用光子技术增强图神经网络，提升分子预测精度
Photonics-Enhanced Graph Convolutional Networks
- 通过光在虚拟频率晶格中传播生成节点间强度相关矩阵作为位置编码
- 在分子数据集上比传统方法降低6.3%误差，分类精度提升2.3%
- 适合追求光子加速的图学习研究者和高性能计算应用
光子技术可为机器学习提供硬件原生路径。但高效部署需融合光学处理与传统CPU/GPU神经网络架构。本文提出一种混合工作流，将光子位置编码（PEs）与先进图学习模型结合。通过模拟光在合成频率晶格上的传播与读出，获得节点间强度相关矩阵，作为图卷积网络（GCN）的全局结构编码。在长程图基准分子数据集上评估，相比基于拉普拉斯的位置编码基线，该方法在回归任务中实现6.3%更低的平均绝对误差，在分类任务中平均精度提升2.3%，使用两层GCN作为基线。若在高重复率光子硬件中实现，相关性测量可跳过数字仿真直接生成特征，实现快速特征提取。结果表明，光子位置编码能提升GCN性能，并支持图学习的光学加速。
原文摘要 · Abstract (English)
Photonics can offer a hardware-native route for machine learning (ML). However, efficient deployment of photonics-enhanced ML requires hybrid workflows that integrate optical processing with conventional CPU/GPU based neural network architectures. Here, we propose such a workflow that combines photonic positional embeddings (PEs) with advanced graph ML models. We introduce a photonics-based method that augments graph convolutional networks (GCNs) with PEs derived from light propagation on synthetic frequency lattices whose couplings match the input graph. We simulate propagation and readout to obtain internode intensity correlation matrices, which are used as PEs in GCNs to provide global structural information. Evaluated on Long Range Graph Benchmark molecular datasets, the method outperforms baseline GCNs with Laplacian based PEs, achieving $6.3\%$ lower mean absolute error for regression and $2.3\%$ higher average precision for classification tasks using a two-layer GCN as a baseline. When implemented in high repetition rate photonic hardware, correlation measurements can enable fast feature generation by bypassing digital simulation of PEs. Our results show that photonic PEs improve GCN performance and support optical acceleration of graph ML.
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