Abstract
Combining quantum and Bayesian principles leads to optimality in metrology, but the optimization equations involved are often hard to solve. This Letter mitigates this problem with a class of measurement strategies for quantities isomorphic to location parameters, which are shown to admit a closed-form optimization. The resulting framework admits any parameter range, prior information, or state, and the associated estimators apply to finite samples. As an example, the metrology of relative weights is formulated from first principles and shown to require hyperbolic errors. The primary advantage of this approach lies in its simplifying power: it reduces the search for good strategies to identifying which symmetry leaves a state of maximum ignorance invariant. This will facilitate the application of quantum metrology to fundamental physics, where symmetries play a key role. Published by the American Physical Society 2024