Information geometric approach to mixed state quantum estimation

TL;DR

This paper applies information geometry to quantum state estimation, deriving higher-order corrections to the Cramér-Rao bound for mixed states using geometric methods, enhancing understanding of quantum statistical inference.

Contribution

It introduces a geometric approach to quantum estimation, deriving generalized higher-order corrections for mixed states that are independent of the estimator choice.

Findings

Derived generalized Bhattacharyya corrections for mixed states

Established geometric connections with pure state corrections

Provided estimator-independent higher-order bounds

Abstract

Information geometry promotes an investigation of the geometric structure of statistical manifolds, providing a series of elucidations in various areas of scientific knowledge. In the physical sciences, especially in quantum theory, this geometric method has an incredible parallel with the distinguishability of states, an ability of great value for determining the effectiveness in implementing physical processes. This gives us the context for this work. Here we will approach a problem of uniparametric statistical inference from an information-geometric perspective. We will obtain the generalised Bhattacharyya higher-order corrections for the Cram\'{e}r-Rao bound, where the statistics is given by a mixed quantum state. Using an unbiased estimator $T$, canonically conjugated to the Hamiltonian $H$ that generates the dynamics, we find these corrections independent of the specific choice of estimator. This procedure is performed using information-geometric techniques, establishing connections with corrections to the pure states case.