Metric and classical fidelity uncertainty relations for random unitary matrices

TL;DR

This paper establishes stronger fidelity-based uncertainty relations for random unitary matrices, improving quantum information locking and data hiding schemes, and enhances the understanding of Euclidean subspaces in matrix spaces.

Contribution

It introduces fidelity uncertainty relations with optimal parameters for random unitaries, leading to improved quantum locking, data hiding, and Euclidean subspace constructions.

Findings

Fidelity uncertainty relations hold for random unitaries with optimal parameters.

Enhanced quantum locking in Hellinger distance compared to total variation distance.

Existence of almost Euclidean subspaces with better dimension/distortion trade-offs.

Abstract

We analyze uncertainty relations on finite dimensional Hilbert spaces expressed in terms of classical fidelity, which are stronger then metric uncertainty relations introduced by Fawzi, Hayden and Sen. We establish validity of fidelity uncertainty relations for random unitary matrices with optimal parameters (up to universal constants) which improves upon known results for the weaker notion of metric uncertainty. This result is then applied to locking classical information in quantum states and allows to obtain optimal locking in Hellinger distance, improving upon previous results on locking in the total variation distance, both by strengthening the metric used and by improving the dependence on parameters. We also show that general probabilistic estimates behind the main theorem can be used to prove existence of data hiding schemes with Bayesian type guarantees. As a byproduct of our approach we obtain existence of almost Euclidean subspaces of the matrix spaces $\ell_1^n(\ell_2^m)$ with a better dimension/distortion dependence than allowed in previously known constructions.