期刊
PHYSICAL REVIEW A
卷 104, 期 2, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevA.104.022432
关键词
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资金
- Australian Research Council (ARC) by the Centre of Excellence for Engineered Quantum Systems (EQUS) [CE170100009]
- University of Queensland
- Sydney Quantum Academy Postdoctoral Fellowship
- Westpac Bicentennial Foundation Research Fellowship
- L'Oreal-UNESCO FWIS Fellowship
- Australian Research Council [DE170100712]
- Australian Research Council [DE170100712] Funding Source: Australian Research Council
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.
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.
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