Quantum metrology derives its capabilities from the careful employ of quantum resources for carrying out measurements. This advantage, however, relies on refined data postprocessing, assessed based on the variance of the estimated parameter. When Bayesian techniques are adopted, more elements become available for assessing the quality of the estimation. Here we adopt generalized classical Cramér-Rao bounds for looking in detail into a phase-estimation experiment performed with quantum light. In particular, we show that the third-order absolute moment can give a superior capability in revealing biases in the estimation, compared to standard approaches. Our studies point to the identification of an alternative strategy that brings a possible advantage in monitoring the correct operation of high-precision sensors.
Diagnosing imperfections in quantum sensors via generalized Cramér-Rao bounds / Cimini, V.; Genoni, M. G.; Gianani, I.; Spagnolo, N.; Sciarrino, F.; Barbieri, M.. - In: PHYSICAL REVIEW APPLIED. - ISSN 2331-7019. - 13:2(2020). [10.1103/PhysRevApplied.13.024048]
Diagnosing imperfections in quantum sensors via generalized Cramér-Rao bounds
Cimini V.
;Gianani I.;Spagnolo N.;Sciarrino F.;
2020
Abstract
Quantum metrology derives its capabilities from the careful employ of quantum resources for carrying out measurements. This advantage, however, relies on refined data postprocessing, assessed based on the variance of the estimated parameter. When Bayesian techniques are adopted, more elements become available for assessing the quality of the estimation. Here we adopt generalized classical Cramér-Rao bounds for looking in detail into a phase-estimation experiment performed with quantum light. In particular, we show that the third-order absolute moment can give a superior capability in revealing biases in the estimation, compared to standard approaches. Our studies point to the identification of an alternative strategy that brings a possible advantage in monitoring the correct operation of high-precision sensors.File | Dimensione | Formato | |
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