Learning spectral density functions in open quantum systems
Abstract
Spectral density functions quantify how environmental modes couple to quantum systems and govern their open dynamics. Inferring such frequency-dependent functions from time-domain measurements is an ill-conditioned inverse problem. Here, we use exactly solvable spin-boson models with pure-dephasing and amplitude-damping channels to reconstruct spectral density functions from noisy simulated data. First, we introduce a parameter estimation approach based on machine learning regressors to infer Lorentzian and Ohmic-like spectral density parameters, quantifying robustness to noise. Second, we show that a cosine transform inversion yields a physics-consistent spectral prior estimation, which is refined by a constrained neural network enforcing positivity and correct asymptotic behaviour. Our neural network framework robustly reconstructs structured spectral densities by filtering simulated noisy signals and learning general functional dependencies.
Cite
@article{arxiv.2602.24056,
title = {Learning spectral density functions in open quantum systems},
author = {Felipe Peleteiro and João Victor Shiguetsugo Kawanami Lima and Pedro Marcelo Prado and Felipe Fernandes Fanchini and Ariel Norambuena},
journal= {arXiv preprint arXiv:2602.24056},
year = {2026}
}
Comments
8 pages, 4 figures