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This paper establishes the sharp uniform continuity bound for quantum conditional entropy, refining theoretical limits on how state perturbations affect information measures in bipartite systems. The research notably leverages ChatGPT 5.6 Sol to develop key proof ideas that adapt and improve upon existing classical methodologies from IEEE ISIT (2020).
Sharp continuity of quantum conditional entropy
arXiv:2607.24687v1 Announce Type: new Abstract: We prove the sharp uniform continuity bound for quantum conditional entropy. If two bipartite states are at trace distance at most $\delta$ and $d=\dim A$, the optimal dimension-only modulus of continuity is $h_2(\delta)+\delta\log(d^2-1)$ up to $\delta=1-d^{-2}$ and $2\log d$ thereafter, where $h_2$ denotes the binary entropy. When $\dim B\ge d$, this bound is tight for every $\delta\in[0,1]$. The key proof idea was developed with the assistance of ChatGPT 5.6 Sol, building on and adapting the tight classical proof of Alhejji \& Smith [IEEE ISIT (2020)], which follows a conceptually different approach.
Abstract:We prove the sharp uniform continuity bound for quantum conditional entropy. If two bipartite states are at trace distance at most $\delta$ and $d=\dim A$, the optimal dimension-only modulus of continuity is $h_2(\delta)+\delta\log(d^2-1)$ up to $\delta=1-d^{-2}$ and $2\log d$ thereafter, where $h_2$ denotes the binary entropy. When $\dim B\ge d$, this bound is tight for every $\delta\in[0,1]$. The key proof idea was developed with the assistance of ChatGPT 5.6 Sol, building on and adapting the tight classical proof of Alhejji \& Smith [IEEE ISIT (2020)], which follows a conceptually different approach.
arXiv:2607.24687 [quant-ph]
(or arXiv:2607.24687v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2607.24687 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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