The integration of the Internet of Things (IoT) with quantum communication networks opens the door to transformative applications, including massive machine communication, virtual reality (VR), the Metaverse, and a wide range of artificial intelligence (AI)-driven technologies. The Quantum IoT (QIoT) meets these demands by enabling ultrasecure communication, advanced data processing, and enhanced sensing capabilities. By leveraging quantum principles, such as entanglement and teleportation, QIoT achieves superior efficiency and security compared to traditional IoT systems. As a pioneering quantum computing and communication convergence, QIoT is expected to experience significant growth over the next decade, driven by rapid advancements in quantum technologies. However, QIoT networks face critical challenges, such as energy efficiency (EE) issues, memory limitations caused by qubit decoherence, and substantial losses during quantum communication, including the exponential decay of entanglement success over quantum optical channels. To address these challenges, we propose an optimal decoherence-aware entanglement generation framework to maximize EE in QIoT networks. Using Dinkelbach’s method and Lagrangian analysis, the framework reformulates the resource allocation (RA) problem as a nonlinear fractional programming model, which is solved iteratively through linear programming techniques. This approach ensures energy-efficient entanglement generation rates (EGRs) while maintaining reliable quantum communication. Simulation results validate the effectiveness of the proposed framework, highlighting its advantages and providing valuable insights for the development of QIoT systems.
