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.

Quantum Physics (quant-ph); Computational Physics (physics.comp-ph)

arXiv:2602.24056 [quant-ph]

(or arXiv:2602.24056v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2602.24056 Focus to learn more arXiv-issued DOI via DataCite

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