DRLQ: A Novel Deep Reinforcement Learning (DRL)-based Technique for Task Placement in Quantum Cloud Computing Environments

Title: Enhancing Quantum Cloud Computing with DRLQ In the rapidly evolving landscape of quantum computing, traditional approaches struggle to efficiently manage tasks. This leads to inefficiencies in task scheduling and resource management. However, a practical solution in the form of DRLQ, a Deep Reinforcement Learning-based technique, offers a dynamic task placement strategy to optimize quantum task completion time and scheduling efficiency. DRLQ leverages advanced reinforcement learning techniques to continuously interact with the quantum computing environment, enhancing task completion efficiency and reducing the need for rescheduling. Experimental results have shown that DRLQ significantly improves task execution efficiency, reducing total quantum task completion time by 37.81% to 72.93% compared to other heuristic approaches and minimizing the need for task rescheduling. The impact and future prospects of DRLQ are promising, as it presents an innovative approach for efficient quantum cloud resource management, enabling adaptive learning and decision-making in quantum cloud computing environments. In addition to quantum computing, AI solutions can also drive business transformation. By identifying automation opportunities, defining KPIs, selecting suitable AI solutions, and implementing them gradually, businesses can leverage AI for transformation. For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com or stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom. Explore how AI can redefine your sales processes and customer engagement by visiting itinai.com. Useful Links: - AI Lab in Telegram @itinai – free consultation - Twitter – @itinaicom