DRLQ 是一种基于深度强化学习 (DRL) 的新技术，用于在量子云计算环境中进行任务分配。它利用深度 Q 网络 (DQN) 架构，并通过 Rainbow DQN 方法增强，以创建动态任务分配策略。DRLQ 通过与量子计算环境的持续交互学习最佳任务分配策略，从而提高任务完成效率，减少重新调度需求。

🎯 DRLQ 采用深度 Q 网络 (DQN) 和 Rainbow DQN 方法，整合了多种先进的强化学习技术，包括 Double DQN、优先经验回放、多步学习、分布式强化学习和 Noisy Nets，以提高强化学习模型的训练效率和有效性。

🤖 DRLQ 的系统模型包括一组可用的量子计算节点 (QNodes) 和一组传入的量子任务 (QTasks)，每个任务都有特定的属性，例如量子比特数、电路深度和到达时间。任务分配问题被表述为为每个传入的 QTask 选择最合适的 QNode，以最大程度地减少总响应时间并减少替换频率。

📈 实验表明，DRLQ 显著提高了任务执行效率。与其他启发式方法相比，该方法将总量子任务完成时间减少了 37.81% 到 72.93%。此外，DRLQ 有效地减少了任务重新调度的需求，在评估中实现了零重新调度尝试，而现有方法则出现了大量的重新调度尝试。

💡 DRLQ 是一种基于深度强化学习的创新方法，用于优化量子云计算环境中的任务分配。它利用 Rainbow DQN 技术，克服了传统启发式方法的局限性，为高效的量子云资源管理提供了一种动态自适应的解决方案。

The ever-evolving nature of quantum computing renders managing tasks with the traditional heuristic approach very tricky. These models often struggle with adapting to the changes and complexities of quantum computing while maintaining the system efficiency. Scheduling tasks is crucial for such systems to reduce time wastage and resource management. Existing models are liable to place tasks on unsuitable quantum computers, requiring frequent rescheduling due to mismatched resources. The quantum computation resources require novel strategies to optimize task completion time and scheduling efficiency.

Currently, quantum task placement relies on heuristic approaches or manually crafted policies. While practical in certain contexts, these methods cannot exploit the full potential of dynamic quantum cloud computing environments. As quantum cloud computing integrates classical cloud resources to host applications that interact with quantum computers remotely, efficient resource management becomes increasingly critical.

Researchers from the University of Melbourne and Data61, CSIRO have proposed DRLQ, a novel technique based on Deep Reinforcement Learning (DRL) for task placement in quantum cloud computing environments. DRLQ leverages the Deep Q Network (DQN) architecture, enhanced with the Rainbow DQN approach, to create a dynamic task placement strategy. DRLQ aims to address the limitations of traditional heuristic methods by learning optimal task placement policies through continuous interaction with the quantum computing environment, thus enhancing task completion efficiency and reducing the need for rescheduling.

The DRLQ framework employs Deep Q Networks (DQN) combined with the Rainbow DQN approach, which integrates several advanced reinforcement learning techniques, including Double DQN, Prioritized Replay, Multi-step Learning, Distributional RL, and Noisy Nets. These enhancements collectively improve the training efficiency and effectiveness of the reinforcement learning model. 

The system model includes a set of available quantum computation nodes (QNodes) and a set of incoming quantum tasks (QTasks), each with specific properties such as qubit number, circuit depth, and arrival time. The task placement problem is formulated as selecting the most appropriate QNode for each incoming QTask to minimize the total response time and mitigate replacement frequency. The state space of the reinforcement learning model consists of features of QNodes and QTasks, while the action space is defined as the selection of a QNode for a QTask. The reward function is designed to minimize the total completion time and penalize task rescheduling attempts, encouraging the policy to find optimal placements that reduce completion time and avoid rescheduling.

Experiments conducted on QSimPy simulation toolkit demonstrate that DRLQ significantly improves task execution efficiency. The proposed method reduces total quantum task completion time by 37.81% to 72.93% compared to other heuristic approaches. Moreover, DRLQ effectively minimizes the need for task rescheduling, achieving zero rescheduling attempts in evaluations, compared to substantial rescheduling attempts by existing methods.

In conclusion, the paper presents DRLQ, an innovative Deep Reinforcement Learning-based approach for optimizing task placement in quantum cloud computing environments. By leveraging the Rainbow DQN technique, DRLQ addresses the limitations of traditional heuristic methods, providing a dynamic and adaptive solution for efficient quantum cloud resource management. This approach is one of the first in quantum cloud resource management, enabling adaptive learning and decision-making.

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