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

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.