Polariton lattices as binarized neuromorphic networks
Abstract We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates, intricately interconnected and energized through nonresonant optical pumping. The network employs a binary framework, where each neuron, facilitated by the spatial coherence of pairwi...
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025-01-01
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| Series: | Light: Science & Applications |
| Online Access: | https://doi.org/10.1038/s41377-024-01719-4 |
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| _version_ | 1832594451463143424 |
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| author | Evgeny Sedov Alexey Kavokin |
| author_facet | Evgeny Sedov Alexey Kavokin |
| author_sort | Evgeny Sedov |
| collection | DOAJ |
| description | Abstract We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates, intricately interconnected and energized through nonresonant optical pumping. The network employs a binary framework, where each neuron, facilitated by the spatial coherence of pairwise coupled condensates, performs binary operations. This coherence, emerging from the ballistic propagation of polaritons, ensures efficient, network-wide communication. The binary neuron switching mechanism, driven by the nonlinear repulsion through the excitonic component of polaritons, offers computational efficiency and scalability advantages over continuous weight neural networks. Our network enables parallel processing, enhancing computational speed compared to sequential or pulse-coded binary systems. The system’s performance was evaluated using diverse datasets, including the MNIST dataset for image recognition and the Speech Commands dataset for voice recognition tasks. In both scenarios, the proposed system demonstrates the potential to outperform existing polaritonic neuromorphic systems. For image recognition, this is evidenced by an impressive predicted classification accuracy of up to 97.5%. In voice recognition, the system achieved a classification accuracy of about 68% for the ten-class subset, surpassing the performance of conventional benchmark, the Hidden Markov Model with Gaussian Mixture Model. |
| format | Article |
| id | doaj-art-f65679fe91f340e28527605d84318075 |
| institution | Kabale University |
| issn | 2047-7538 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Light: Science & Applications |
| spelling | doaj-art-f65679fe91f340e28527605d843180752025-01-19T12:39:12ZengNature Publishing GroupLight: Science & Applications2047-75382025-01-0114111710.1038/s41377-024-01719-4Polariton lattices as binarized neuromorphic networksEvgeny Sedov0Alexey Kavokin1Spin-Optics laboratory, St. Petersburg State UniversitySpin-Optics laboratory, St. Petersburg State UniversityAbstract We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates, intricately interconnected and energized through nonresonant optical pumping. The network employs a binary framework, where each neuron, facilitated by the spatial coherence of pairwise coupled condensates, performs binary operations. This coherence, emerging from the ballistic propagation of polaritons, ensures efficient, network-wide communication. The binary neuron switching mechanism, driven by the nonlinear repulsion through the excitonic component of polaritons, offers computational efficiency and scalability advantages over continuous weight neural networks. Our network enables parallel processing, enhancing computational speed compared to sequential or pulse-coded binary systems. The system’s performance was evaluated using diverse datasets, including the MNIST dataset for image recognition and the Speech Commands dataset for voice recognition tasks. In both scenarios, the proposed system demonstrates the potential to outperform existing polaritonic neuromorphic systems. For image recognition, this is evidenced by an impressive predicted classification accuracy of up to 97.5%. In voice recognition, the system achieved a classification accuracy of about 68% for the ten-class subset, surpassing the performance of conventional benchmark, the Hidden Markov Model with Gaussian Mixture Model.https://doi.org/10.1038/s41377-024-01719-4 |
| spellingShingle | Evgeny Sedov Alexey Kavokin Polariton lattices as binarized neuromorphic networks Light: Science & Applications |
| title | Polariton lattices as binarized neuromorphic networks |
| title_full | Polariton lattices as binarized neuromorphic networks |
| title_fullStr | Polariton lattices as binarized neuromorphic networks |
| title_full_unstemmed | Polariton lattices as binarized neuromorphic networks |
| title_short | Polariton lattices as binarized neuromorphic networks |
| title_sort | polariton lattices as binarized neuromorphic networks |
| url | https://doi.org/10.1038/s41377-024-01719-4 |
| work_keys_str_mv | AT evgenysedov polaritonlatticesasbinarizedneuromorphicnetworks AT alexeykavokin polaritonlatticesasbinarizedneuromorphicnetworks |