Speaker
Description
Asynchronous event-based graph neural networks (AEGNNs)
have recently emerged as an efficient paradigm for processing sparse
and high-temporal-resolution data from neuromorphic cameras. Here,
we propose quantum analog AEGNNs (QA-AEGNNs), a novel frame-
work to implement AEGNNs on neutral-atom quantum computers. The
streaming event data, produced by a neuromorphic camera, is mapped
to an array of trapped neutral atoms. Each atom represents a graph
node (event), and is positioned such that geometric proximity reflects
the spatio-temporal neighborhood of events. The native Rydberg Hamil-
tonian of the quantum processor is programmed to mirror the message-
passing computations of the AEGNN, with atomic qubit states serving
as node feature embeddings and inter-atom interactions realizing graph
edges. Overall, QA-AEGNNs leverage the continuous Hamiltonian dy-
namics of neutral-atom quantum systems to natively execute event-based
graph computations with potential accuracy improvements over classical
methods
| Topic | Quantum kernel methods and their applications |
|---|---|
| Additional Topic | Hybrid quantum-classical approaches for learning and optimization |