DQC2O: Distributed Quantum Computing for Collaborative Optimization in Future Networks

Abstract

With the advantages of high-speed parallel processing, quantum computers can efficiently solve large-scale complex optimimization 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 entaglement, faces many challenges in exchanging information across quantum computers. In this paper, we propose an adaptive distributed quantum computing approach, called DQC2O, to manage quantum computers and quantum networks 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 military applications of collaborative optimization in future networks, such as smart grid management, IoT cooperation, and semantic communications. Promising research directions that can lead to the design and implementation of future distributed quantum computing frameworks are also highlighted.