ABSTRACT
As the Internet of Things (IoT) progresses towards the concept of the metaverse, it becomes evident that a complex network of interconnected devices and services emerge, hence demanding innovative strategies for routing and ensuring security. This study presents a novel architecture that utilizes quantum deep learning techniques to construct a trust-based routing mechanism for IoT landscape within the metaverse. The architecture combines the variational quantum eigensolver (VQE) and quantum annealing (QA) to achieve this objective. The VQE algorithm, commonly utilized for the purpose of determining the lowest energy state of quantum systems, is being applied in this study to model trust levels. These trust levels are based on the historical and real-time interactions of devices. Simultaneously, the proficient professionals at quality assurance (QA) employ these confidence levels to dynamically construct ideal routing paths. The integration of quantum algorithms and conventional deep learning methods has dual benefits of safeguarding data privacy and enhancing routing efficiency. This integration enables the system to efficiently tackle the unique issues presented by the expansive digital environment known as the metaverse. Based on the first data, it is anticipated that there will be a significant decrease in malicious routing attempts, enhanced throughput, and increased network robustness. The findings presented in this study illustrate the potential of quantum deep learning to significantly transform future practices in metaverse IoT routing.
