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<p>We present a theoretical framework for the Quantum-Enhanced Swarm Intelligence Network (QESIN), integrating NV center quantum magnetometry with swarm robotics, hyperspectral imaging, thermal imaging, GPR, and hierarchical Bayesian fusion for landmine detection. This simulation-based study addresses high false alarm rates (up to 50% in cluttered environments) and poor performance against non-metallic explosives (detection rates 70%). QESIN achieves sub-picotesla sensitivity and adaptive coverage in simulations. Monte Carlo simulations (10<sup>7</sup>data points) with realistic noise models yield 98.2% detection efficiency (95% CI [97.5, 98.9]), &gt;80% false positive reduction, and ROC-AUC 0.992. Extensions include NV relaxometry for trace explosives. Validations (<i>p</i> &lt; 10<sup>–10</sup>) and Python code support the framework, emphasizing the need for empirical field testing to address engineering challenges.</p>

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A simulation-based quantum-enhanced swarm intelligence framework for landmine detection using NV centers in diamond

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月28日

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  • Sami Rashid Mohammed Shibah

摘要

We present a theoretical framework for the Quantum-Enhanced Swarm Intelligence Network (QESIN), integrating NV center quantum magnetometry with swarm robotics, hyperspectral imaging, thermal imaging, GPR, and hierarchical Bayesian fusion for landmine detection. This simulation-based study addresses high false alarm rates (up to 50% in cluttered environments) and poor performance against non-metallic explosives (detection rates 70%). QESIN achieves sub-picotesla sensitivity and adaptive coverage in simulations. Monte Carlo simulations (107data points) with realistic noise models yield 98.2% detection efficiency (95% CI [97.5, 98.9]), >80% false positive reduction, and ROC-AUC 0.992. Extensions include NV relaxometry for trace explosives. Validations (p < 10–10) and Python code support the framework, emphasizing the need for empirical field testing to address engineering challenges.