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    Area of Science:

    • Quantum optics
    • Artificial intelligence hardware

    Background:

    • All-optical neural networks (AONNs) offer speed and energy benefits for AI.
    • Scalability of AONNs is hindered by high power demands of nonlinear optical elements.

    Purpose of the Study:

    • To introduce a low-power nonlinear activation scheme for scalable AONNs.
    • To enable multi-input, multi-output optical processing and all-optical training.

    Main Methods:

    • Utilized a three-level quantum system driven by dual laser fields.
    • Implemented a two-channel nonlinear activation matrix with tunable sigmoid and ReLU functions.
    • Validated through theoretical modeling and experimental demonstration in rubidium vapor cells.

    Main Results:

    • Achieved ultralow power consumption (17 μW per neuron).
    • Demonstrated feasibility of scaling to millions of neurons with under 20 W total power.
    • Successfully generated gradient-like signals for all-optical backpropagation.

    Conclusions:

    • The developed scheme significantly advances scalable, high-speed, and energy-efficient optical AI hardware.
    • Paves the way for practical all-optical training of deep neural networks.