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IEEE Transactions on Mobile Computing

A Quantum Reinforcement Learning Approach for Joint Resource Allocation and Task Offloading in Mobile Edge Computing

Xinliang Wei, Xitong Gao, Kejiang Ye, Chengzhong Xu, Yu Wang

Published Nov 13, 2024• 21 citations
Publisher

Abstract

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.

DOI
10.1109/tmc.2024.3496918

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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.
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.
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.
The integration of Quantum-Inspired Reinforcement Learning (QiRL) into Edge-Driven CyberPhysical Systems (CPS) presents a transformative approach to enhance real-time decision-making in distributed environments. While classical reinforcement learning (RL) struggles with scalability and computational complexity in edge-CPS, quantum-inspired Quantum-Inspired Reinforcement Learning for Edge-Driven Cyber-Physical Systems algorithms leverage superposition and entanglement principles to optimize policy exploration and convergence. This paper proposes a novel QiRL framework that combines quantum-inspired optimization with deep RL, enabling efficient resource allocation and adaptive control in latency-sensitive applications (e.g., smart grids, autonomous drones). We formalize the mathematical foundations of QiRL, design a hybrid neural-quantum architecture for edge deployment, and evaluate its performance under dynamic CPS constraints. Simulations on IoT-based case studies demonstrate 20−35%\mathbf{2 0}-\mathbf{3 5 \%}20−35% faster convergence and 15%\mathbf{1 5 \%}15% higher energy efficiency compared to classical Deep Q-Networks (DQN). Key challenges, such as quantum noise simulation and edge-hardware limitations, are discussed to guide future research. This work bridges quantum computing principles with edge intelligence, offering a scalable pathway for nextgeneration CPS.
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.
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.
Exploring the convergence of quantum computing and machine learning, this paper delves into Quantum Reinforcement Learning (QRL) with a specific focus on Variational Quantum Circuits (VQC). Alongside a comprehensive examination of the field, the study presents a concrete implementation of QRL tailored to foundational gym environments. Leveraging principles from Quantum Computing, experiments reveal QRL’s potential in enhancing reinforcement learning tasks within these environments, showcasing notable space optimization for RL models. Quantitative results indicate a reduction in the number of trainable parameters by up to 90% when compared to classical approaches, significantly improving efficiency, mainly due to quantum principles of superposition and entanglement. This highlights the promising implications of integrating quantum computing techniques to address challenges and advance capabilities in fundamental reinforcement learning scenarios. Furthermore, the study discusses the adaptability of QRL algorithms to diverse problem domains, suggesting their potential for scalability and applicability beyond simple environments. Such versatility underscores the robustness and practical relevance of QRL methodologies in real-world scenarios, positioning them as valuable tools for tackling complex reinforcement learning challenges.