Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks

• URL: http://arxiv.org/abs/2310.02309v1
• Date: Tue, 3 Oct 2023 18:00:02 GMT
• Title: Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks
• Authors: Enrico Rinaldi, Manuel Gonz\'alez Lastre, Sergio Garc\'ia Herreros, Shahnawaz Ahmed, Maryam Khanahmadi, Franco Nori, Carlos S\'anchez Mu\~noz
• Abstract summary: We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single measurement. We benchmark the precision of this method against Bayesian inference, which is optimal in the sense of information retrieval. 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.
• Score: 0.21990652930491852
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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