Research Article
Optimal Task Scheduling in 6G Networks: A Variational Quantum Computing Approach
@INPROCEEDINGS{10.1007/978-3-031-67357-3_5, author={Uman Khalid and Junaid ur Rehman and Ahmad Farooq and Fakhar Zaman and Hyundong Shin}, title={Optimal Task Scheduling in 6G Networks: A Variational Quantum Computing Approach}, proceedings={Industrial Networks and Intelligent Systems. 10th EAI International Conference, INISCOM 2024, Da Nang, Vietnam, February 20--21, 2024, Proceedings}, proceedings_a={INISCOM}, year={2024}, month={7}, keywords={Cloud radio access network Task scheduling Variational quantum computing Quantum approximate optimization algorithm}, doi={10.1007/978-3-031-67357-3_5} }- Uman Khalid
Junaid ur Rehman
Ahmad Farooq
Fakhar Zaman
Hyundong Shin
Year: 2024
Optimal Task Scheduling in 6G Networks: A Variational Quantum Computing Approach
INISCOM
Springer
DOI: 10.1007/978-3-031-67357-3_5
Abstract
Optimal task scheduling in 6G networks plays a crucial role in enabling a wide range of applications such as augmented reality, virtual reality, autonomous vehicles, and the Internet of Things (IoT). With the network landscape becoming a more complex and diverse ecosystem, it is critical to advance conventional scheduling algorithms in order to guarantee the necessary efficiency and performance. In this regard, quantum computing can significantly speed up search for optimal schedules, increase the likelihood of finding optimal solutions, and facilitate the creation of correlations between tasks in a scheduling problem by virtue of parallelism, superposition, and entanglement. In this paper, we explore the variational quantum computing approach to tackle the complex task scheduling problem in cloud radio access network (C-RAN) architecture for 6G networks. By leveraging the quantum approximate optimization algorithm (QAOA) and utilizing IBM Qiskit as a simulation testbed, we aim to optimize task scheduling for enhancing wireless network performance. Herein, the classical quadratic constrained integer optimization (QCIO) problem instance is transformed to an Ising Hamiltonian formulation to implement task scheduling optimization on a quantum computer. We also evaluate the effectiveness and stability of QAOA by analyzing the expected cost and the probability of obtaining an optimal schedule as a function of QAOA circuit layers. Our findings highlight the applicability of variational quantum computing in addressing intricate optimization problems as well as setting the stage for the development of more advanced quantum optimization algorithms for 6G networks.
