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  • Open Access

Machine learning non-Markovian two-level quantum noise spectroscopy

Juan M. Scarpetta1,2,*, John H. Reina1,2, and Morten Hjorth-Jensen2

  • 1Departament of Physics and Centre for Bioinformatics and Photonics—CIBioFi, Universidad del Valle, Cali 760032, Colombia
  • 2Department of Physics and Center for Computing in Science Education, University of Oslo, Oslo N-0316, Norway

  • *Contact author: scarpetta.juan@correounivalle.edu.co
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Phys. Rev. Research 7, 043285 – Published 11 December, 2025

DOI: https://doi.org/10.1103/2lzl-vpjd

Abstract

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 Feedforward 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.

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Phys. Rev. Research 7, 043285– Published 11 December, 2025
  • Received 6 June 2025
  • Accepted 8 November 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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