Identifying topology of leaky photonic lattices with machine learning
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916107948916736 |
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| author | Smolina, Ekaterina O. Smirnov, Lev A. Leykam, Daniel Nori, Franco Smirnova, Daria A. |
| author_facet | Smolina, Ekaterina O. Smirnov, Lev A. Leykam, Daniel Nori, Franco Smirnova, Daria A. |
| contents | We show how machine learning techniques can be applied for the classification of topological phases in leaky photonic lattices using limited measurement data. We propose an approach based solely on bulk intensity measurements, thus exempt from the need for complicated phase retrieval procedures. In particular, we design a fully connected neural network that accurately determines topological properties from the output intensity distribution in dimerized waveguide arrays with leaky channels, after propagation of a spatially localized initial excitation at a finite distance, in a setting that closely emulates realistic experimental conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_14407 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Identifying topology of leaky photonic lattices with machine learning Smolina, Ekaterina O. Smirnov, Lev A. Leykam, Daniel Nori, Franco Smirnova, Daria A. Optics Machine Learning Data Analysis, Statistics and Probability We show how machine learning techniques can be applied for the classification of topological phases in leaky photonic lattices using limited measurement data. We propose an approach based solely on bulk intensity measurements, thus exempt from the need for complicated phase retrieval procedures. In particular, we design a fully connected neural network that accurately determines topological properties from the output intensity distribution in dimerized waveguide arrays with leaky channels, after propagation of a spatially localized initial excitation at a finite distance, in a setting that closely emulates realistic experimental conditions. |
| title | Identifying topology of leaky photonic lattices with machine learning |
| topic | Optics Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2308.14407 |