Quantum Computing-Driven Mobile E-Learning Systems for Ultra-Secure and High-Speed Content Delivery in Dynamic Educational Environments

Mobile e-learning platforms' tremendous growth has changed modern education systems by providing instant and flexible access to e-learning content. However, increased reliance on wireless communications and cloud-based infrastructure in mobile e-learning platforms is creating many issues in terms of data security, latency, scalability, and content delivery. Traditional techniques in security and communication are not always effective in dealing with the changing requirements of mobile e-learning systems, where huge amounts of data are constantly being transferred between various devices. This paper introduces the network architecture of a Quantum Computing-Driven Mobile E-Learning System (QCMELS) designed to utilize quantum cryptographic protocols for ultra-secure, high-speed content delivery. To validate its architectural feasibility prior to the widespread availability of physical quantum hardware, the framework's quantum mechanics are modeled via quantum circuit simulation and quantum-inspired optimization algorithms. A multi-layer system architecture design has been made to accommodate secure authentication and dynamic quantum session key generation, adaptive bandwidth, and low latency for Wi-Fi, 5G, and Internet-of-Things-based mobile e-learning platforms. Moreover, the model employs intelligent routing as well as edge caching mechanisms to enhance efficient communication in terms of mitigating congestion. An experimental analysis showed that the QCMELS framework performed better than any other mobile learning framework across several performance measures. The QCMELS model delivered 97.8 Mbps throughput, 98.6% PDR, and 98.1% security accuracy and brought about a reduction in average latency to 61 ms. On top of this, there was efficiency in resource usage, which stood at 92.4%. This shows that the optimization framework proposed is indeed effective. The findings obtained from the ablation study provided additional evidence on how the quantum security and edge computing modules contributed towards improving performance. The findings suggest that the proposed framework is scalable, reliable, and secure. It is an ideal framework for future mobile e-learning systems. The research helps to advance intelligent educational systems.

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