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.

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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

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article

Optical Kernel Machine With Programmable Nonlinearity

SeungYun Han, Bruno Loureiro, Hui Cao, Fei Xia et al.
Laser & Photonics Review
Neural Networks and Reservoir Computing
article

Optical Kernel Machine With Programmable Nonlinearity

SeungYun Han, Bruno Loureiro, Hui Cao, Fei Xia, Sylvain Gigan
article en

Abstract

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.

Laser & Photonics Review
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)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Office of Naval Research
Affordable and clean energy
Openalex Percentile: Top 4%
Neural Networks and Reservoir Computing
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