Scalable physical deep learning using optical dynamics with state-skipping training and single-map architecture
- DOI
- 10.1038/s43588-026-01075-w
- Published
- 2026-11-05
- Container
- Nature Computational Science
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s43588-026-01075-w,
title = {Scalable physical deep learning using optical dynamics with state-skipping training and single-map architecture},
author = {Yongbo Zhang and Mitsumasa Nakajima and Katsuma Inoue and Toshikazu Hashimoto and Yasuo Kuniyoshi and Kohei Nakajima},
year = {2026},
journal = {Nature Computational Science},
doi = {10.1038/s43588-026-01075-w},
url = {https://doi.org/10.1038/s43588-026-01075-w}
}RIS
TY - JOUR TI - Scalable physical deep learning using optical dynamics with state-skipping training and single-map architecture AU - Yongbo Zhang AU - Mitsumasa Nakajima AU - Katsuma Inoue AU - Toshikazu Hashimoto AU - Yasuo Kuniyoshi AU - Kohei Nakajima PY - 2026 JO - Nature Computational Science DO - 10.1038/s43588-026-01075-w UR - https://doi.org/10.1038/s43588-026-01075-w ER -
APA
Zhang, Y., Nakajima, M., Inoue, K., Hashimoto, T., Kuniyoshi, Y., & Nakajima, K. (2026). Scalable physical deep learning using optical dynamics with state-skipping training and single-map architecture. Nature Computational Science. https://doi.org/10.1038/s43588-026-01075-w
Source records
- crossref · retrieved 2026-11-08T12:43:30.488Z