Machine learning-enhanced characterisation of open quantum dynamics

Additionally, we extend our approach to a regression task, where the neural network accurately predicts continuous values of the Ohmicity parameter, the coupling strength and the cut-off frequency, providing a comprehensive characterisation of the spectral density.<br><br>Furthermore, to address scenarios beyond the weak coupling regime, we employ the reaction coordinate mapping. For a dissipative spin-boson model with a structured spectral density comprising one, two, or three Lorentzian peaks, we demonstrate that a neural network can classify the spectral density based on the number of peaks and accurately predict their positions.

The methodology developed in this thesis, along with the case studies analysed, demonstrates the effectiveness of machine learning techniques for characterising environments with arbitrary spectral densities across a broad range of coupling regimes.