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Related Experiment Video

Updated: Jan 17, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

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Optimizing optical chaotic sequences using GAN and the Fisher-Yates algorithm.

Daming Wang, Haoran Bian, Yihang Lei

    Optics Express
    |September 23, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel optical chaotic sequence optimization using deep learning and post-processing. The method enhances chaotic sequences for improved randomness and security applications.

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    Area of Science:

    • Optics and Photonics
    • Artificial Intelligence
    • Information Security

    Background:

    • Traditional chaotic sequences face challenges in randomness and time-delay signatures.
    • Deep learning offers potential for optimizing complex signal generation.

    Purpose of the Study:

    • To propose and demonstrate an optical chaotic sequence optimization scheme.
    • To enhance the randomness and security of chaotic sequences using AI.

    Main Methods:

    • Integration of Generative Adversarial Network (GAN) into an optical feedback chaotic system.
    • Application of the Fisher-Yates algorithm for post-processing randomness enhancement.
    • Quantization of the optimized chaotic sequence into a random bit sequence.

    Main Results:

    • Significant improvement in distribution characteristics and complexity of chaotic sequences.
    • Effective suppression of the time-delay signature in optical chaotic sequences.
    • Successful validation of the optimized sequence using the NIST statistical test suite.

    Conclusions:

    • The proposed scheme effectively optimizes optical chaotic sequences for enhanced randomness and security.
    • The integration of artificial neural networks presents a novel approach for random bit sequence generation.
    • This method offers a promising direction for future research in secure communication and random number generation.