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arXiv · 2503.03649

Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine

Andrei V. Ermolaev·Mathilde Hary·Lev Leybov·Piotr Ryczkowski·Anas Skalli·Daniel Brunner·Goëry Genty·John M. Dudley
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Abstract

We report a generalized nonlinear Schr\"odinger equation simulation model of an extreme learning machine (ELM) based on optical fiber propagation. Using the MNIST handwritten digit dataset as a benchmark, we study how accuracy depends on propagation dynamics, as well as parameters governing spectral encoding, readout, and noise. For this dataset and with quantum noise limited input, test accuracies of : over 91% and 93% are found for propagation in the anomalous and normal dispersion regimes respectively. Our results also suggest that quantum noise on the input pulses introduces an intrinsic penalty to ELM performance.

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Andrei V. Ermolaev, Mathilde Hary, Lev Leybov, Piotr Ryczkowski, Anas Skalli, Daniel Brunner, Goëry Genty, John M. Dudley. 2025-03-05. Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine. https://doi.org/10.1364/ol.562186

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