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PreprintCC BY-NC-ND 4.0arXiv

Image recognition based on optical spike processing with exciton-polaritons

  • Olgierd Jeziorski
  • Jakub Rogala
  • Krzysztof Tyszka
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Abstract

Exciton-polariton condensates combine ultrafast dynamics with strong optical nonlinearities, making them promising for optical neuromorphic computing. Here, we numerically demonstrate image recognition using the transient response of an exciton-polariton condensate. Downscaled MNIST images are encoded as optical pulse sequences, and the resulting condensate dynamics are sampled and processed by a linear classifier. For ten-class classification, condensate features achieve 92.2% accuracy, compared with 91.4% for raw-pixel linear classification. Combining both feature sets increases the accuracy to 93.2%. For the challenging binary classification of digits 7 and 9, the combined model reaches 97.3%, exceeding both the linear (95.5%) and feed-forward neural-network (96.6%) baselines. These results demonstrate that polariton dynamics can extract useful nonlinear features for ultrafast optical information processing.

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Cite

BibTeXRIS
BibTeX
@misc{jeziorski2026image,
  title = {{Image recognition based on optical spike processing with exciton-polaritons}},
  author = {Olgierd Jeziorski and Jakub Rogala and Krzysztof Tyszka},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.09939},
  eprint = {2609.09939},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.09939},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - UNPB
TI  - Image recognition based on optical spike processing with exciton-polaritons
AU  - Olgierd Jeziorski
AU  - Jakub Rogala
AU  - Krzysztof Tyszka
PY  - 2026
DA  - 2026/09/09
PB  - arXiv
DO  - 10.48550/arxiv.2609.09939
UR  - https://arxiv.org/abs/2609.09939
AB  - Exciton-polariton condensates combine ultrafast dynamics with strong optical nonlinearities, making them promising for optical neuromorphic computing. Here, we numerically demonstrate image recognition using the transient response of an exciton-polariton condensate. Downscaled MNIST images are encoded as optical pulse sequences, and the resulting condensate dynamics are sampled and processed by a linear classifier. For ten-class classification, condensate features achieve 92.2% accuracy, compared with 91.4% for raw-pixel linear classification. Combining both feature sets increases the accuracy to 93.2%. For the challenging binary classification of digits 7 and 9, the combined model reaches 97.3%, exceeding both the linear (95.5%) and feed-forward neural-network (96.6%) baselines. These results demonstrate that polariton dynamics can extract useful nonlinear features for ultrafast optical information processing.
ER  -