, Support Vector and Feed-Forward Neural Network regression algorithms to perform a highly accurate regression of the two-level system-bath coupling strength. High accuracy Ohmicity classification was implemented to provide a complete characterization of the spectral density function. We define a time-averaged trace-distance metric to feed the machine learning algorithms which, together with numerically exact populations as inputs, produce a highly accurate non-Markovian regression spanning the transition from fast to slow baths and from weak to strong coupling regimes of the interaction. The dynamics database of the non-Hermitian systems has been built up within the independent spin-boson and pure dephasing model.
@article{arxiv.2506.06555,
title = {Machine learning non-Markovian two-level quantum noise spectroscopy},
author = {Juan Manuel Scarpetta and John Henry Reina and Morten Hjorth-Jensen},
journal= {arXiv preprint arXiv:2506.06555},
year = {2025}
}