Even Odd Splitting of the Gaussian Quantum Fisher Information: From Symplectic Geometry to Metrology

TL;DR

This paper presents a novel decomposition of the quantum Fisher information for Gaussian states into even and odd parts, linking symplectic geometry to quantum metrology and enabling better sensor design.

Contribution

It introduces a canonical even-odd decomposition of QFI for Gaussian states, connecting geometric structures with metrological applications and resource analysis.

Findings

Odd QFI vanishes for pure states

Decomposition clarifies resource contributions in Gaussian sensing

Provides bounds and applications to Gaussian channels

Abstract

We introduce a canonical decomposition of the quantum Fisher information (QFI) for centered multimode Gaussian states into two additive pieces: an even part that captures changes in the symplectic spectrum and an odd part associated with correlation-generating dynamics. On the pure-state manifold, the even contribution vanishes identically, while the odd contribution coincides with the QFI derived from the natural metric on the Siegel upper half-space, revealing a direct geometric underpinning of pure-Gaussian metrology. This also provides a link between the graphical representation of pure Gaussian states and an explicit expression for the QFI in terms of graphical parameters. For evolutions completely generated by passive Gaussian unitaries (orthogonal symplectics), the odd QFI vanishes, while thermometric parameters contribute purely to the even sector with a simple spectral form; we also derive a state-dependent lower bound on the even QFI in terms of the purity-change rate. We extend the construction to the full QFI matrix, obtaining an additive even odd sector decomposition that clarifies when cross-parameter information vanishes. Applications to unitary sensing (beam splitter versus two-mode squeezing) and to Gaussian channels (loss and phase-insensitive amplification), including joint phase loss estimation, demonstrate how the decomposition cleanly separates resources associated with spectrum versus correlations. The framework supplies practical design rules for continuous-variable sensors and provides a geometric lens for benchmarking probes and channels in Gaussian quantum metrology.