Measurement incompatibility in Bayesian multiparameter quantum estimation
Francesco Albarelli, Dominic Branford, Jes\'us Rubio

TL;DR
This paper develops a comprehensive Bayesian framework for multiparameter quantum estimation, analyzing how measurement incompatibility affects precision limits and providing practical tools and bounds for quantum metrology applications.
Contribution
It introduces explicit conditions for optimal estimation, quantifies the impact of measurement incompatibility, and offers an open-source package for practical evaluation.
Findings
Measurement incompatibility can at most double the minimum estimation loss.
The framework provides analytical bounds and practical benchmarks for quantum metrology.
The open-source package enables efficient assessment of quantum measurement strategies.
Abstract
We present a comprehensive and pedagogical formulation of Bayesian multiparameter quantum estimation, providing explicit conditions for achieving minimum quadratic losses. 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 originally developed for 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. This result implies that, in many practical situations, the latter may provide a sufficient and computationally efficient benchmark without…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Mechanics and Applications · Quantum Computing Algorithms and Architecture
