Learning spectral density functions in open quantum systems

• URL: http://arxiv.org/abs/2602.24056v1
• Date: Fri, 27 Feb 2026 14:45:59 GMT
• Title: Learning spectral density functions in open quantum systems
• Authors: Felipe Peleteiro, João Victor Shiguetsugo Kawanami Lima, Pedro Marcelo Prado, Felipe Fernandes Fanchini, Ariel Norambuena,
• Abstract summary: We use exactly solvable spin-boson models with pure-dephasing and amplitude-damping channels to reconstruct spectral density functions from noisy data.<n>Our neural network robustly reconstructs structured densities by filtering noisy signals and learning general functional dependencies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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