Abstract
Global sensing enables parameter estimation across arbitrary parameter ranges with a finite number of shots. While various formulations exist, the Bayesian paradigm offers a flexible approach to optimal protocol design under minimal assumptions. However, there are several sets of assumptions capturing the notions of prior ignorance and uninformed estimation, leading to two main approaches: invariance of the prior distribution under specific parameter transformations, and adherence to the geometry of a state space. While the first approach often leads to simpler priors and estimators and is more broadly applicable in adaptive settings, the second can lead to faster posterior convergence in a well-defined measurement setting. We examine the practical consequences of both approaches and show how to apply them in examples of rate and coherence estimation in noisy scenarios. More importantly, by employing the notion of location-isomorphic parameters, we unify the two approaches into a practical and versatile framework for optimal global quantum sensing, detailing when and how each set of assumptions should be employed—a blueprint for the design of quantum sensors.