报告人：Dr. Michael Xuan Cao 博士后 RWTH Aachen University

报告题目：Channel Simulation: Tight meta converse for error and strong converse exponents

时间：2025年7月24日10:30-12:00

地点：数学楼423会议室

摘要：

    We determine the exact error and strong converse exponents of shared randomness-assisted channel simulation in worst case total-variation distance. Namely, we find that these exponents can be written as simple optimizations over the Rényi channel mutual information. Strikingly, and in stark contrast to channel coding, there are no critical rates, allowing a tight characterization for arbitrary rates below and above the simulation capacity. We derive our results by asymptotically expanding the meta-converse for channel simulation [Cao et al., IEEE Trans. Inf. Theory (2024)], which corresponds to non-signaling assisted codes. We prove this to be asymptotically tight by employing the approximation algorithms from [Berta et al., Proc. IEEE ISIT (2024)], which show how to round any non-signaling assisted strategy to a strategy that only uses shared randomness. Notably, this implies that any additional quantum entanglement-assistance does not change the error or the strong converse exponents.

报告人简介：

    Dr. Michael Xuan Cao is currently a research fellow at the Institute for Quantum Information, RWTH Aachen University. He works in the field of classical and quantum Shannon theory, with a particular focus on finite-blocklength problems, especially in network settings. He also has a long-term interest in the connections between statistical graphical models, belief propagation, and quantum Gibbs sampling. He received his Ph.D. in Information Engineering (2021), Bachelor of Engineering in Information Engineering (2014), and Bachelor of Science in Mathematics (2014), all from the Chinese University of Hong Kong.

邀请人：李永龙 教授