An Intelligent Hybrid Quantum-Deep Reinforcement Framework for Energy-Efficient Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) experience increased energy consumption and inefficient routing, resulting in reduced network performance. Conventional methods, like Q-learning and Deep Q-Networks (DQN), have difficulties with scalability and adaptive learning in changing environments. We present a Hybrid Quantum-Deep Reinforcement Learning (QDRL) framework that combines quantum state encoding, experience replay, and deep reinforcement learning for optimum routing. The QD-RL system effectively identifies energy-optimized pathways, decreasing energy usage by 35% and extending network lifespan by 40%. In comparison to QLR, DQN, and EA-AODV, our model attains exceptional results: 98.6% accuracy, 97.8% precision, and 99.1% ROC-AUC. QDRL improves routing flexibility by utilising quantum superposition and deep reinforcement learning, making it suitable for extensive WSNs. Future investigations may examine quantum hardware acceleration to enhance real-time performance further. This study sets a novel benchmark for energy-efficient, intelligent WSN routing, facilitating the advancement of next-generation AI-driven networks.