All-optical computing towards 100-GHz clock rates.
Overview
- Department of Applied Physics, California Institute of Technology, Pasadena, 91125 CA USA
- Department of Electrical Engineering, California Institute of Technology, Pasadena, 91125 CA USA
- Physics and Informatics Laboratories, NTT Research, Inc., Sunnyvale, 94085 CA USA
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, FL USA
- T. J. Watson Laboratory of Applied Physics, California Institute of Technology, Pasadena, 91125 CA USA
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
A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.
Data
No dataset and no data link were found in the paper.
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
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
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://
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/
url = {https://
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/
VL - 15
IS - 1
SP - 321
SN - 2095-5545
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.1038/
"type": "article-journal",
"title": "All-optical computing towards 100-GHz clock rates",
"container-title": "Light, science & applications",
"author": [
{
"family": "Li",
"given": "Gordon H Y"
},
{
"family": "Parto",
"given": "Midya"
},
{
"family": "Ge",
"given": "Jinhao"
},
{
"family": "Ji",
"given": "Qing-Xin"
},
{
"family": "Gao",
"given": "Maodong"
},
{
"family": "Yu",
"given": "Yan"
},
{
"family": "Williams",
"given": "James"
},
{
"family": "Gray",
"given": "Robert M"
},
{
"family": "Leefmans",
"given": "Christian R"
},
{
"family": "Englebert",
"given": "Nicolas"
},
{
"family": "Vahala",
"given": "Kerry J"
},
{
"family": "Marandi",
"given": "Alireza"
}
],
"container-title-short":
"volume": "15",
"issue": "1",
"page": "321",
"DOI": "10.1038/
"PMID": "42469215",
"PMCID": "PMC13379394",
"ISSN": "2095-5545",
"publisher": "Nature Publishing Group",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
17
]
]
}
}
Similar papers
The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.
- [1] doi:10.1038/s41467-026-73070-8
- Compact photonic spiking neuron with inherent stochasticity based on phase-change material for probabilistic computing.Journal: Nature communicationsIn common: 2 references
- [2] doi:10.1038/s41467-026-75979-6 [code]
- Artificial neural manifolds.Journal: Nature communicationsIn common: 1 reference
- [3] doi:10.1126/sciadv.aea1712 [code]
- Structural vibration monitoring with diffractive optical processors.Journal: Science advancesIn common: 1 reference
- [4] doi:10.1126/sciadv.aef6263 [code]
- Deep learning-enabled versatile shape perception for soft robots via single-ended multimode fiber.Journal: Science advancesIn common: 1 reference
Contribute
The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.
Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.
Claim this paper
Correct its record
Say what each link of this record is, remove the ones that are not the paper's, add the ones that are missing. The correction becomes a new version of the record, in its Versions section.
Request its removal
To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).
Discussion, reproductions, activity
Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.
Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.
Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.
