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This research advances quantum metrology by extending parameter estimation techniques beyond the Markovian regime using reaction coordinate mapping for non-Markovian dynamics. The study provides analytical expressions for Fisher information in linear systems undergoing Gaussian continuous measurements, offering improved precision scaling for tasks like bosonic bath thermometry.
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Parameter Estimation in a Continuously Monitored Non-Markovian Quantum System

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2607.15978v1 Announce Type: new Abstract: Continuous monitoring is a powerful tool for analyzing quantum systems and is increasingly used as a non-demolition technique in quantum metrology. In this context, noisy data acquired through continuous monitoring can be used to precisely pinpoint unknown parameters of the system. However, extending the theoretical framework that connects these data to the underlying parameters beyond Markovian dynamics is notoriously difficult, primarily because such dynamics cannot be expressed as completely positive divisible maps. To overcome this, we propose a method based on the reaction coordinate mapping to extend these parameter-estimation techniques beyond the Markovian regime. Our approach specifically targets linear systems with non-Markovian dynamics undergoing Gaussian continuous measurements, such as homodyne detection. Within this framework, we analyze Bayesian estimation and provide an analytical expression for the Fisher information, alongside the asymptotic scaling of the estimation precision for arbitrary parameters. Finally, we demonstrate the efficacy of our method through the example of thermometry of a bosonic bath.

Abstract:Continuous monitoring is a powerful tool for analyzing quantum systems and is increasingly used as a non-demolition technique in quantum metrology. In this context, noisy data acquired through continuous monitoring can be used to precisely pinpoint unknown parameters of the system. However, extending the theoretical framework that connects these data to the underlying parameters beyond Markovian dynamics is notoriously difficult, primarily because such dynamics cannot be expressed as completely positive divisible maps. To overcome this, we propose a method based on the reaction coordinate mapping to extend these parameter-estimation techniques beyond the Markovian regime. Our approach specifically targets linear systems with non-Markovian dynamics undergoing Gaussian continuous measurements, such as homodyne detection. Within this framework, we analyze Bayesian estimation and provide an analytical expression for the Fisher information, alongside the asymptotic scaling of the estimation precision for arbitrary parameters. Finally, we demonstrate the efficacy of our method through the example of thermometry of a bosonic bath.

arXiv:2607.15978 [quant-ph]

(or arXiv:2607.15978v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2607.15978 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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