Machine learning classification of non-Markovian noise disturbing quantum dynamics

TL;DR

This paper demonstrates that machine learning models, including support vector machines and neural networks, can effectively classify non-Markovian noise sources in quantum systems using simulated data, with potential applications in quantum device benchmarking.

Contribution

It introduces a machine learning approach for classifying non-Markovian noise in quantum dynamics, validated on simulated data, and highlights its potential for experimental noise benchmarking.

Findings

High accuracy in classifying quantum noise sources.

Effective use of simple measurements for classification.

Potential for application in quantum device noise benchmarking.

Abstract

In this paper machine learning and artificial neural network models are proposed for the classification of external noise sources affecting a given quantum dynamics. For this purpose, we train and then validate support vector machine, multi-layer perceptron and recurrent neural network models with different complexity and accuracy, to solve supervised binary classification problems. As a result, we demonstrate the high efficacy of such tools in classifying noisy quantum dynamics using simulated data sets from different realizations of the quantum system dynamics. In addition, we show that for a successful classification one just needs to measure, in a sequence of discrete time instants, the probabilities that the analysed quantum system is in one of the allowed positions or energy configurations. Albeit the training of machine learning models is here performed on synthetic data, our approach is expected to find application in experimental schemes, as e.g. for the noise benchmarking of noisy intermediate-scale quantum devices.