Quantum K-Nearest-Neighbor Image Classification Algorithm Based on K-L Transform

Enlightened by quantum computing theory, a quantum K-Nearest-Neighbor image classification algorithm with the K-L transform is proposed. Firstly, the image features are extracted by the K-L transform. Then the image features are mapped into quantum states by quantum coding. Next, the Hamming distance between image features is computed and utilized to express the similarity of the image. Afterward, the image is classified by a new distance-weighted k value classification method. Finally, the classification results of the image are obtained by measuring the quantum state. Theoretical analysis shows that the presented quantum K-Nearest-Neighbor image classification algorithm could reduce the time complexity. Simulation experiments based on MNIST, Fashion-MNIST and CIFAR-10 data sets demonstrate that the proposed quantum K-Nearest-Neighbor algorithm has relatively higher classification accuracy.