A machine learning based approach to the identification of spectral densities in quantum open systems
- URL: http://arxiv.org/abs/2507.13730v1
- Date: Fri, 18 Jul 2025 08:23:15 GMT
- Title: A machine learning based approach to the identification of spectral densities in quantum open systems
- Authors: Jessica Barr, Shreyasi Mukherjee, Alessandro Ferraro, Mauro Paternostro, Giorgio Zicari,
- Abstract summary: We present a machine learning-based approach for characterising the environment that affects the dynamics of an open quantum system.<n>We focus on the case of an exactly solvable spin-boson model, where the system-environment interaction, whose strength is encoded in the spectral density, induces pure dephasing.
- Score: 39.58317527488534
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a machine learning-based approach for characterising the environment that affects the dynamics of an open quantum system. We focus on the case of an exactly solvable spin-boson model, where the system-environment interaction, whose strength is encoded in the spectral density, induces pure dephasing. By using artificial neural networks trained on the Fourier-transformed time evolution of some observables of the system, we perform both classification -- distinguishing sub-Ohmic, Ohmic, and super-Ohmic spectral densities -- and regression -- thus estimating key parameters of the spectral density function, when the latter is expressed through a power law. Our results demonstrate high classification accuracy and robust parameter estimation, highlighting the potential of machine learning as a powerful tool for probing environmental features in quantum systems and advancing quantum noise spectroscopy.
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