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Exponents for Shared Randomness-Assisted Channel Simulation
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DOI：10.1109/tit.2026.3665384.png)
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Abstract
En 中文
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 R & eacute;nyi 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.
Keywords:
Distortion
Random variables
Decoding
Channel coding
Channel capacity
Vectors
Upper bound
Quantum entanglement
Quantum computing
Quantum channels
Information theory
channel capacity
mutual information
channel coding
Journal
I
IF:
2.9
Papers:
317
Citations:
0