Bayesian multiparameter quantum metrology with limited data
Rubio, J; Dunningham, J
Date: 20 March 2020
Journal
Physical Review A
Publisher
American Physical Society (APS)
Publisher DOI
Abstract
A longstanding problem in quantum metrology is how to extract as much information as possible in realistic
scenarios with not only multiple unknown parameters, but also limited measurement data and some degree of
prior information. Here we present a practical solution to this: We derive a Bayesian multi-parameter quantum
bound, ...
A longstanding problem in quantum metrology is how to extract as much information as possible in realistic
scenarios with not only multiple unknown parameters, but also limited measurement data and some degree of
prior information. Here we present a practical solution to this: We derive a Bayesian multi-parameter quantum
bound, construct the optimal measurement when our bound can be saturated for a single shot, and consider
experiments involving a repeated sequence of these measurements. Our method properly accounts for the number
of measurements and the degree of prior information, and we illustrate our ideas with a qubit sensing network
and a model for phase imaging, clarifying the nonasymptotic role of local and global schemes. Crucially, our
technique is a powerful way of implementing quantum protocols in a wide range of practical scenarios that tools
such as the Helstrom and Holevo Cramér-Rao bounds cannot normally access.
Physics and Astronomy
Faculty of Environment, Science and Economy
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