https://doi.org/10.1140/epjs/s11734-025-01954-9
Regular Article
A machine learning based approach to the identification of spectral densities in quantum open systems
1
Centre for Quantum Materials and Technologies, School of Mathematics and Physics, Queen’s University Belfast, University Road, BT7 1NN, Belfast, UK
2
Dipartimento di Fisica e Astronomia “Ettore Majorana”, Università di Catania, Via S. Sofia 64, 95123, Catania, Italy
3
Dipartimento di Fisica “Aldo Pontremoli”, Università degli Studi di Milano, Via Celoria 16, 20133, Milan, Italy
4
Quantum Theory group, Dipartimento di Fisica e Chimica “Emilio Segrè”, Università degli Studi di Palermo, via Archirafi 36, 90123, Palermo, Italy
a
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b
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Received:
18
July
2025
Accepted:
5
September
2025
Published online:
21
September
2025
Abstract
We present a machine learning-based approach for characterising the environment that affects the dynamics of an open quantum system. We focus on the case of an exactly solvable spin-boson model, where the system-environment interaction, whose strength is encoded in the spectral density, induces pure dephasing. By using artificial neural networks trained on the Fourier-transformed time evolution of some observables of the system, we perform both classification—distinguishing sub-Ohmic, Ohmic, and super-Ohmic spectral densities—and regression—thus estimating key parameters of the spectral density function, when the latter is expressed through a power law. Our results demonstrate high classification accuracy and robust parameter estimation, highlighting the potential of machine learning as a powerful tool for probing environmental features in quantum systems and advancing quantum noise spectroscopy.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
