Application of Quantum Self-Organization Mapping Networks to Classification

Jing Hai Yang

doi:10.4028/www.scientific.net/AMM.411-414.707

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 411-414Application of Quantum Self-Organization Mapping...

Application of Quantum Self-Organization Mapping Networks to Classification

Abstract:

A quantum self-organization 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 mapping networks.

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Periodical:

Applied Mechanics and Materials (Volumes 411-414)

Pages:

707-711

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.411-414.707

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Cite this paper

Online since:

September 2013

Authors:

Jing Hai Yang

Keywords:

Quantum Clustering Algorithm, Quantum Computing, Quantum Neuron, Quantum Self-Organization Networks

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