用中性原子量子计算机实现事件相机的异步图神经网络，提升处理效率。
Analog Quantum Asynchronous Event-Based Graph Neural Network
- 将事件流映射为原子阵列，利用原子间相互作用模拟图神经网络消息传递。
- 通过类比哈密顿量动态并行计算，实现高效异步事件图推理。
- 适合量子机器学习、事件视觉与高时效数据处理的研究者参考。
异步事件图神经网络（AEGNN）是处理事件相机产生的稀疏高时间分辨率数据的高效范式。本文提出量子模拟型AEGNN（QA-AEGNN），在中性原子量子计算机上实现AEGNN。中性原子处理器基于可控的里德伯原子相互作用，提供可编程的模拟量子计算平台。我们把流式事件数据映射到囚禁原子阵列中，每个原子代表一个图节点（事件），其空间位置反映事件的时空邻近关系。量子处理器的本征里德伯哈密顿量被编程以匹配AEGNN的消息传递计算，原子量子态作为节点特征嵌入，原子间相互作用实现图边。此外，提出一种混合量子-经典训练方案，通过经典反馈优化哈密顿量参数（如激光脉冲幅度和失谐），从数据中学习量子AEGNN模型。该方法利用中性原子系统的连续哈密顿动力学和大规模并行性，原生执行事件图计算，具备潜在精度提升优势。
原文摘要 · Abstract (English)
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
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