SatQNet uses decentralized RL with an edge-centric directed line graph neural network to route entanglements in dynamic satellite-assisted quantum networks, outperforming heuristics and generalizing to unseen topologies.
A Multiple-Entanglement Routing Framework for Quantum Networks
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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.
citing papers explorer
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SatQNet: Satellite-assisted Quantum Network Entanglement Routing Using Directed Line Graph Neural Networks
SatQNet uses decentralized RL with an edge-centric directed line graph neural network to route entanglements in dynamic satellite-assisted quantum networks, outperforming heuristics and generalizing to unseen topologies.
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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.