Variational Quantum Eigensolver‐Driven Cut‐Vertex Analysis for Secure IoT Wireless Sensor Network Monitoring

ABSTRACT In recent times, secure and energy‐efficient link monitoring in wireless sensor networks plays a significant role in facilitating reliable communication between Internet of Things applications. The classical articulation‐point detection methods efficiently identified the structural vulnerabilities but do not incorporate a node level attributes. This research proposes a novel variational quantum eigensolver–based decision tree for secure and energy‐efficient link monitoring in the Internet of Things towards wireless sensor networks by reformulating the structural monitoring as a weighted connectivity optimization problem. Based on the quantum computation, the variational quantum eigensolver is designed as a quantum‐assisted optimizer within localized subgraphs to evaluate weighted connectivity degradation. The decision tree is applied to predict real‐time intrusion by using the cut vertices and provides efficient and interpretable identification of malicious activity. In addition, the particle swarm optimization with dynamic opposition strategy is designed for selecting the monitoring nodes under connectivity constraints, which ensures full coverage as well as connectivity. The experimental validation signifies that the variational quantum eigensolver–based decision tree scheme achieves a superior accuracy of 98.3% and lower energy consumption of 73.6 J at the 250th node. As demonstrated by comparative and statistical analysis, the efficiency, scalability, and security of the proposed framework are superior to the conventional methods, supporting real‐time, resource‐limited Internet of Things–wireless sensor network settings.