Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912423777140736 |
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| author | Ermolaev, Andrei V. Hary, Mathilde Leybov, Lev Ryczkowski, Piotr Skalli, Anas Brunner, Daniel Genty, Goëry Dudley, John M. |
| author_facet | Ermolaev, Andrei V. Hary, Mathilde Leybov, Lev Ryczkowski, Piotr Skalli, Anas Brunner, Daniel Genty, Goëry Dudley, John M. |
| contents | We report a generalized nonlinear Schrödinger 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03649 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine Ermolaev, Andrei V. Hary, Mathilde Leybov, Lev Ryczkowski, Piotr Skalli, Anas Brunner, Daniel Genty, Goëry Dudley, John M. Optics Machine Learning We report a generalized nonlinear Schrödinger 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. |
| title | Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine |
| topic | Optics Machine Learning |
| url | https://arxiv.org/abs/2503.03649 |