Photonics Breakthroughs 2024: Nonlinear Photonic Computing at Scale
A photonic neural network utilizes photons instead of electrons to process information, with the prospect of higher computing efficiency, lower power consumption, and reduced latency. This paper reviews several recent breakthroughs in large-scale photonic neural networks incorporating nonlinear oper...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10909496/ |
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| author | Hao Wang Jianqi Hu Andrea Morandi Alfonso Nardi Fei Xia Xuanchen Li Romolo Savo Qiang Liu Rachel Grange Sylvain Gigan |
| author_facet | Hao Wang Jianqi Hu Andrea Morandi Alfonso Nardi Fei Xia Xuanchen Li Romolo Savo Qiang Liu Rachel Grange Sylvain Gigan |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | A photonic neural network utilizes photons instead of electrons to process information, with the prospect of higher computing efficiency, lower power consumption, and reduced latency. This paper reviews several recent breakthroughs in large-scale photonic neural networks incorporating nonlinear operations. Specifically, we highlight our recent work, which leverages multiple light scattering and second harmonic generation in a slab of disordered lithium niobate nanocrystals for high-performance nonlinear photonic computing. The interplay of these optical effects not only enhances the computational capabilities of photonic neural networks but also increases the number of photonic computing operations. In addition, we discuss current challenges and outline future directions of nonlinear photonic computing. These advancements pave the way for exploring new frontiers in optical computing, unlocking opportunities for innovative experimental implementations, broad applications, and theoretical foundations of photonic neural networks. |
| format | Article |
| id | doaj-art-d9b9d6fb00154963bddebe0d0b78811b |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-d9b9d6fb00154963bddebe0d0b78811b2025-08-20T03:53:27ZengIEEEIEEE Photonics Journal1943-06552025-01-011721410.1109/JPHOT.2025.354794810909496Photonics Breakthroughs 2024: Nonlinear Photonic Computing at ScaleHao Wang0https://orcid.org/0000-0003-0210-3372Jianqi Hu1https://orcid.org/0000-0002-2018-253XAndrea Morandi2https://orcid.org/0000-0001-8199-1536Alfonso Nardi3https://orcid.org/0000-0001-9610-9358Fei Xia4Xuanchen Li5https://orcid.org/0009-0002-9830-2332Romolo Savo6https://orcid.org/0000-0001-7221-5869Qiang Liu7Rachel Grange8https://orcid.org/0000-0001-7469-9756Sylvain Gigan9https://orcid.org/0000-0002-9914-6231Laboratoire Kastler Brossel, École Normale Supérieure—Paris Sciences et Lettres Research University, Sorbonne Université, Centre National de la Recherche Scientifique, UMR 8552, Collège de France, Paris, FranceLaboratoire Kastler Brossel, École Normale Supérieure—Paris Sciences et Lettres Research University, Sorbonne Université, Centre National de la Recherche Scientifique, UMR 8552, Collège de France, Paris, FranceETH Zurich, Institute for Quantum Electronics, Department of Physics, Optical Nanomaterial Group, Zurich, SwitzerlandETH Zurich, Institute for Quantum Electronics, Department of Physics, Optical Nanomaterial Group, Zurich, SwitzerlandLaboratoire Kastler Brossel, École Normale Supérieure—Paris Sciences et Lettres Research University, Sorbonne Université, Centre National de la Recherche Scientifique, UMR 8552, Collège de France, Paris, FranceETH Zurich, Institute for Quantum Electronics, Department of Physics, Optical Nanomaterial Group, Zurich, SwitzerlandETH Zurich, Institute for Quantum Electronics, Department of Physics, Optical Nanomaterial Group, Zurich, SwitzerlandState Key Laboratory of Precision Space–Time Information Sensing Technology, Department of Precision Instrument, Tsinghua University, Beijing, ChinaETH Zurich, Institute for Quantum Electronics, Department of Physics, Optical Nanomaterial Group, Zurich, SwitzerlandLaboratoire Kastler Brossel, École Normale Supérieure—Paris Sciences et Lettres Research University, Sorbonne Université, Centre National de la Recherche Scientifique, UMR 8552, Collège de France, Paris, FranceA photonic neural network utilizes photons instead of electrons to process information, with the prospect of higher computing efficiency, lower power consumption, and reduced latency. This paper reviews several recent breakthroughs in large-scale photonic neural networks incorporating nonlinear operations. Specifically, we highlight our recent work, which leverages multiple light scattering and second harmonic generation in a slab of disordered lithium niobate nanocrystals for high-performance nonlinear photonic computing. The interplay of these optical effects not only enhances the computational capabilities of photonic neural networks but also increases the number of photonic computing operations. In addition, we discuss current challenges and outline future directions of nonlinear photonic computing. These advancements pave the way for exploring new frontiers in optical computing, unlocking opportunities for innovative experimental implementations, broad applications, and theoretical foundations of photonic neural networks.https://ieeexplore.ieee.org/document/10909496/Machine learningneural networksnonlinear opticsoptical computing |
| spellingShingle | Hao Wang Jianqi Hu Andrea Morandi Alfonso Nardi Fei Xia Xuanchen Li Romolo Savo Qiang Liu Rachel Grange Sylvain Gigan Photonics Breakthroughs 2024: Nonlinear Photonic Computing at Scale IEEE Photonics Journal Machine learning neural networks nonlinear optics optical computing |
| title | Photonics Breakthroughs 2024: Nonlinear Photonic Computing at Scale |
| title_full | Photonics Breakthroughs 2024: Nonlinear Photonic Computing at Scale |
| title_fullStr | Photonics Breakthroughs 2024: Nonlinear Photonic Computing at Scale |
| title_full_unstemmed | Photonics Breakthroughs 2024: Nonlinear Photonic Computing at Scale |
| title_short | Photonics Breakthroughs 2024: Nonlinear Photonic Computing at Scale |
| title_sort | photonics breakthroughs 2024 nonlinear photonic computing at scale |
| topic | Machine learning neural networks nonlinear optics optical computing |
| url | https://ieeexplore.ieee.org/document/10909496/ |
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