All-optical computing towards 100-GHz clock rates
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.
Authors
- Alireza Marandi (ORCID: https://orcid.org/0000-0002-0470-0050)
- Kerry J. Vahala (ORCID: https://orcid.org/0000-0003-1783-1380)
- Maodong Gao (ORCID: https://orcid.org/0000-0002-7791-0008)
- Midya Parto (ORCID: https://orcid.org/0000-0003-2100-5671)
- Nicolas Englebert (ORCID: https://orcid.org/0000-0002-5170-9576)
- Gordon H.Y. Li (ORCID: https://orcid.org/0000-0001-8184-4915)
- Robert M. Gray (ORCID: https://orcid.org/0000-0001-5980-8774)
- Qing-Xin Ji (ORCID: https://orcid.org/0000-0002-6336-8350)
- Christian R. Leefmans
- Yan Yu (ORCID: https://orcid.org/0009-0003-4024-7171)
- Jinhao Ge
- James Williams
Institutions
- University of Central Florida (US)
- California Institute of Technology (US)
- 3D Technology Laboratories (United States) (US)
Publication Details
- Journal
- Light Science & Applications
- Published
- 2026-07-17
- DOI
- https://doi.org/10.1038/s41377-026-02314-5
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Army Research Office