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Entanglement Swapping for Repeater Chains with Finite Memory Sizes
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We develop entanglement swapping protocols and memory allocation methods for quantum repeater chains. Unlike most of the existing studies, the memory size of each quantum repeater in this work is a parameter that can be optimized. Based on Markov chain modeling of the entanglement distribution process, we determine the trade-off between the entanglement distribution rate and the memory size for temporal multiplexing techniques. We then propose three memory allocation methods that achieve entanglement distribution rates decaying polynomially with respect to the distance while using constant average memory slots per node. We also quantify the average number of memory slots required due to classical communication delay, as well as the delay of entanglement distribution. Our results show that a moderate memory size suffices to achieve a polynomial decay of entanglement distribution rate with respect to the distance, which is the scaling achieved by the optimal protocol even with infinite memory size at each node.
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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 ...
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