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Deep diffractive optical neural networks for detecting skyrmionic topologies of light and their live training

Bezuidenhout, Hadrian, Peters, Cade, Kumar, Ram Nandan, Forbes, Andrew, Nape, Isaac·2026·Nature Communications
DOI 10.1038/s41467-026-77376-5PMC PMC13646250
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  • Detection of skyrmion states requires either full Stokes polarimetry with spatial resolution or single-shot projective metasurfaces followed by computational classification, or the development of dedicated optical elements that can efficiently separate skyrmion states.

    “Detection therefore requires either full Stokes polarimetry with spatial resolution or with single shot-projective metasurfaces (79) followed by computational classification or the development of dedicated optical elements that can efficiently separate skyrmion states”

    From Topological robustness of optical skyrmions through a real-world free-space link (2026) · cites this paper as [80]

  • A skyrmion mode sorter is yet to be fully realized.

    “a skyrmion mode sorter that is yet to be fully realized (80).”

    From Topological robustness of optical skyrmions through a real-world free-space link (2026) · cites this paper as [80]

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Research works printed but not linked: 63

  1. Forbes, A., De Oliveira, M. & Dennis, M. R. Structured light. Nat. Photonics 15, 253–262 (2021). · DOI
  2. Sugic, D. et al. Particle-like topologies in light. Nat. Commun. 12, 6785 (2021). · DOI · Paper page
  3. Deng, Z.-L., Shi, T., Krasnok, A., Li, X. & Alù, A. Observation of localized magnetic plasmon skyrmions. Nat. Commun. 13, 8 (2022). · DOI · Paper page
  4. Teng, H. et al. Construction of optical spatiotemporal skyrmions. Light.: Sci. Appl. 14, 324 (2025). · DOI
  5. Guo, C., Xiao, M., Guo, Y., Yuan, L. & Fan, S. Meron spin textures in momentum space. Phys. Rev. Lett. 124, 106103 (2020). · DOI
  6. Gao, S. et al. Paraxial skyrmionic beams. Phys. Rev. A 102, 053513 (2020). · DOI · Paper page
  7. Ma, J. et al. Nanophotonic quantum skyrmions enabled by semiconductor cavity quantum electrodynamics. Nat. Phys. 21, 1462–1468 (2025). · DOI
  8. Ornelas, P., Nape, I., de Mello Koch, R. & Forbes, A. Non-local skyrmions as topologically resilient quantum entangled states of light. Nat. Photonics 18, 258–266 (2024). · DOI · Paper page
  9. de Mello Koch, R. et al. Revealing the topological nature of entangled orbital angular momentum states of light. Nat. Commun. 16, 11095 (2025). · DOI
  10. Wang, A. A. et al. Topological protection of optical skyrmions through complex media. Light.: Sci. Appl. 13, 314 (2024). · DOI · Paper page
  11. Guo, Z. et al. Topological robustness of classical and quantum optical skyrmions in atmospheric turbulence. Nat. Commun. 17, 2085 (2026). · DOI · Paper page
  12. de Mello Koch, R., Lu, B. Q., Ornelas, P., Nape, I. & Forbes, A. Quantum skyrmions in general quantum channels. APL Quantum 2, 026126 (2025). · DOI · Paper page
  13. Peters, C., Everts, K., Kleine, T., Ornelas, P. and Forbes, A."Seeing through randomness with topological light,” arXiv https://doi.org/10.48550/arXiv.2508.12305 (2025). · DOI · Paper page
  14. Wang, A. A. et al. Perturbation-resilient integer arithmetic using optical skyrmions. Nat. Photonics 19, 1367–1375 (2025). · DOI
  15. Dai, Y. et al. Plasmonic topological quasiparticle on the nanometre and femtosecond scales. Nature 588, 616–619 (2020). · DOI
  16. Du, L., Yang, A., Zayats, A. V. & Yuan, X. Deep-subwavelength features of photonic skyrmions in a confined electromagnetic field with orbital angular momentum. Nat. Phys. 15, 650–654 (2019). · DOI · Paper page
