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Tight One-Shot Analysis for Convex Splitting with Applications in Quantum Information Theory

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arxiv 2304.12055 v2 pith:AIUJTZVA submitted 2023-04-24 quant-ph cs.ITmath-phmath.ITmath.MP

classification quant-phcs.ITmath-phmath.ITmath.MP
keywords quantumone-shotconvexexponentsplittingerrorinformationanalysis
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Convex splitting is a powerful technique in quantum information theory used in proving the achievability of numerous information-processing protocols such as quantum state redistribution and quantum network channel coding. In this work, we establish a one-shot error exponent and a one-shot strong converse for convex splitting with trace distance as an error criterion. Our results show that the derived error exponent (strong converse exponent) is positive if and only if the rate is in (outside) the achievable region. This leads to new one-shot exponent results in various tasks such as communication over quantum wiretap channels, secret key distillation, one-way quantum message compression, quantum measurement simulation, and quantum channel coding with side information at the transmitter. We also establish a near-optimal one-shot characterization of the sample complexity for convex splitting, which yields matched second-order asymptotics. This then leads to stronger one-shot analysis in many quantum information-theoretic tasks.

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Cited by 3 Pith papers

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

  1. Sharp estimates of quantum covering problems via a novel trace inequality

    quant-ph 2025-07 conditional novelty 7.0 of 10

    A novel trace inequality, derived via operator layer cake, sharpens one-shot covering, privacy, splitting, decoupling, and channel simulation bounds by removing spectral-size factors.

  2. Layer Cake Representations for Quantum Divergences

    quant-ph 2025-07 conditional novelty 7.0 of 10

    Layer cake integrals define quantum Rényi and f-divergences that coincide with known integral representations and yield a proof of the conjectured trace formula for Rényi divergence with α>1.

  3. Alternating minimization for computing doubly minimized Petz Renyi mutual information

    quant-ph 2025-07 accept novelty 7.0 of 10

    Alternating minimization provably computes the doubly minimized Petz Rényi mutual information for all quantum states, with linear convergence for α∈(1,2] and O(1/n) convergence for α∈(1/2,1).

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