Multimode fiber laser cavities as nonlinear optical processors
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910017672708096 |
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| author | Eşlik, Dilem Kesgin, Bahadır Utku Kılınç, Fatma Nur Teğin, Uğur |
| author_facet | Eşlik, Dilem Kesgin, Bahadır Utku Kılınç, Fatma Nur Teğin, Uğur |
| contents | Optical computing provides a promising path toward energy-efficient machine learning, yet implementing nonlinear transformations without complex electronics or high-power sources remains challenging. Here, we demonstrate that continuous-wave multimode fiber laser cavities can function as nonlinear optical processors. Input images encoded as phase patterns on a spatial light modulator undergo high-dimensional transformation through the interplay of multimode interference and gain saturation dynamics. The cavity maps input data into spatially stable, class-separable intensity distributions, enabling a simple linear classifier to achieve accuracies of 85--99\% across diverse benchmarks -- including medical imaging and remote sensing -- with orders of magnitude fewer trainable parameters than deep neural networks. Our results establish multimode fiber lasers as compact, low-power physical processors for scalable optical machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09519 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multimode fiber laser cavities as nonlinear optical processors Eşlik, Dilem Kesgin, Bahadır Utku Kılınç, Fatma Nur Teğin, Uğur Optics Optical computing provides a promising path toward energy-efficient machine learning, yet implementing nonlinear transformations without complex electronics or high-power sources remains challenging. Here, we demonstrate that continuous-wave multimode fiber laser cavities can function as nonlinear optical processors. Input images encoded as phase patterns on a spatial light modulator undergo high-dimensional transformation through the interplay of multimode interference and gain saturation dynamics. The cavity maps input data into spatially stable, class-separable intensity distributions, enabling a simple linear classifier to achieve accuracies of 85--99\% across diverse benchmarks -- including medical imaging and remote sensing -- with orders of magnitude fewer trainable parameters than deep neural networks. Our results establish multimode fiber lasers as compact, low-power physical processors for scalable optical machine learning. |
| title | Multimode fiber laser cavities as nonlinear optical processors |
| topic | Optics |
| url | https://arxiv.org/abs/2602.09519 |