Experimental characterization of a non-Markovian quantum process

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 characterization of non-Markovian noise requires tomography of a multitime 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.

Automated summary

In this paper, a more efficient method is proposed to estimate non-Markovianity using machine learning models, quantified by an information-theoretic measure, with tomographically incomplete measurement. The model was tested in a quantum optical experiment and achieved a 90% accuracy in predicting the non-Markovianity measure, paving the way for efficient detection of non-Markovian noise in large scale quantum computers.