Experimental characterisation of a non-Markovian quantum process

TL;DR

This paper introduces a machine learning approach to efficiently estimate non-Markovian quantum noise using incomplete measurements, demonstrated through a quantum optical experiment, facilitating scalable noise detection in quantum computing.

Contribution

It presents a novel machine learning method for estimating non-Markovianity with incomplete data, reducing the complexity of quantum process characterization.

Findings

Achieved 90% accuracy in predicting non-Markovianity measure.

Demonstrated the method on a quantum optical experiment.

Enabled efficient detection of non-Markovian noise in large-scale quantum systems.

Abstract

Every quantum system is coupled to an environment. Such system-environment interaction leads to temporal correlation between quantum operations at different times, resulting in non-Markovian noise. In principle, a full characterisation of non-Markovian noise requires tomography of a multi-time processes matrix, which is both computationally and experimentally demanding. In this paper, we propose a more efficient solution. We employ machine learning models to estimate the amount of non-Markovianity, as quantified by an information-theoretic measure, with tomographically incomplete measurement. We test our model on a quantum optical experiment, and we are able to predict the non-Markovianity measure with $90\%$ accuracy. Our experiment paves the way for efficient detection of non-Markovian noise appearing in large scale quantum computers.