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- W4306973586 abstract "Mahalanobis distance is a distance measure that takes into account the relationship between features. In this paper, we proposed a quantum K NN classification algorithm based on the Mahalanobis distance, which combines the classical K NN algorithm with quantum computing to solve supervised classification problem in machine learning. Firstly, a quantum sub-algorithm for searching the minimum of disordered data set is utilized to find out K nearest neighbors of the testing sample. Finally, its category can be obtained by counting the categories of K nearest neighbors. Moreover, it is shown that the proposed quantum algorithm has the effect of squared acceleration compared with the classical counterpart." @default.
- W4306973586 created "2022-10-22" @default.
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- W4306973586 date "2022-10-21" @default.
- W4306973586 modified "2023-09-27" @default.
- W4306973586 title "Quantum K-nearest neighbors classification algorithm based on Mahalanobis distance" @default.
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- W4306973586 doi "https://doi.org/10.3389/fphy.2022.1047466" @default.
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