RELiQ uses reinforcement learning with graph neural networks for local-information entanglement routing in quantum networks and outperforms existing local heuristics on random and real-world topologies while matching or exceeding global heuristics due to fast adaptation.
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RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks
RELiQ uses reinforcement learning with graph neural networks for local-information entanglement routing in quantum networks and outperforms existing local heuristics on random and real-world topologies while matching or exceeding global heuristics due to fast adaptation.