Quantum Broadcast Channel Simulation via Multipartite Convex Splitting

We show that the communication cost of quantum broadcast channel simulation under free entanglement assistance between the sender and the receivers is asymptotically characterized by an efficiently computable single-letter formula in terms of the channel's multipartite mutual information. Our core contribution is a new one-shot achievability result for multipartite quantum state splitting via multipartite convex splitting. As part of this, we face a general instance of the quantum joint typicality problem with arbitrarily overlapping marginals. The crucial technical ingredient to sidestep this difficulty is a conceptually novel multipartite mean-zero decomposition lemma, together with employing recently introduced complex interpolation techniques for sandwiched Rényi divergences. Moreover, we establish an exponential convergence of the simulation error when the communication costs are within the interior of the capacity region. As the costs approach the boundary of the capacity region moderately quickly, we show that the error still vanishes asymptotically.

Authors

Institutions

Publication Details

Journal
Communications in Mathematical Physics
Published
2025-01-16
DOI
https://doi.org/10.1007/s00220-024-05191-4
Citations
6
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
9.92

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantum Broadcast Channel Simulation via Multipartite Convex Splitting

Mario Berta, Hao–Chung Cheng, Li Gao
6 citations
Communications in Mathematical Physics
Quantum Computing Algorithms and Architecture
9.92
article

Quantum Broadcast Channel Simulation via Multipartite Convex Splitting

Mario Berta, Hao–Chung Cheng, Li Gao
article en
6 citations

Abstract

We show that the communication cost of quantum broadcast channel simulation under free entanglement assistance between the sender and the receivers is asymptotically characterized by an efficiently computable single-letter formula in terms of the channel's multipartite mutual information. Our core contribution is a new one-shot achievability result for multipartite quantum state splitting via multipartite convex splitting. As part of this, we face a general instance of the quantum joint typicality problem with arbitrarily overlapping marginals. The crucial technical ingredient to sidestep this difficulty is a conceptually novel multipartite mean-zero decomposition lemma, together with employing recently introduced complex interpolation techniques for sandwiched Rényi divergences. Moreover, we establish an exponential convergence of the simulation error when the communication costs are within the interior of the capacity region. As the costs approach the boundary of the capacity region moderately quickly, we show that the error still vanishes asymptotically.

Communications in Mathematical PhysicsVol. 406(2)
National Taiwan University of Science and Technology (TW), National Taiwan University (TW), Wuhan University (CN), Foxconn (Taiwan) (TW), National Center for Theoretical Sciences (TW), National Center for Theoretical Sciences, Physics Division, RWTH Aachen University (DE), National Taipei University (TW)
National Science Foundation, National Natural Science Foundation of China, Ministry of Education, Ministry of Education, India, National Taiwan University, RWTH Aachen University, National Science and Technology Council, European Research Council, Division of Mathematical Sciences
Openalex Percentile: Top 3%
Quantum Computing Algorithms and Architecture
9.92
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.