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.
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.
Exploiting quantum computing at the mobile edge holds immense potential for facilitating large-scale network design, processing multimodal data, optimizing resource management, and enhancing network security. In this paper, we propose a pioneering paradigm of mobile edge quantum computing (MEQC) that integrates quantum computing capabilities into classical edge computing servers that are proximate to mobile devices. To conceptualize the MEQC, we first design an MEQC system, where mobile devices can offload classical and quantum computation tasks to edge servers equipped with classical and quantum computers. We then formulate the hybrid classical-quantum computation offloading problem whose goal is to minimize system cost in terms of latency and energy consumption. To solve the offloading problem efficiently, we propose a hybrid discrete-continuous multi-agent reinforcement learning algorithm to learn long-term sustainable offloading and partitioning strategies. Finally, numerical results demonstrate that the proposed algorithm can reduce the MEQC system cost by up to 30% compared to existing baselines.
Mobile edge computing (MEC) has revolutionized the way computational tasks are offloaded and latency is reduced by leveraging edge servers close to end devices. Efficient resource allocation and task offloading are crucial for enhancing system performance in MEC environments. Traditional reinforcement learning (RL) approaches have shown promise in optimizing resource allocation and task offloading problems. However, they often face challenges such as high computational complexity and the need for extensive training data. Quantum reinforcement learning (QRL) emerges as a promising solution to overcome these limitations by leveraging quantum computing principles to enhance efficiency and scalability. In this paper, we propose a hybrid quantum-classical non-sequential model for joint resource allocation and task offloading in MEC systems. Our model combines the advantages of RL in handling environmental dynamics and quantum computing in reducing adjustable parameters and accelerating the training process. Extensive experiments demonstrate that our proposed algorithm can achieve higher training and inference performance under various parameter settings compared to traditional RL models and previous QRL models.
As healthcare becomes increasingly data-driven, integrating hybrid mobile edge-quantum computing (MEQC) into smart healthcare systems emerges as a promising solution for handling growing computational demand, especially for latency-sensitive tasks. Therefore, this paper proposes a deep reinforcement learning (DRL)-based Lyapunov approach for schedule computation offloading, aiming to minimize the total latency in hybrid MEQC-based smart healthcare systems. In this framework, a sustainable computation offloading strategy is obtained while guaranteeing the individual latency constraints and the required success ratio for each computation task. More precisely, the original latency minimization problem is transformed into a stepwise mixed-integer non-convex optimization problem using Lyapunov techniques. Subsequently, a Deep Q-Network (DQN) is adopted for computation offloading mode selection. The effectiveness of the proposed approach and its dependency on various system parameters are validated and assessed through numerical simulations.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
In this paper, a novel paradigm of mobile edge-quantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings.
In this paper, a novel paradigm of mobile edgequantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings.