Machine learning non-Markovian two-level quantum noise spectroscopy

TL;DR

This paper introduces machine learning techniques to automate the characterization of non-Markovian quantum noise in two-level systems, enabling accurate spectral density and coupling strength estimation across different regimes.

Contribution

The study develops a machine learning framework for non-Hermitian two-level quantum noise spectroscopy, including novel regression and classification methods for spectral and coupling characterization.

Findings

High-accuracy regression of system-bath coupling strength

Effective Ohmicity classification for spectral density

Robust non-Markovian dynamics modeling across regimes

Abstract

We develop machine learning models for the automated characterization of quantum noise spectroscopy for non-Hermitian two-level systems. We use the Random Forest, Support Vector and Feed-Forward Neural Network regression algorithms to perform a highly accurate regression of the two-level system-bath coupling strength. High accuracy Ohmicity classification was implemented to provide a complete characterization of the spectral density function. We define a time-averaged trace-distance metric to feed the machine learning algorithms which, together with numerically exact populations as inputs, produce a highly accurate non-Markovian regression spanning the transition from fast to slow baths and from weak to strong coupling regimes of the interaction. The dynamics database of the non-Hermitian systems has been built up within the independent spin-boson and pure dephasing model.