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In existing graph state distribution methods, there is a lack of consideration for node resources. However, in practical quantum communication networks, the limited availability of quantum resources within nodes is one of the key factors that restrict communication efficiency. Therefore, proposing a graph state distribution method that takes into account the limited quantum resources of nodes is essential for the practical application of graph states. We introduce a segmentation-integration method for graph state distribution, which segments large-scale graph states into several subgraph states for separate preparation and distribution. Finally, by performing graph state-related operations on some nodes, the subgraph states are restored to the desired complete graph state. This approach effectively reduces the quantum resources required at individual nodes when generating large-scale graph states, helping to maintain the smooth operation of the entire quantum communication network and preventing congestion due to any single node.
Future research can focus on optimizing the segmentation algorithm, improving the efficiency of quantum gate operations, and exploring new applications of graph states in quantum networks.
For example, further studies can investigate the use of machine learning techniques to optimize the segmentation process or develop new quantum algorithms that leverage the properties of graph states for more efficient quantum computing and communication tasks.
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IEEE/ICACT20250011 Slide.19
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