Quantum K-nearest neighbors classification algorithm based on Mahalanobis distance - Gao, Li-Zhen; Lu, Chun-Yue; Guo, Gong-De; Zhang, Xin; Lin, Song (Frontiers in Physics, 2022)
Mahalanobis distance is a distance measure that takes into account the relationship between features.In this paper, we proposed a quantum KNN classification algorithm based on the Mahalanobis distance, which combines the classical KNN algorithm with quantum computing to solve supervised classification problem in machine learning. Firstly, the quantum parallelism is utilized to calculate the Mahalanobis distance between the training samples and the test one. Then, 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. It is shown that the proposed quantum algorithm has the effect of squared acceleration compared with the classical counterpart.