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.
arXiv preprint arXiv:2404.19489 (2024).https://doi
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Multi-stage silicon retina on SCAMP-5 achieves 13% lower saliency prediction loss and 47% fewer events than standard DVS using a ~100k-parameter network.
FPGA hardware for event-graph NN achieves 92.7% accuracy on SHD dataset with fewer parameters than SOTA while outperforming prior FPGA SNNs.
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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.