  17. Hakobyan, V., Shen, Y. & Brasselet, E. Unitary spin-orbit optical-skyrmionic wave plates. Phys. Rev. Appl. 22, 054038 (2024). · DOI
  18. Hakobyan, V. & Brasselet, E. Q-plates: from optical vortices to optical skyrmions. Phys. Rev. Lett. 134, 083802 (2025). · DOI
  19. Koni, M. et al. Dual-wavelength quantum skyrmions from liquid crystal topological defects. Phys. Rev. Lett. 135, 223804 (2025). · DOI
  20. Lin, W., Ota, Y., Arakawa, Y. & Iwamoto, S. Microcavity-based generation of full poincaré beams with arbitrary skyrmion numbers. Phys. Rev. Res. 3, 023055 (2021). · DOI · Paper page
  21. Gutiérrez-Cuevas, R. & Pisanty, E. Optical polarization skyrmionic fields in free space. J. Opt. 23, 024004 (2021). · DOI
  22. Singh, K., Ornelas, P., Dudley, A. & Forbes, A. Synthetic spin dynamics with Bessel-Gaussian optical skyrmions. Opt. Express 31, 15289–15300 (2023). · DOI
  23. Teng, H., Zhong, J., Chen, J., Lei, X. & Zhan, Q. Physical conversion and superposition of optical skyrmion topologies. Photonics Res. 11, 2042–2053 (2023). · DOI
  24. Li, T. et al. Realization of spinful metaphotonic Stokes skyrmions. J. Opt. 26, 09LT01 (2024). · DOI
  25. He, T. et al. Optical skyrmions from metafibers with subwavelength features. Nat. Commun. 15, 10141 (2024). · DOI
  26. Singh, K., Tabebordbar, N., Forbes, A. & Dudley, A. Digital Stokes polarimetry and its application to structured light: tutorial. J. Opt. Soc. Am. A 37, C33–C44 (2020). · DOI
  27. Toninelli, E. et al. Concepts in quantum state tomography and classical implementation with intense light: a tutorial. Adv. Opt. Photonics 11, 67–134 (2019). · DOI
  28. Roux, F. S. & Zhang, Y. Projective measurements in quantum and classical optical systems. Phys. Rev. A 90, 033835 (2014). · DOI
  29. Ndagano, B. et al. A deterministic detector for vector vortex states. Sci. Rep. 7, 13882 (2017). · DOI
  30. Mirhosseini, M., Malik, M., Shi, Z. & Boyd, R. W. Efficient separation of the orbital angular momentum eigenstates of light. Nat. Commun. 4, 2781 (2013). · DOI
  31. Defienne, H. & Faccio, D. Arbitrary spatial mode sorting in a multimode fiber. Phys. Rev. A 101, 063830 (2020). · DOI
  32. Ma, Y. et al. Using optical skyrmions to assess vectorial adaptive optics capabilities in the presence of complex aberrations. Sci. Adv. 11, eadv7904 (2025). · DOI
  33. Sui, X., Wu, Q., Liu, J., Chen, Q. & Gu, G. A review of optical neural networks. IEEE Access 8, 70773–70783 (2020). · DOI
  34. Zhang, Z. et al. Structured light meets machine intelligence. eLight 5, 1–35 (2025). · DOI
  35. Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39–47 (2020). · DOI
  36. Fu, T. et al. Optical neural networks: progress and challenges. Light.: Sci. Appl. 13, 263 (2024). · DOI
  37. Li, S. et al. All-optical image identification with programmable matrix transformation. Opt. Express 29, 26474–26485 (2021). · DOI
  38. Huang, Z. et al. All-optical signal processing of vortex beams with diffractive deep neural networks. Phys. Rev. Appl. 15, 014037 (2021). · DOI
  39. Huang, Z. et al. Orbital angular momentum deep multiplexing holography via an optical diffractive neural network. Opt. Express 30, 5569–5584 (2022). · DOI
  40. Zhang, J., Ye, Z., Yin, J., Lang, L. & Jiao, S. Polarized deep diffractive neural network for sorting, generation, multiplexing, and de-multiplexing of orbital angular momentum modes. Opt. Express 30, 26728–26741 (2022). · DOI
  41. Mei, F. et al. Cascaded metasurfaces for high-purity vortex generation. Nat. Commun. 14, 6410 (2023). · DOI
  42. Fontaine, N. K. et al. Laguerre-Gaussian mode sorter. Nat. Commun. 10, 1865 (2019). · DOI
