Leveraging Quantum Computing for Enhanced Load Balancing in Real-time IoT Systems through Digital Twin Integration

The rapid proliferation of Internet of Things (IoT) technologies has significantly transformed various industrial sectors, creating an urgent demand for effective real-time data analysis and service delivery. This paper addresses the critical challenge of optimizing load allocation across geographically dispersed server nodes within Digital Twin (DT) environments, which simulate physical assets in a virtual space. A detailed description of the system model is provided, consisting of a network of interconnected server nodes and a protocol for efficient data exchange and load management. By employing Quantum Computing-inspired Optimization (QCi) techniques, recognized for their efficacy in complex optimization scenarios, this study aims to enhance resource allocation specifically tailored for IoT applications. A novel QCi optimization technique is introduced, designed to improve load balancing across multiple servers within the IoT-DT framework. Furthermore, a predictive QCi Neural Network model is proposed to accurately forecast optimal server node distribution based on workload requirements and server availability in the IoT environment. Comprehensive benchmarking against state-of-the-art solutions demonstrates that the proposed methodology significantly enhances both temporal efficiency and resource utilization in real-time data processing environments. This research highlights the potential of synergizing QCi techniques with DT technology to effectively address the escalating demands of IoT-driven applications, ultimately enabling more responsive and efficient service delivery.