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arXiv · 2511.16645

Measurement incompatibility in Bayesian multiparameter quantum estimation

Francesco Albarelli·Dominic Branford·Jesús Rubio
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Abstract

We present a comprehensive and pedagogical formulation of Bayesian multiparameter quantum estimation. Within this framework, we analyse the role of measurement incompatibility and establish its quantitative effect on attainable precision. We achieve this by deriving upper bounds based on the pretty good measurement -- a notion from hypothesis testing -- combined with the evaluation of the Nagaoka-Hayashi lower bound. In general, we prove that, as in the many-copy regime of local estimation theory, incompatibility can at most double the minimum loss relative to the idealised scenario in which individually optimal measurements are assumed jointly implementable. Therefore, in practical situations, the latter may provide a sufficient and computationally efficient benchmark without solving the full optimisation problem. Our results, which we illustrate through applications of discrete phase imaging, phase and dephasing estimation, and qubit sensing, provide analytical and numerical tools for assessing ultimate precision limits and the role of measurement incompatibility in Bayesian multiparameter quantum metrology, including an open-source package for all the bounds discussed here.

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Francesco Albarelli, Dominic Branford, Jesús Rubio. 2025-11-20. Measurement incompatibility in Bayesian multiparameter quantum estimation. https://doi.org/10.22331/q-2026-08-13-2192

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