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Rate-reliability tradeoff for deterministic identification

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arxiv 2502.02389 v4 pith:VBINBURH submitted 2025-02-04 cs.IT math.ITquant-ph

classification cs.ITmath.ITquant-ph
keywords mathbfexponentschannelsidentificationsmallchanneldeterministicdimension
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We investigate deterministic identification over arbitrary memoryless channels under the constraint that the error probabilities of first and second kind are exponentially small in the block length $\mathbf{n}$, controlled by reliability exponents $\mathbf{E_1,E_2 \geq 0}$. In contrast to the regime of slowly vanishing errors, where the identifiable message length scales linearithmically as $\mathbf{\Theta(n\log n)}$, here we find that for positive exponents linear scaling is restored, now with a rate that is a function of the reliability exponents. We give upper and lower bounds on the ensuing rate-reliability function in terms of (the logarithm of) the packing and covering numbers of the channel output set, which for small error exponents $\mathbf{E_1,E_2>0}$ can be expanded in leading order as the product of the Minkowski dimension of a certain parametrisation the channel output set and $\mathbf{\log\min\{E_1,E_2\}}$. These allow us to recover the previously observed slightly superlinear identification rates, and offer a different perspective for understanding them in more traditional information theory terms. We also show that even if only one of the two errors is required to be exponentially small, the linearithmic scaling is lost. We further illustrate our results with a discussion of the case of dimension zero, and extend them to classical-quantum channels and quantum channels with tensor product input restriction.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Deterministic identification for Bernoulli channels and related channels with continuous input

    cs.IT 2026-05 conditional novelty 7.0 of 10

    Deterministic identification capacity Ċ_DI(W) equals exactly 1/2 for Bernoulli channels and channels reducible to them via continuous output distribution curves.

  2. Optimal Codes for Deterministic Identification over Gaussian Channels: Closing the Capacity Gap

    cs.IT 2026-04 unverdicted novelty 7.0 of 10

    The linearithmic deterministic identification capacity of Gaussian channels is 1/2, achieved by an optimized code that matches the upper bound and closes gaps in rate-reliability tradeoffs for all error regimes.

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