For quantum repeater chains with classical communication delays, predictive and reinforcement-learning policies that act on partial information deliver end-to-end entanglement faster than wait-for-broadcast swap-asap at high success probabilities.
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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning
For quantum repeater chains with classical communication delays, predictive and reinforcement-learning policies that act on partial information deliver end-to-end entanglement faster than wait-for-broadcast swap-asap at high success probabilities.