Asynchronous, event-based graph neural networks (AEGNNs) have recently emerged as an efficient paradigm for processing the sparse and high-temporal-resolution data from event cameras. In this paper, we propose quantum analog AEGNNs (QA-AEGNNs), a novel framework to implement an AEGNN on a neutral-atom quantum computer. Neutral-atom quantum processors offer a programmable analog quantum computing platform based on controllable Rydberg-atom interactions. To this end, we map the streaming event data to an array of trapped neutral atoms, where each atom represents a graph node (event) and is positioned such that geometric proximity reflects the spatio-temporal neighborhood of events. The native Rydberg Hamiltonian 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. Furthermore, we propose a hybrid quantum-classical training scheme in which the analog Hamiltonian parameters (e.g., laser pulse amplitudes and detunings) are optimized using classical feedback to learn the quantum AEGNN model from data. Our approach leverages the continuous Hamiltonian dynamics and massive parallelism of neutral-atom quantum systems to natively execute event-based graph computations with potential accuracy improvements

翻译：异步事件基图神经网络（AEGNNs）近期作为处理事件相机产生的稀疏高时间分辨率数据的有效范式而兴起。本文提出量子模拟AEGNNs（QA-AEGNNs），一种在中性原子量子计算机上实现AEGNN的新型框架。中性原子量子处理器基于可控里德伯原子相互作用提供可编程的模拟量子计算平台。为此，我们将流式事件数据映射至俘获中性原子阵列，其中每个原子代表一个图节点（事件），通过几何邻近性反映事件的时空邻域。量子处理器的原生里德伯哈密顿量被编程以镜像AEGNN的消息传递计算过程，原子量子比特态作为节点特征嵌入，原子间相互作用实现图边。此外，我们提出混合量子-经典训练方案，其中利用经典反馈优化模拟哈密顿参数（如激光脉冲幅度和失谐量），以从数据中学习量子AEGNN模型。本方法利用中性原子量子系统的连续哈密顿动力学和大规模并行性，以原生方式执行基于事件的图计算，并具有潜在的精度提升。