The invention discloses an image classification method based on an extensible
quantum convolutional neural network, and relates to the technical field of image classification, and the method comprises the steps: carrying out the preprocessing of to-be-classified image data, and then carrying out the
slicing processing; converting the slice data into
quantum state information through angle coding; a
quantum system is constructed, the
quantum system comprises a quantum
feature extraction part and a quantum classification measurement part, the quantum
feature extraction part comprises a quantum variational circuit model with a quantum convolutional layer, and the model can perform
feature extraction on
quantum state information by using alternating single quantum bit gates and
double quantum bit gates; training a quantum variational circuit model by using a part of
quantum state information; and inputting the other part of
quantum state information into the trained quantum variational circuit model, and measuring the output quantum state of the quantum variational circuit model. According to the method, the problems that traditional
deep learning is high in computing resource demand, limited in
quantum computer scale and the like are solved, and a novel and practical method is provided for efficient image classification.