Traditional heuristic methods struggle to adapt to the rapidly evolving landscape of quantum computing while maintaining system efficiency3. They often place tasks on unsuitable quantum computers, requiring frequent rescheduling due to mismatched resources. These methods cannot exploit the full potential of dynamic quantum cloud computing environments, and thus, novel strategies are needed for optimizing task completion time and scheduling efficiency3.
DRLQ improves task placement in quantum environments by utilizing a Deep Reinforcement Learning (DRL) approach combined with the Rainbow DQN technique. This method enables adaptive learning and decision-making, optimizing task placement based on changing conditions and resource availability. DRLQ significantly reduces total quantum task completion time and minimizes the need for task rescheduling, addressing the limitations of traditional heuristic methods in quantum cloud resource management.
The Rainbow DQN approach 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.