  43. Zhou, J., Pu, H. & Yan, J. Spatiotemporal diffractive deep neural networks. Opt. Express 32, 1864–1877 (2024). · DOI
  44. Li, H. et al. Redefinable neural network for structured light array. Adv. Photonics Nexus 3, 056015–056015 (2024). · DOI
  45. Goel, S. et al. Simultaneously sorting overlapping quantum states of light. Phys. Rev. Lett. 130, 143602 (2023). · DOI
  46. Mengu, D. & Ozcan, A. All-optical phase recovery: diffractive computing for quantitative phase imaging. Adv. Opt. Mater. 10, 2200281 (2022). · DOI
  47. Wang, Q., Liu, J., Lyu, D. & Wang, J. Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks. Light.: Sci. Appl. 13, 10 (2024). · DOI
  48. Wang, Q., Lyu, D., Liu, J. & Wang, J. Polarization and orbital angular momentum encoded quantum Toffoli gate enabled by diffractive neural networks. Phys. Rev. Lett. 133, 140601 (2024). · DOI
  49. Qian, C. et al. Performing optical logic operations by a diffractive neural network. Light.: Sci. Appl. 9, 59 (2020). · DOI · Paper page
  50. Zarei, S. & Khavasi, A. Realization of optical logic gates using on-chip diffractive optical neural networks. Sci. Rep. 12, 15747 (2022). · DOI
  51. Spall, J., Guo, X., Barrett, T. D. & Lvovsky, A. Fully reconfigurable coherent optical vector–matrix multiplication. Opt. Lett. 45, 5752–5755 (2020). · DOI
  52. Koni, M., Bezuidenhout, H. & Nape, I. Emulating quantum computing with optical matrix multiplication. APL Photonics 9, 106120 (2024). · DOI
  53. Jia, Q. et al. Compensating the distorted OAM beams with a near-zero time delay. Appl. Phys. Lett. 121, 011104 (2022). · DOI
  54. Rocha, J. C. A., Būtaitė, U. G., Carpenter, J. & Phillips, D. B. Self-configuring high-speed multi-plane light conversion. Nat. Commun. 17, 73 (2025). · DOI
  55. Peters, C., Cocotos, V. & Forbes, A. Structured light in atmospheric turbulence-a guide to its digital implementation: tutorial. Adv. Opt. Photonics 17, 113–184 (2025). · DOI
  56. Ferrie, C. Self-guided quantum tomography. Phys. Rev. Lett. 113, 190404 (2014). · DOI
  57. Li, S., Yang, B. & Zou, Y. Adaptive curriculum learning for video captioning. IEEE Access 10, 31751–31759 (2022). · DOI
  58. Ng, D., Chen, Y., Tian, B., Fu, Q. and Chng, E. S. “Convmixer: Feature interactive convolution with curriculum learning for small footprint and noisy far-field keyword spotting,” in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), p. 3603–3607 (IEEE, 05 2022). · DOI
  59. Park, B. et al. A curriculum learning strategy to enhance the accuracy of classification of various lesions in chest-pa X-ray screening for pulmonary abnormalities. Sci. Rep. 9, 15352 (2019). · DOI
  60. Liu, F. et al. A review of the evaluation system for curriculum learning. Electronics 12, 1676 (2023). · DOI
  61. Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018). · DOI · Paper page
  62. Peters, C. et al. Extracting topological features of skyrmionic light: a tutorial. Adv. Photonics 8, 023001–023001 (2026). · DOI · Paper page
  63. Momeni, A. et al. Training of physical neural networks. Nature 645, 53–61 (2025). · DOI

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Metadata

Authors
Bezuidenhout, H., Peters, C., Kumar, R. N., Forbes, A., & Nape, I.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2026-10-07
Volume
17
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-026-77376-5
PMC
PMC13646250
Cited by
1
References
4
Language
en
Licence
Funders
South African Quantum Technology Initiative (SA QuTI), the NRF/CSIR Rental Pool Programme and the Oppenheimer Memorial Trust for financial support.

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