  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.
