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- W4387389626 abstract "We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by continuous weak measurements, our method harnesses quantum correlations in discrete photon-counting data characterized by quantum jumps. We benchmark the precision of this method against Bayesian inference, which is optimal in the sense of information retrieval. By using numerical experiments on a two-level quantum system, we demonstrate that our approach can achieve a similar optimal performance as Bayesian inference, while drastically reducing computational costs. Additionally, the method exhibits robustness against the presence of imperfections in both measurement and training data. This approach offers a promising and computationally efficient tool for quantum parameter estimation with photon-counting data, relevant for applications such as quantum sensing or quantum imaging, as well as robust calibration tasks in laboratory-based settings." @default.
- W4387389626 created "2023-10-06" @default.
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- W4387389626 date "2023-10-03" @default.
- W4387389626 modified "2023-10-07" @default.
- W4387389626 title "Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks" @default.
- W4387389626 doi "https://doi.org/10.48550/arxiv.2310.02309" @default.
- W4387389626 hasPublicationYear "2023" @default.
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