Quantum-Assisted Joint Virtual Network Function Deployment and Maximum Flow Routing for Space Information Networks
<|reference_start|>Quantum-Assisted Joint Virtual Network Function Deployment and Maximum Flow Routing for Space Information Networks: Network function virtualization (NFV)-enabled space information network (SIN) has emerged as a promising method to facilitate global coverage and seamless service. This paper proposes a novel NFV-enabled SIN to provide end-to-end communication and computation services for ground users. Based on the multi-functional time expanded graph (MF-TEG), we jointly optimize the user association, virtual network function (VNF) deployment, and flow routing strategy (U-VNF-R) to maximize the total processed data received by users. The original problem is a mixed-integer linear program (MILP) that is intractable for classical computers. Inspired by quantum computing techniques, we propose a hybrid quantum-classical Benders' decomposition (HQCBD) algorithm. Specifically, we convert the master problem of the Benders' decomposition into the quadratic unconstrained binary optimization (QUBO) model and solve it with quantum computers. To further accelerate the optimization, we also design a multi-cut strategy based on the quantum advantages in parallel computing. Numerical results demonstrate the effectiveness and efficiency of the proposed algorithm and U-VNF-R scheme.<|reference_end|>
@article{zhang2024quantum-assisted,
    title={Quantum-Assisted Joint Virtual Network Function Deployment and Maximum
  Flow Routing for Space Information Networks},
    author={Yu Zhang, Yanmin Gong, Lei Fan, Yu Wang, Zhu Han, and Yuanxiong Guo},
    journal={arXiv preprint arXiv:2409.13508},
    year={2024},
    doi={10.1109/TMC.2024.3466857},
    archivePrefix={arXiv},
    eprint={2409.13508},
    primaryClass={cs.NI}
}