Optical Kernel Machine With Programmable Nonlinearity
ABSTRACT Nonlinear optical kernel machines enable parallel processing of complex, nonlinear data, achieving optimal performance when the kernel nonlinearity is tailored to a specific task. However, in conventional nonlinear optical approaches, adjusting the kernel nonlinearity typically requires varying the optical power. To overcome this issue, we present an optical kernel with structural nonlinearity that can be continuously tuned at low power. It is implemented in a linear optical scattering cavity with a reconfigurable micro‐mirror array. By tuning the degree of nonlinearity through multiple scattering, we vary the kernel sensitivity and information capacity. We further optimize the kernel nonlinearity to best approximate parity functions from first order to fifth order for binary variables. Our scheme offers potential applicability across photonic platforms, providing programmable kernels with high performance and low power consumption.
Authors
- SeungYun Han (ORCID: https://orcid.org/0000-0003-3344-3214)
- Bruno Loureiro
- Hui Cao
- Fei Xia
- Sylvain Gigan
Institutions
- Centre National de la Recherche Scientifique (FR)
- University of California, Irvine (US)
- Laboratoire Kastler Brossel (FR)
- École Normale Supérieure - PSL (FR)
- Yale University (US)
- École Normale Supérieure (BI)
Publication Details
- Journal
- Laser & Photonics Review
- Published
- 2026-06-12
- DOI
- https://doi.org/10.1002/lpor.202503155
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Office of Naval Research