2022/09/16 by Napat Ngoenriang, Minrui Xu, Ngoenriang, Napat +10 · 1 citation
Computer Science · #Cloud Computing and Resource Management #Computer Science and Game Theory (cs.GT) #Distributed #FOS: Computer and information sciences #FOS: Physical sciences #IoT and Edge/Fog Computing #Parallel #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2210.02887
openalex publication_date 2022/09/16 · openalex created_date 2022/10/08 · openalex updated_date 2026/07/28
With the advantages of high-speed parallel processing, quantum computers can efficiently solve large-scale complex optimization problems in future networks. However, due to the uncertain qubit fidelity and quantum channel noise, distributed quantum computing which relies on quantum networks connected through entanglement faces a lot of challenges for exchanging information across quantum computers. In this paper, we propose an adaptive distributed quantum computing approach to manage quantum computers and quantum channels for solving optimization tasks in future networks. Firstly, we describe the fundamentals of quantum computing and its distributed concept in quantum networks. Secondly, to address the uncertainty of future demands of collaborative optimization tasks and instability over quantum networks, we propose a quantum resource allocation scheme based on stochastic programming for minimizing quantum resource consumption. Finally, based on the proposed approach, we discuss the potential applications for collaborative optimization in future networks, such as smart grid management, IoT cooperation, and UAV trajectory planning. Promising research directions that can lead to the design and implementation of future distributed quantum computing frameworks are also highlighted.