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Optical computing

A nonlinear dimension for machine learning in optical disordered media

A recent study shows that, by leveraging nonlinear optical processes in disordered media, photonic processors can transform high-dimensional machine-learning data, using nonlinear functions that are otherwise challenging for digital electronic processors to compute.

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Fig. 1: Feature extraction from optical data using nonlinear optical speckles.

References

  1. McMahon, P. L. Nat. Rev. Phys. 5, 717–734 (2023).

    Article  Google Scholar 

  2. Gigan, S. Nat. Phys. 18, 980–985 (2022).

    Article  Google Scholar 

  3. Wang, H. et al. Nat. Comp. Sci. https://doi.org/10.1038/s43588-024-00644-1 (2024).

    Article  Google Scholar 

  4. Zheng, H. et al. Nat. Nanotechnol. 19, 471–478 (2024).

    Article  Google Scholar 

  5. Wei, K. et al. Preprint at https://doi.org/10.48550/arXiv.2308.03407 (2023).

  6. Wang, T. et al. Nat. Photon. 17, 408–415 (2023).

    Article  Google Scholar 

  7. Ashtiani, F., Geers, A. J. & Aflatouni, F. Nature 606, 501–506 (2022).

    Article  Google Scholar 

  8. Huang, C. et al. Nat. Electron. 4, 837–844 (2021).

    Article  Google Scholar 

  9. Lin, X. et al. Science 361, 1004–1008 (2018).

    Article  MathSciNet  Google Scholar 

  10. Wang, X. et al. Preprint at https://doi.org/10.48550/arXiv.2306.04554 (2023).

  11. Moon, J., Cho, Y. C., Kang, S., Jang, M. & Choi, W. Nat. Phys. 19, 1709–1718 (2023).

    Article  Google Scholar 

  12. Wright, L. G. et al. Nature 601, 549–555 (2022).

    Article  Google Scholar 

  13. Bandyopadhyay, S. et al. Preprint at https://doi.org/10.48550/arXiv.2208.01623 (2022).

  14. Momeni, A., Rahmani, B., Malléjac, M., Del Hougne, P. & Fleury, R. Science 382, 1297–1303 (2023).

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The author thanks J. Hu and P. L. McMahon for their detailed feedback to a draft of the manuscript.

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Correspondence to Tianyu Wang.

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Wang, T. A nonlinear dimension for machine learning in optical disordered media. Nat Comput Sci 4, 394–395 (2024). https://doi.org/10.1038/s43588-024-00648-x

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