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One-Shot Point-to-Point Channel Simulation

Cao, Michael X. and Ramakrishnan, Navneeth and Berta, Mario and Tomamichel, Marco (2022) One-Shot Point-to-Point Channel Simulation. In: 2022 IEEE International Symposium on Information Theory (ISIT). IEEE , Piscataway, NJ, pp. 796-801. ISBN 978-1-6654-2159-1. https://resolver.caltech.edu/CaltechAUTHORS:20220804-765722000

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Abstract

We study the problem of one-shot channel simulation of DMCs with unlimited shared randomness. For any fixed tolerance measured in total variational distance, we propose an achievability bound and a converse bound on the size of the code to simulate the channel. The achievability bound utilizes the convex split lemma, whereas the converse bound is the result of the relationships between smoothed max-divergences and the max-mutual information. The achievability proof does not rely on a "universal state" (compared with some previous related works), and provides a tighter bound. Using the two bounds, we also provide an alternative proof to the reverse Shannon theorem.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/ISIT50566.2022.9834411DOIArticle
ORCID:
AuthorORCID
Ramakrishnan, Navneeth0000-0002-7119-1989
Berta, Mario0000-0002-0428-3429
Tomamichel, Marco0000-0001-5410-3329
Additional Information:© 2022 IEEE. This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore and the Ministry of Education, Singapore under the Research Centres of Excellence programme. MC and MT are also supported by NUS startup grants (R-263-000-E32-133 and R-263-000-E32-731).
Group:Institute for Quantum Information and Matter, AWS Center for Quantum Computing
Funders:
Funding AgencyGrant Number
National Research Foundation (Singapore)UNSPECIFIED
Ministry of Education (Singapore)UNSPECIFIED
National University of SingaporeR-263-000-E32-133
National University of SingaporeR-263-000-E32-731
DOI:10.1109/isit50566.2022.9834411
Record Number:CaltechAUTHORS:20220804-765722000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220804-765722000
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:116091
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:04 Aug 2022 22:40
Last Modified:04 Aug 2022 22:40

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