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    This study demonstrates an optical reservoir computer using 24 vertical-cavity surface-emitting lasers (VCSELs). The system successfully performed complex tasks like 2-bit XOR and 3-bit header recognition with high accuracy.

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    Area of Science:

    • Photonics and Optical Computing
    • Machine Learning Hardware

    Background:

    • Reservoir computing (RC) offers a novel approach to machine learning by utilizing the complex dynamics of physical systems.
    • Optical implementations of RC promise high-speed computation due to the inherent speed of light.

    Purpose of the Study:

    • To experimentally investigate the performance of a novel optical reservoir computer.
    • To evaluate the computational capabilities of a network of diffractively coupled vertical-cavity surface-emitting lasers (VCSELs).

    Main Methods:

    • Constructed an optical reservoir computer with 24 physical VCSEL nodes.
    • Employed diffractive coupling via an external cavity to interconnect the VCSELs.
    • Assessed system memory and task performance, including 2-bit XOR, 3-bit header recognition (HR), and 2-bit digital-to-analog conversion (DAC).

    Main Results:

    • Achieved bit error ratios (BERs) below 1% for the 2-bit XOR and 3-bit HR tasks.
    • Obtained a root mean square error (RMSE) of 0.067 for the 2-bit DAC task.
    • Demonstrated the system's capability to perform complex computational tasks with high fidelity.

    Conclusions:

    • The experimental optical reservoir computer based on coupled VCSELs is a viable platform for high-performance computation.
    • This approach shows potential for efficient processing of complex data streams in optical machine learning systems.