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Quantum Hypothesis Testing Lemma for Deterministic Identification over Quantum Channels

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arxiv 2504.20991 v2 pith:BTTGF6IA submitted 2025-04-29 cs.IT math.ITquant-ph

classification cs.ITmath.ITquant-ph
keywords quantumchannelscodelemmacodesdeterministicexistencehypothesis
verification ladder T0 review T1 audit T2 compute T3 formal
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In our previous work, we presented the \emph{Hypothesis Testing Lemma}, a key tool that establishes sufficient conditions for the existence of good deterministic identification (DI) codes for memoryless channels with finite output, but arbitrary input alphabets. In this work, we provide a full quantum analogue of this lemma, which shows that the existence of a DI code in the quantum setting follows from a suitable packing in a modified space of output quantum states. Specifically, we demonstrate that such a code can be constructed using product states derived from this packing. This result enables us to tighten the capacity lower bound for DI over quantum channels beyond the simultaneous decoding approach. In particular, we can now express these bounds solely in terms of the Minkowski dimension of a certain state space, giving us new insights to better understand the nature of the protocol, and the separation between simultaneous and non-simultaneous codes. We extend the discussion with a particular channel example for which we can construct an optimum code.

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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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