Experimental characterisation of a non-Markovian quantum process

• URL: http://arxiv.org/abs/2102.01327v1
• Date: Tue, 2 Feb 2021 06:00:04 GMT
• Title: Experimental characterisation of a non-Markovian quantum process
• Authors: K. Goswami, C. Giarmatzi, C. Monterola, S. Shrapnel, J. Romero, and F. Costa
• Abstract summary: We employ machine learning models to estimate the amount of non-Markovianity. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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