A machine learning based approach to the identification of spectral densities in quantum open systems

2025/07/18 by Jessica Barr, Barr, Jessica, Shreyasi Mukherjee +7 · 2 citations
Computer Science · Physics and Astronomy · #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Spectroscopy and Quantum Chemical Studies

paper · pdf · doi:10.1140/epjs/s11734-025-01954-9

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.

Cited by

• Learning spectral density functions in open quantum systems
• Detection of noise correlations in two qubit systems by Machine Learning

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