OSCR

All-optical computing towards 100-GHz clock rates.

Overview

Authors: Gordon H Y Li1, Midya Parto2,3,4, Jinhao Ge5, Qing-Xin Ji5, Maodong Gao3,5, Yan Yu5, James Williams2, Robert M Gray2, Christian R Leefmans1, Nicolas Englebert2, Kerry J Vahala5, Alireza Marandi1,2
  1. Department of Applied Physics, California Institute of Technology, Pasadena, 91125 CA USA
  2. Department of Electrical Engineering, California Institute of Technology, Pasadena, 91125 CA USA
  3. Physics and Informatics Laboratories, NTT Research, Inc., Sunnyvale, 94085 CA USA
  4. CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, FL USA
  5. T. J. Watson Laboratory of Applied Physics, California Institute of Technology, Pasadena, 91125 CA USA
Institutions: California Institute of Technology (United States); University of Central Florida (United States); NTT (United States) (United States)
Journal: Light, science & applications, volume 15, issue 1, article 321
Dates: received 8 April 2025; accepted 13 April 2026; published online 17 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41377-026-02314-5 · PMID 42469215 · PMCID PMC13379394 · OpenAlex W7169589779
Open access: gold, a free copy (OpenAlex)
Status: code on request
Methods: Spectral & time-frequency
Keywords: Nonlinear optics, Ultrafast photonics
Topic: Neural Networks and Reservoir Computing (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (ARO) (W911NF-23-1-0048); National Science Foundation (NSF) (1918549)
Citations: not cited yet (Europe PMC); 76 references in the paper

Abstract

A computer’s clock rate ultimately determines the minimum time between sequential operations or instructions. Despite exponential advances in electronic computer performance due to Moore’s Law and increasingly parallel system architectures, clock rates of conventional processors have remained stagnant at ~5 GHz for nearly two decades. This creates a significant challenge for applications requiring real-time processing or control of ultrafast information systems. Here, we break this barrier by proposing and experimentally demonstrating computing based on an all-optical recurrent neural network leveraging the ultrafast characteristics of linear and nonlinear optical operations while circumventing electronic bottlenecks. The all-optical computer realizes linear operations, nonlinear functions, and memory entirely in the optical domain with accuracy surpassing a purely linear model up to 80 GHz clock rates depending on the task. We experimentally demonstrate a prototypical task of noisy waveform classification as well as perform ultrafast in-situ analysis of the soliton states from integrated optical microresonators. We further illustrate the application of the architecture for generative artificial intelligence based on quantum fluctuations to generate images even in the absence of input optical signals. Our results highlight the potential of all-optical computing beyond what can be achieved with digital electronics by utilizing ultrafast linear, nonlinear, and memory functions and quantum fluctuations.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

Code availability

The code used to analyze the data and generate the plots for this paper is available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

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Data

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Data availability

The data used to generate the plots and results in this paper are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 2 keywords, 2 funders, 36 references.

Cite

This paper

Li, G. H. Y., Parto, M., Ge, J., Ji, Q.-X., Gao, M., Yu, Y., Williams, J., Gray, R. M., Leefmans, C. R., Englebert, N., Vahala, K. J., & Marandi, A. (2026). All-optical computing towards 100-GHz clock rates. Light, science & applications, 15(1), 321. https://doi.org/10.1038/s41377-026-02314-5

BibTeX

@article{li2026all,
author = {Li, Gordon H Y and Parto, Midya and Ge, Jinhao and Ji, Qing-Xin and Gao, Maodong and Yu, Yan and Williams, James and Gray, Robert M and Leefmans, Christian R and Englebert, Nicolas and Vahala, Kerry J and Marandi, Alireza},
title = {{All-optical computing towards 100-GHz clock rates}},
journal = {Light, science \& applications},
year = {2026},
month = jul,
volume = {15},
number = {1},
pages = {321},
publisher = {Nature Publishing Group},
issn = {2095-5545},
doi = {10.1038/s41377-026-02314-5},
url = {https://doi.org/10.1038/s41377-026-02314-5},
pmid = {42469215},
pmcid = {PMC13379394}
}

RIS

TY - JOUR
AU - Li, Gordon H Y
AU - Parto, Midya
AU - Ge, Jinhao
AU - Ji, Qing-Xin
AU - Gao, Maodong
AU - Yu, Yan
AU - Williams, James
AU - Gray, Robert M
AU - Leefmans, Christian R
AU - Englebert, Nicolas
AU - Vahala, Kerry J
AU - Marandi, Alireza
TI - All-optical computing towards 100-GHz clock rates
T2 - Light, science & applications
J2 - Light Sci Appl
PY - 2026
DA - 2026/07/17
VL - 15
IS - 1
SP - 321
SN - 2095-5545
PB - Nature Publishing Group
DO - 10.1038/s41377-026-02314-5
UR - https://doi.org/10.1038/s41377-026-02314-5
LA - en
ER -

CSL-JSON

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