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 are quantized and transformed into their corresponding quantum state representations.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 compared with the classical K-means algorithm,the proposed method not only effectively solves the platform clustering and grouping problem on such action scales,but also significantly reduces the time and space complexity of the algorithm.
维普
万方数据