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    Area of Science:

    • Optics and Photonics
    • Computer Science
    • Artificial Intelligence

    Background:

    • Computer-generated holography (CGH) is vital for holographic displays but faces challenges with computational complexity and data storage.
    • Current methods struggle to balance compression efficiency with the fidelity of reconstructed holograms.

    Purpose of the Study:

    • To develop an optimized scheme for CGH data compression.
    • To enhance the efficiency and quality of holographic data compression using advanced AI techniques.

    Main Methods:

    • Proposed a compression scheme integrating a quantum-inspired convolutional neural network (QICNN) with a genetic algorithm (GA).
    • Utilized quantum-inspired principles within the QICNN for efficient feature extraction and representation.
    • Employed GA for optimal weight initialization in the deep neural network to improve reconstruction quality.

    Main Results:

    • Achieved superior compression performance compared to conventional deep learning-based methods.
    • Demonstrated substantial reduction in storage requirements for holographic data.
    • Maintained high fidelity in reconstructed holograms without increased computational complexity.

    Conclusions:

    • The proposed QICNN-GA approach offers an effective pathway for efficient CGH data compression.
    • This research contributes to the advancement of real-time holographic display technologies by addressing data handling limitations.