Noise Classification in Three-Level Quantum Networks by Machine Learning

TL;DR

This paper demonstrates that machine learning can accurately classify different types of classical noise affecting a three-level quantum system, aiding in understanding noise correlations in quantum networks.

Contribution

The study introduces a neural network-based method to classify classical noise types in a three-level quantum system, including non-Markovian correlations, with high accuracy and robustness.

Findings

Supervised learning classifies classical noise with over 99% accuracy.

The method distinguishes non-Markovian noise types but not Markovian correlations.

Approach is robust to measurement errors and limited data, suitable for experimental use.

Abstract

We investigate a machine learning based classification of noise acting on a small quantum network with the aim of detecting spatial or multilevel correlations, and the interplay with Markovianity. We control a three-level system by inducing coherent population transfer exploiting different pulse amplitude combinations as inputs to train a feedforward neural network. We show that supervised learning can classify different types of classical dephasing noise affecting the system. Three non-Markovian (quasi-static correlated, anti-correlated and uncorrelated) and Markovian noises are classified with more than $99\%$ accuracy. On the contrary, correlations of Markovian noise cannot be discriminated with our method. Our approach is robust to statistical measurement errors and retains its effectiveness for physical measurements where only a limited number of samples is available making it very experimental-friendly. Our result paves the way for classifying spatial correlations of noise in quantum architectures.