Machine learning non-Markovian two-level quantum noise spectroscopy

• URL: http://arxiv.org/abs/2506.06555v1
• Date: Fri, 06 Jun 2025 22:05:19 GMT
• Title: Machine learning non-Markovian two-level quantum noise spectroscopy
• Authors: Juan Manuel Scarpetta, John Henry Reina, Morten Hjorth-Jensen,
• Abstract summary: We develop machine learning models for the automated characterization of quantum noise spectroscopy for non-Hermitian two-level systems.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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