All-Optical Reservoir Computing via a Nonlinear Sagnac Interferometer for Machine Learning Applications
We present an all-optical reservoir computing device based on a nonlinear Sagnac interferometer. By leveraging multiple wavelength input channels, we demonstrate high accuracy in machine learning prediction tasks, as exemplified by the Mackey-Glass time series benchmark.
Authors
- Roberto Morandotti (ORCID: https://orcid.org/0000-0001-7717-1519)
- Armaghan Eshaghi (ORCID: https://orcid.org/0009-0000-4293-1223)
- Imtiaz Alamgir (ORCID: https://orcid.org/0000-0002-3611-8970)
- Sai T. Chu (ORCID: https://orcid.org/0000-0001-8263-8507)
- Nicolas Perron (ORCID: https://orcid.org/0000-0002-4172-6639)
- Mario Chemnitz (ORCID: https://orcid.org/0000-0002-8340-7214)
- Evgeny A. Viktorov (ORCID: https://orcid.org/0000-0002-4606-2376)
- Bennet Fischer (ORCID: https://orcid.org/0000-0002-1186-0167)
- Celine Mazoukh (ORCID: https://orcid.org/0009-0002-6536-0552)
- Luigi Di Lauro (ORCID: https://orcid.org/0000-0003-0107-2738)
- Pavel Dmitriev
- Anton V. Kovalev (ORCID: https://orcid.org/0000-0001-7848-8526)
- Brent E. Little (ORCID: https://orcid.org/0000-0002-6031-4509)
- A. Aadhi
- Piotr Roztocki
- David Moss
- Shervin Vakili
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/cleo_fs.2025.ff142_3
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00