Photonics-Enhanced Graph Convolutional Networks

TL;DR

This paper introduces a hybrid photonics-graph neural network workflow that leverages light propagation-based positional embeddings to enhance GCN performance and enable optical acceleration, demonstrated on molecular graph datasets.

Contribution

It presents a novel photonics-based method for generating positional embeddings for GCNs using light propagation on synthetic frequency lattices, improving accuracy and speed.

Findings

Achieved 6.3% lower MAE in regression tasks.

Attained 2.3% higher average precision in classification.

Demonstrated potential for optical acceleration in graph ML.

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.