DRLQ: A Novel Deep Reinforcement Learning (DRL)-based Technique for Task Placement in Quantum Cloud Computing Environments
What challenges do traditional heuristic methods face in quantum computing?
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.
How does DRLQ improve task placement in quantum environments?
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.
What technologies does the Rainbow DQN approach integrate?
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.