Photonic neural networks perform brain-inspired computations using photons
instead of electrons that can achieve substantially improved computing
performance. However, existing architectures can only handle data with regular
structures, e.g., images or videos, but fail to generalize to graph-structured
data beyond Euclidean space, e.g., social networks or document co-citation
networks. Here, we propose an all-optical graph representation learning
architecture, termed diffractive graph neural network (DGNN), based on the
integrated diffractive photonic computing units (DPUs) to address this
limitation. Specifically, DGNN optically encodes node attributes into strip
optical waveguides, which are transformed by DPUs and aggregated by on-chip
optical couplers to extract their feature representations. Each DPU comprises
successive passive layers of metalines to modulate the electromagnetic optical
field via diffraction, where the metaline structures are learnable parameters
shared across graph nodes. DGNN captures complex dependencies among the node
neighborhoods and eliminates the nonlinear transition functions during the
light-speed optical message passing over graph structures. We demonstrate the
use of DGNN extracted features for node and graph-level classification tasks
with benchmark databases and achieve superior performance. Our work opens up a
new direction for designing application-specific integrated photonic circuits
for high-efficiency processing of large-scale graph data structures using deep
learning.

使用 photon 而非 electron 进行类脑计算的光子神经网络通常能实现更强的计算性能。本文提出了一种全光学图表示学习体系结构，称为衍射图神经网络（DGNN），它基于集成的衍射光子计算单元（DPUs），能够处理图结构数据。DGNN 捕获节点邻域之间的复杂依赖性，并通过图结构上的光速光传递消除非线性过渡函数。该工作为使用深度学习高效处理大规模图数据结构的应用特定集成光学电路设计开辟了一条新的道路。