DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum Cloud Computing

TL;DR

This paper introduces DRLQ, a deep reinforcement learning method for task placement in quantum cloud computing, significantly improving efficiency and reducing rescheduling through adaptive learning.

Contribution

It presents the first DRL-based task placement strategy for quantum cloud environments, enhancing adaptability and optimization over traditional heuristics.

Findings

Reduces quantum task completion time by up to 72.93%.

Prevents task rescheduling in quantum cloud environments.

Demonstrates superior performance over heuristic methods.

Abstract

The quantum cloud computing paradigm presents unique challenges in task placement due to the dynamic and heterogeneous nature of quantum computation resources. Traditional heuristic approaches fall short in adapting to the rapidly evolving landscape of quantum computing. This paper proposes DRLQ, a novel Deep Reinforcement Learning (DRL)-based technique for task placement in quantum cloud computing environments, addressing the optimization of task completion time and quantum task scheduling efficiency. It leverages the Deep Q Network (DQN) architecture, enhanced with the Rainbow DQN approach, to create a dynamic task placement strategy. This approach is one of the first in the field of quantum cloud resource management, enabling adaptive learning and decision-making for quantum cloud environments and effectively optimizing task placement based on changing conditions and resource availability. We conduct extensive experiments using the QSimPy simulation toolkit to evaluate the performance of our method, demonstrating substantial improvements in task execution efficiency and a reduction in the need to reschedule quantum tasks. Our results show that utilizing the DRLQ approach for task placement can significantly reduce total quantum task completion time by 37.81% to 72.93% and prevent task rescheduling attempts compared to other heuristic approaches.