Quantum Self-Organization Feature Mapping Networks with Application

A quantum self-organization feature mapping networks model based on quantum neurons is presented in this paper. Both the input and the weight of the model are represented by the quantum bits, and the output of the model is represented by the real number. The model is composed of input layer and competitive layer. First, the samples are transformed into quantum states and are submitted to the input layer, and then the similar coefficients of quantum states are computed between the inputs and the weights. Secondly, the implicit pattern characters of the clustering samples are extracted in the competitive layer, and then the clustering results are showed. The quantum states of weights are updated by quantum rotation gates. The networks are trained by the algorithm combining the unsupervised learning and supervised learning together. Finally two experiments demonstrate that the model and algorithm are evidently superior to the general self-organization feature mapping networks.