Year: 2025
Type: article
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.
Source: Physical Review Research
DOI: 10.1103/2lzl-vpjd
Institutions: University of Oslo, Universidad del Valle
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,625
OpenAlex ID: W4417261385