Metrological symmetries in singular quantum multi-parameter estimation

• URL: http://arxiv.org/abs/2503.05483v1
• Date: Fri, 07 Mar 2025 14:52:40 GMT
• Title: Metrological symmetries in singular quantum multi-parameter estimation
• Authors: George Mihailescu, Saubhik Sarkar, Abolfazl Bayat, Steve Campbell, Andrew K. Mitchell,
• Abstract summary: We show that metrological symmetries appear in the Bayesian posterior distribution as lines of persistent likelihood running through the space of unknown parameters.<n>These lines are contour lines of the effective parameters which, through suitable parameter transformations, can be estimated and follow their own effective CRBs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The theoretical foundation of quantum sensing is rooted in the Cram\'er-Rao formalism, which establishes quantitative precision bounds for a given quantum probe. In many practical scenarios, where more than one parameter is unknown, the multi-parameter Cram\'er-Rao bound (CRB) applies. Since this is a matrix inequality involving the inverse of the quantum Fisher information matrix (QFIM), the formalism breaks down when the QFIM is singular. In this paper, we examine the physical origins of such singularities, showing that they result from an over-parametrization on the metrological level. This is itself caused by emergent metrological symmetries, whereby the same set of measurement outcomes are obtained for different combinations of system parameters. Although the number of effective parameters is equal to the number of non-zero QFIM eigenvalues, the Cram\'er-Rao formalism typically does not provide information about the effective parameter encoding. Instead, we demonstrate through a series of concrete examples that Bayesian estimation can provide deep insights. In particular, the metrological symmetries appear in the Bayesian posterior distribution as lines of persistent likelihood running through the space of unknown parameters. These lines are contour lines of the effective parameters which, through suitable parameter transformations, can be estimated and follow their own effective CRBs.

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