Quantum Computing-Driven Digital Twin Architecture for Optimized Load Distribution in IoT Networks

The growing demand for reliable service delivery and real-time data processing has intensified challenges in efficiently distributing workloads across geographically distributed server nodes in Internet of Things (IoT)-enabled Digital Twin (DT) environments. To address this issue, this study proposes a quantum computing–inspired (QCi) optimization–based framework to enhance load balancing in IoT–DT systems. The approach integrates a novel QCi optimization strategy with a QCi-based predictive neural network to estimate optimal server node allocation by considering real-time server availability and workload dynamics. Extensive evaluations show that the proposed method outperforms Bayesian Belief Networks, conventional Artificial Neural Networks, and Support Vector Machines, achieving a sensitivity of 87.36%, specificity of 94.38%, precision of 91.25%, and coverage of 97.51%. The results demonstrate that combining QCi optimization with DT technology significantly improves processing efficiency, prediction accuracy, and adaptability for real-time IoT workloads.