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IEEE Vehicular Technology Conference

Smart Healthcare with Hybrid Mobile Edge-Quantum Computing: Dynamic Computation Offloading for Latency Improvement

Ziqiaing Ye, Yulan Gao, Yue Xiao, Minrui Xu, Han Yu, Dusit Tao Niyato

Published Oct 10, 2023• 5 citations
Publisher

Abstract

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.

DOI
10.1109/vtc2023-fall60731.2023.10333492

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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.
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.
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.
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.
This paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. Under stochastic behaviours and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantumempowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover’s algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed QeDRL algorithm and its superior computational learning speed.