Verma Vandana Rani, Nishad Dinesh Kumar, Sharma Vishnu, Singh Vinay Kumar, Verma Anshul, Shah Dharti Raj
Department of Computer Science and Engineering, Golgotias College of Engineering, Greater Noida, India.
Department of Electrical Engineering, Dr. Shakuntala Misra National Rehabilitation University, Lucknow, India.
Sci Rep. 2025 Jan 2;15(1):405. doi: 10.1038/s41598-024-84441-w.
Quantum computing and machine learning convergence enable powerful new approaches for optimizing mobile edge computing (MEC) networks. This paper uses Lyapunov optimization theory to propose a novel quantum machine learning framework for stabilizing computation offloading in next-generation MEC systems. Our approach leverages hybrid quantum-classical neural networks to learn optimal offloading policies that maximize network performance while ensuring the stability of data queues, even under dynamic and unpredictable network conditions. Rigorous mathematical analysis proves that our quantum machine learning controller achieves close-to-optimal performance while bounding queue backlogs. Extensive simulations demonstrate that the proposed framework significantly outperforms conventional offloading approaches, improving network throughput by up to 30% and reducing power consumption by over 20%. These results highlight the immense potential of quantum machine learning to revolutionize next-generation MEC networks and support emerging applications at the intelligent network edge.
量子计算与机器学习的融合为优化移动边缘计算（MEC）网络带来了强大的新方法。本文运用李雅普诺夫优化理论，提出了一种新颖的量子机器学习框架，用于稳定下一代MEC系统中的计算卸载。我们的方法利用混合量子 - 经典神经网络来学习最优卸载策略，即使在动态和不可预测的网络条件下，也能在确保数据队列稳定性的同时最大化网络性能。严格的数学分析证明，我们的量子机器学习控制器在限制队列积压的同时实现了接近最优的性能。大量仿真表明，所提出的框架显著优于传统卸载方法，网络吞吐量提高了30%，功耗降低了20%以上。这些结果凸显了量子机器学习在变革下一代MEC网络以及支持智能网络边缘新兴应用方面的巨大潜力。
