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<p>The integration of soft computing with quantum simulation presents a promising paradigm for automating the design of secure image scrambling circuits. This paper introduces a novel framework that combines Genetic Algorithms (GA) and Mamdani Fuzzy Inference Systems (FIS) to automatically generate and optimize quantum bit-plane scrambling circuits for NEQR and GNEQR image representations. The GA explores a diverse space of circuits composed of NOT, CNOT, Toffoli, and SWAP gates, while the FIS approximates the scrambling entropy without full quantum simulation, enabling rapid multi-objective optimization (minimizing circuit cost and ensuring entropy &gt; 3.5). Selected circuits are validated using the Qiskit Aer quantum simulator, and a weighted scoring system adapts circuit selection to different image types (medical, military, consumer). Extensive experiments on six image modalities (Lena, Monkey, MRI, CT, X-ray, retinal) demonstrate that the optimized circuits achieve near-ideal entropy (up to 7.89 bits), NPCR &gt; 99%, UACI close to 33.46%, and near-zero correlation, significantly outperforming static designs. Scalability tests on 500 × 500 images and robustness evaluations under depolarizing noise (10⁻<sup>3</sup>) confirm graceful degradation and practical feasibility on near-term quantum hardware. A comparative analysis between classical emulation and true quantum simulation highlights the importance of probabilistic measurement effects. The framework’s ability to generate on-demand, threshold-compliant circuits eliminates the need for static comparative benchmarking, offering a general-purpose, adaptive solution for quantum image security.</p>

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Soft-quantum computing integration: automated design and optimization of scrambling circuits for NEQR and GNEQR image using GAs and fuzzy logic

  • 期刊论文

  • 发表日期：

    2026年7月10日

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  • Tayseer S. Atia,
  • Zahraa A. Ali,
  • Ahmed Y. Yousuf

摘要

The integration of soft computing with quantum simulation presents a promising paradigm for automating the design of secure image scrambling circuits. This paper introduces a novel framework that combines Genetic Algorithms (GA) and Mamdani Fuzzy Inference Systems (FIS) to automatically generate and optimize quantum bit-plane scrambling circuits for NEQR and GNEQR image representations. The GA explores a diverse space of circuits composed of NOT, CNOT, Toffoli, and SWAP gates, while the FIS approximates the scrambling entropy without full quantum simulation, enabling rapid multi-objective optimization (minimizing circuit cost and ensuring entropy > 3.5). Selected circuits are validated using the Qiskit Aer quantum simulator, and a weighted scoring system adapts circuit selection to different image types (medical, military, consumer). Extensive experiments on six image modalities (Lena, Monkey, MRI, CT, X-ray, retinal) demonstrate that the optimized circuits achieve near-ideal entropy (up to 7.89 bits), NPCR > 99%, UACI close to 33.46%, and near-zero correlation, significantly outperforming static designs. Scalability tests on 500 × 500 images and robustness evaluations under depolarizing noise (10⁻3) confirm graceful degradation and practical feasibility on near-term quantum hardware. A comparative analysis between classical emulation and true quantum simulation highlights the importance of probabilistic measurement effects. The framework’s ability to generate on-demand, threshold-compliant circuits eliminates the need for static comparative benchmarking, offering a general-purpose, adaptive solution for quantum image security.