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针对联合作战战役行动中平台聚类编组问题，本文提出了一种基于量子K-means的量子增强求解方法。该方法首先分别对经典K-means算法中的聚类类别数目设定和聚类中心点选择两部分进行了优化处理；其次，该方法针对聚类数据样本与各聚类中心点之间的欧氏距离构建对应的量子线路；然后，该方法针对聚类数据集的误差平方和构建对应的量子线路。实验结果表明，所提方法不但有效解决了此类行动规模下的平台聚类编组问题，与经典K-means算法相比，算法的时间复杂度和空间复杂度都有较大幅度降低。
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关键词:
- 量子K-means算法 /
- 量子增强 /
- 平台聚类
The paper proposes a quantum enhanced solution method based on quantum K-means for platform clustering and grouping in joint operations campaigns. The method first calculates the number of categories for platform clustering based on the determined number of task clusters, and sets the number of clustering categories in the classical K-means algorithm. By using the location information of the tasks, the clustering center points are calculated and derived. Secondly, the Euclidean distance is used as an indicator to measure the distance between the platform data and each cluster center point. The platform data is quantized and transformed into the corresponding quantum state representation. According to theoretical derivation, the Euclidean distance solution is transformed into the quantum state inner product solution. By designing and constructing a universal quantum state inner product solution quantum circuit, the Euclidean distance solution is completed. Then, based on the sum of squared errors of the clustering dataset, the corresponding quantum circuits are constructed through calculation and deduction. The experimental results show that the proposed method not only effectively solves the platform clustering and grouping problem under such action scales, but also significantly reduces the time and space complexity of the algorithm compared to the classical K-means algorithm.-
Keywords:
- QK-means algorithm /
- Quantum Enhancement /
- platform clustering
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