Quick Navigation

Overview Topics Abstract Importance Paper Tools References Reactions Discussion Publication

Topics

Quantum Channels Communication Theory

A Mirror-Descent Algorithm for Computing the Petz-Rényi Capacity of Classical-Quantum Channels

arXiv
Authors: Yu-Hong Lai, Hao-Chung Cheng

Year

2026

Paper ID

3749

Status

Preprint

Abstract Read

~2 min

Abstract Words

90

Citations

N/A

Abstract

We study the computation of the α-Rényi capacity of a classical-quantum (c-q) channel for αin(0,1). We propose an exponentiated-gradient (mirror descent) iteration that generalizes the Blahut-Arimoto algorithm. Our analysis establishes relative smoothness with respect to the entropy geometry, guaranteeing a global sublinear convergence of the objective values. Furthermore, under a natural tangent-space nondegeneracy condition (and a mild spectral lower bound in one regime), we prove local linear (geometric) convergence in Kullback-Leibler divergence on a truncated probability simplex, with an explicit contraction factor once the local curvature constants are bounded.

Why This Paper Matters

  • This paper contributes to the Quantum Channels & Communication Theory research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • We study the computation of the α-Rényi capacity of a classical-quantum (c-q) channel for αin(0,1).

Paper Tools

Become a member to use research tools

Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.

Become a member Sign in
Show Paper arXiv Publisher Share Cite This Paper Copy URL Compare Copy DOI Add to Reading List Category Correction Request

References & Citation Signals

[1] DOI https://doi.org/arXiv:2601.10558 [2] arXiv https://arxiv.org/abs/2601.10558 [3] Publisher https://arxiv.org/abs/2601.10558

Local Citation Graph (Related-Paper Links)

Current Paper #3749

External citation index: OpenAlex citation signal

Community Reactions

Quick sentiment from readers on this paper.

Score: 0
Likes: 0 Dislikes: 0

Sign in to react to this paper.

Discussion & Reviews (Moderated)

Average Rating: 0.0 / 5 (0 ratings)

No written reviews yet.