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This paper introduces a single-letter, efficiently computable strong converse bound for the classical identification capacity of quantum channels by leveraging Gaussian mean widths in weighted Euclidean geometries. The approach yields a semidefinite representation that improves upon existing bounds for key channel models like depolarizing and erasure channels, offering a more rigorous analytical framework for quantum information theory.
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Gaussian mean width strong converse bound on the classical identification capacity of quantum channels

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arXiv:2606.05032v1 Announce Type: new Abstract: We establish a single-letter and efficiently computable strong converse bound on the classical identification capacity of quantum channels. By equipping the $n$-fold channel output space with a product state-weighted $\sigma$-Euclidean geometry, we allow trace-distance separation constraints for identification codes to be controlled by Euclidean covering estimates. Using Sudakov's inequality, we bound the covering numbers of the $n$-fold channel outputs via their Gaussian mean widths in the weighted geometry, whose exponential growth in $n$ is governed by the operator norm of a single-letter positive operator. Upon optimizing over all weighing states $\sigma$, this yields a strong converse bound on the identification capacity of the channel, which also admits a semidefinite representation. Our method improves the best known converse bounds on the identification capacity of several important examples, such as depolarizing, Pauli, erasure, and amplitude damping channels. We also discuss extensions of this method to more general Euclidean geometries on the output space.

Abstract:We establish a single-letter and efficiently computable strong converse bound on the classical identification capacity of quantum channels. By equipping the $n$-fold channel output space with a product state-weighted $\sigma$-Euclidean geometry, we allow trace-distance separation constraints for identification codes to be controlled by Euclidean covering estimates. Using Sudakov's inequality, we bound the covering numbers of the $n$-fold channel outputs via their Gaussian mean widths in the weighted geometry, whose exponential growth in $n$ is governed by the operator norm of a single-letter positive operator. Upon optimizing over all weighing states $\sigma$, this yields a strong converse bound on the identification capacity of the channel, which also admits a semidefinite representation. Our method improves the best known converse bounds on the identification capacity of several important examples, such as depolarizing, Pauli, erasure, and amplitude damping channels. We also discuss extensions of this method to more general Euclidean geometries on the output space.

Quantum Physics (quant-ph); Information Theory (cs.IT); Metric Geometry (math.MG)

arXiv:2606.05032 [quant-ph]

(or arXiv:2606.05032v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2606.05032 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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