Proposes a hybrid quantum-classical framework for running event-based graph neural networks on neutral-atom processors by mapping events to atoms and programming the Rydberg Hamiltonian to realize message passing.
Local Rydberg blockade regimes for disk graph embedding and quantum optimization,
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A divide-and-conquer heuristic enables solving MWIS instances from molecular docking with graphs of 225-585 vertices on neutral-atom quantum emulators, outperforming greedy baselines and recovering provably optimal solutions on some instances.
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Analog Quantum Asynchronous Event-Based Graph Neural Network
Proposes a hybrid quantum-classical framework for running event-based graph neural networks on neutral-atom processors by mapping events to atoms and programming the Rydberg Hamiltonian to realize message passing.
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A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors
A divide-and-conquer heuristic enables solving MWIS instances from molecular docking with graphs of 225-585 vertices on neutral-atom quantum emulators, outperforming greedy baselines and recovering provably optimal solutions on some instances.