Gill and Massar type bound for estimation of $SU(2)$ channel

TL;DR

This paper extends the Gill and Massar bound to $SU(2)$ unitary channel estimation, establishing a convex structure for Fisher information matrices and demonstrating achievable bounds with randomized strategies without ancillas.

Contribution

It introduces a Gill and Massar type lower bound for $SU(2)$ channels, showing its achievability and convex structure in quantum channel estimation.

Findings

Derived a Fisher information matrix for $SU(2)$ channels.

Established a lower bound on weighted traces of covariances.

Constructed strategies that achieve the bound without ancillas.

Abstract

In the estimation for a parametric family of quantum state on a Hilbert space $\mathcal{H}$, the Gill and Massar bound is known as a lower bound of weighted traces of covariances of unbiased estimators. The Gill and Massar bound is derived by considering the convexity of the set of classical Fisher information matrices, and the bound is locally achievable by using randomized strategies when $\mathcal{H}=\mathbb{C}^{2}$. In this paper, we show that estimation for a parametric $SU(2)$ unitary channel model has a similar convex structure as qubit state model, and a Gill and Massar type lower bound of weighted traces of covariances of unbiased estimators can be derived for any weight matrix. We show that the Gill and Massar type lower bound is achievable by using randomized strategies when certain conditions are satisfied. To derive a convex structure of the set of classical Fisher information matrices, we introduce a Fisher information matrix $J^{(U)}$ for a $SU(2)$ unitary channel model, and we show a upper bound of inverse $J^{(U)}$ weighted trace of classical Fisher information matrix. The optimal randomized strategy we construct in this paper does not require ancilla systems in many cases.