ArticleScience advances2022
All-optical graph representation learning using integrated diffractive photonic computing units.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- High-precision integrated diffractive optical networks enabling regression analysis.Nature communications · 2026Article
- Photonic Mixture-of-Experts for scalable multi-task on-chip optical neural networks.Nature communications · 2026Article
- Diffractive Optical Neural Networks: Pathways Toward Intelligent and Scalable Photonic Computing.Nanophotonics (Berlin, Germany) · 2026Review
- A review of applications of machine learning in quantum dots research.Discover nano · 2026Review
- Femto-joule threshold reconfigurable all-optical nonlinear activators for picosecond pulsed optical neural networks.Light, science & applications · 2026Article
- A multi-scale biological network framework for discovering MiRNA-Disease associations.Frontiers in bioinformatics · 2026Article
- High computational density nanophotonic media for machine learning inference.Nature communications · 2025Article
- Programmable diffractive deep neural networks enabled by integrated rewritable metasurfaces.Scientific reports · 2025Article
- Ultra-compact multi-task processor based on in-memory optical computing.Light, science & applications · 2025Article
- Scalable on-chip diffractive speckle spectrometer with high spectral channel density.Light, science & applications · 2025Article
- Empowering nanophotonic applications via artificial intelligence: pathways, progress, and prospects.Nanophotonics (Berlin, Germany) · 2025Review
- Optical neural networks: progress and challenges.Light, science & applications · 2024Review
- Article
- Polarization and wavelength routers based on diffractive neural network.Frontiers of optoelectronics · 2024Article
- Nanowatt all-optical 3D perception for mobile robotics.Science advances · 2024Article
- Demixing microwave signals using system-on-chip photonic processor.Light, science & applications · 2024Article
- Photonic neuromorphic architecture for tens-of-task lifelong learning.Light, science & applications · 2024Article
- Correlated optical convolutional neural network with "quantum speedup".Light, science & applications · 2024Article
- Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks.Nature communications · 2023Article
- High-throughput terahertz imaging: progress and challenges.Light, science & applications · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Photonic neural networks perform brain-inspired computations using photons instead of electrons to achieve substantially improved computing performance. However, existing architectures can only handle data with regular structures but fail to generalize to graph-structured data beyond Euclidean space. Here, we propose the diffractive graph neural network (DGNN), an all-optical graph representation learning architecture based on the diffractive photonic computing units (DPUs) and on-chip optical devices to address this limitation. Specifically, the graph node attributes are encoded into strip optical waveguides, transformed by DPUs, and aggregated by optical couplers to extract their feature representations. DGNN captures complex dependencies among node neighborhoods during the light-speed optical message passing over graph structures. We demonstrate the applications of DGNN 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 large-scale graph data structures using deep learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.