Uploaded December 2025 | Updated September 2026, 2 weeks ago
STP, DIAS Mini Symposium - 4 November 2025
Speaker: George Mihailescu (UCD)
Abstract: The theoretical foundation of quantum sensing is rooted in the Cramér-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 Cramér-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. We seek to examine the physical origins of such singularities, showing that they may manifest from over-parametrization on the metrological level. This is a result of emergent metrological symmetries, whereby the same set of measurement outcomes are obtained for different combinations of system parameters. Typically, the Cramér-Rao formalism does not provide information about the effective parameter encoding, although it does provide a means for deducing the number of effective parameters easily. We demonstrate that the effective parameter encoding can be deduced through a global Bayesian estimation strategy through a series of concrete examples.
STP, DIAS Mini Symposium - 4 November 2025
Speaker: George Mihailescu (UCD)
Abstract: The theoretical foundation of quantum sensing is rooted in the Cramér-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 Cramér-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. We seek to examine the physical origins of such singularities, showing that they may manifest from over-parametrization on the metrological level. This is a result of emergent metrological symmetries, whereby the same set of measurement outcomes are obtained for different combinations of system parameters. Typically, the Cramér-Rao formalism does not provide information about the effective parameter encoding, although it does provide a means for deducing the number of effective parameters easily. We demonstrate that the effective parameter encoding can be deduced through a global Bayesian estimation strategy through a series of concrete examples.
