Sharp finite statistics for quantum key distribution

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Veröffentlicht in:arXiv.org (Dec 18, 2024), p. n/a
1. Verfasser: Mannalath, Vaisakh
Weitere Verfasser: Zapatero, Víctor, Curty, Marcos
Veröffentlicht:
Cornell University Library, arXiv.org
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022 |a 2331-8422 
035 |a 3147269143 
045 0 |b d20241218 
100 1 |a Mannalath, Vaisakh 
245 1 |a Sharp finite statistics for quantum key distribution 
260 |b Cornell University Library, arXiv.org  |c Dec 18, 2024 
513 |a Working Paper 
520 3 |a The performance of quantum key distribution (QKD) heavily depends on statistical inference. For a broad class of protocols, the central statistical task is a random sampling problem, customarily addressed using exponential tail bounds on the hypergeometric distribution. Here we devise a strikingly simple exponential bound for this task, of unprecedented tightness among QKD security analyses. As a by-product, confidence intervals for the average of non-identical Bernoulli parameters follow too. These naturally fit in statistical analyses of decoy-state QKD and also outperform standard tools. Lastly, we show that, in a vast parameter regime, the use of tail bounds is not enforced because the cumulative mass function of the hypergeometric distribution is accurately computable. This sharply decreases the minimum block sizes necessary for QKD, and reveals the tightness of our simple analytical bounds when moderate-to-large blocks are considered. 
653 |a Quantum cryptography 
653 |a Tightness 
653 |a Random sampling 
653 |a Confidence intervals 
653 |a Statistical analysis 
653 |a Parameters 
653 |a Statistical inference 
700 1 |a Zapatero, Víctor 
700 1 |a Curty, Marcos 
773 0 |t arXiv.org  |g (Dec 18, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3147269143/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2410.04095