Photonics-Enhanced Graph Convolutional Networks

2025-12-17

AI Summary

To address the limited global structural modeling capability of Graph Neural Networks (GNNs), this paper proposes a hybrid architecture integrating Photonic Positional Encoding (PPE) with Graph Convolutional Networks (GCNs). PPE leverages light propagation in synthetic frequency lattices to generate intensity-based inter-node correlation matrices, serving as structure-aware positional encodings. This constitutes the first optical-native, low-latency graph structural encoding scheme, circumventing bottlenecks inherent in digital computation. Crucially, PPE is end-to-end differentiable and seamlessly embeddable into GCNs without additional training overhead. Evaluated on the Long Range Graph Benchmark molecular dataset, a two-layer GCN augmented with PPE achieves a 6.3% reduction in mean absolute error (MAE) for regression tasks and a 2.3% improvement in mean classification accuracy—outperforming Laplacian-based baselines. This work establishes a novel paradigm for photonic-hardware-accelerated graph representation learning.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Integrating photonic processing with conventional neural networks for efficient machine learning
• Enhancing graph convolutional networks using photonic positional embeddings from light propagation
• Improving GCN performance on molecular datasets via optical acceleration of graph ML

Innovation

• Photonics-based positional embeddings enhance graph convolutional networks
• Light propagation on synthetic lattices generates internode correlation matrices
• Hybrid optical-electronic workflow accelerates graph machine learning performance