文章基本信息

标题：Quantum-inspired Neural Networks with Applications
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作者：Maojun Cao ; Panchi Li
期刊名称：International Journal of Computer and Information Technology
印刷版ISSN：2279-0764
出版年度：2014
卷号：3
期号：1
页码：83
出版社：International Journal of Computer and Information Technology
摘要：On the basis of analyzing the principles of the quantum rotation gates and quantum controlled-NOT gates, an improved design for CNOT gated quantum neural networks model is proposed and a smart algorithm for it is derived based on the Levenberg-Marquardt algorithm in our paper. In improved model, the input information is expressed by the qubits, which, as the control qubits after rotated by the rotation gate, control the qubits in the hidden layer to reverse. The qubits in the hidden layer, as the control qubits after rotated by the rotation gate, control the qubits in the output layer to reverse. The networks output is described by the probability amplitude of state |1 > in the output layer. It has been shown in two application examples of modeling of acrylamide homogeneous polymerization process and wine recognition that the proposed model is superior to the common BP networks with regard to their convergence ratio, number of iterations, approximation and generalization ability.
关键词：quantum computing; quantum neural networks; ; quantum gate; learning algorithm