Spectral Density Classification For Environment Spectroscopy

TL;DR

This paper demonstrates that machine learning can accurately classify the spectral density type of an environment in open quantum systems by analyzing the system's observable dynamics, aiding in understanding system-environment interactions.

Contribution

It introduces a neural network approach to classify spectral densities in quantum environments, specifically distinguishing Ohmic, sub-Ohmic, and super-Ohmic types.

Findings

High classification accuracy achieved for spectral density types.

Neural network effectively infers environment features from system dynamics.

Applicable to spin-boson models for environment characterization.

Abstract

Spectral densities encode the relevant information characterising the system-environment interaction in an open-quantum system problem. Such information is key to determining the system's dynamics. In this work, we leverage the potential of machine learning techniques to reconstruct the features of the environment. Specifically, we show that the time evolution of a system observable can be used by an artificial neural network to infer the main features of the spectral density. In particular, for relevant examples of spin-boson models, we can classify with high accuracy the Ohmicity parameter of the environment as either Ohmic, sub-Ohmic or super-Ohmic, thereby distinguishing between different forms of dissipation.