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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.


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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).

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