ArticleNature photonics2024
Nonlinear processing with linear optics.
Article in Nature photonics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Article
- Quantum nonlinearity for optical neural computing.Nature communications · 2026Article
- Dual-Mode MOSCAP Silicon Microring Resonator for Energy-Efficient Analog Optical Computing.ACS photonics · 2026Article
- Parallel nonlinear neuromorphic computing with temporal encoding.Science advances · 2026Article
- Broadband, Compact, and Training-Free Optical Processors for Parallel Image Classification.ACS nano · 2026Article
- Compact photonic spiking neuron with inherent stochasticity based on phase-change material for probabilistic computing.Nature communications · 2026Article
- Integrated van der Waals waveguides for all-optical nonlinear photonic circuits.Nature communications · 2026Article
- Time-synthetic optical neural networks with stable programmable gain.Nature communications · 2026Article
- Diffractive Optical Neural Networks: Pathways Toward Intelligent and Scalable Photonic Computing.Nanophotonics (Berlin, Germany) · 2026Review
- Excited State Opto-Ionic Reservoir Computing in Hybrid Perovskite Electrochemically-Gated Luminescent Cells.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Nonlinear optical extreme learner via data reverberation with incoherent light.Science advances · 2026Article
- Compact Optical Reservoir Computing via Luminescence Dynamics in Rare-Earth Ions-Doped Nanocrystals.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Physical neural networks using sharpness-aware training.Nature communications · 2026Article
- Harnessing optical bound states in the continuum for ultrafast, reconfigurable, long-range photonic networks.Light, science & applications · 2026Article
- Metaoptics merging computational optics and optical computing toward intelligent visual perception.Science advances · 2026Review
- Model-free optical processors using in situ reinforcement learning with proximal policy optimization.Light, science & applications · 2026Article
- Physical Interpretation of Diffractive Optical Networks for High-Dimensional Vortex Mode Sorting.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Optical neural networks with intensity‑based projection layers as effective nonlinear activations.Scientific reports · 2025Article
- Multilayer nonlinear diffraction neural networks with programmable and fast ReLU activation function.Nature communications · 2025Article
- Hundred-layer photonic deep learning.Nature communications · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed, the optical implementation of neural networks aims to harness the advantages of optical bandwidth and the energy efficiency of optical interconnections. In the absence of low-power optical nonlinearities, the challenge in the implementation of multilayer optical networks lies in realizing multiple optical layers without resorting to electronic components. Here we present a novel framework that uses multiple scattering, and which is capable of synthesizing programmable linear and nonlinear transformations concurrently at low optical power by leveraging the nonlinear relationship between the scattering potential, represented by data, and the scattered field. Theoretical and experimental investigations show that repeating the data by multiple scattering enables nonlinear optical computing with low-power continuous-wave light. Moreover, we empirically find that scaling of this optical framework follows a power law.
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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.