AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work presents an entanglement-assisted encoding and decoding strategy that yields a channel with higher worst-case fidelity than all of the given channels, and is universal, in the sense that the improvement holds regardless of what channels are given.
It is proved that accumulating and shuffling improves the expected success probability and the success-weighted output Bell fidelity over the baseline without accumulating and shuffling, for every finite number of accumulation rounds and in the asymptotic limit.
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