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Quantum process neural networks model algorithm and applications

Li Pan-Chi Wang Hai-Ying Dai Qing Xiao Hong

Quantum process neural networks model algorithm and applications

Li Pan-Chi, Wang Hai-Ying, Dai Qing, Xiao Hong
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  • PDF Downloads:  637
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Publishing process
  • Received Date:  30 September 2011
  • Accepted Date:  08 February 2012
  • Published Online:  05 August 2012

Quantum process neural networks model algorithm and applications

  • 1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China
Fund Project:  Project supported by the National Natural Science Foundation of China (Grant No. 61170132), the China Postdoctoral Science Foundation (Grant Nos. 20090460864, 201003405), the Postdoctoral Science Foundation of Heilongjiang Province, China (Grant No. LBH-Z09289), and the Scientific Research Foundation of the Education Department of Heilongjiang Province, China (Grant No. 11551015).

Abstract: To enhance the approximation and generalization ability of process neural networks (PNNs), by studying the quantum implementation mechanism of information processing of process neuron, a new idea of designing quantum process neuron is proposed in this paper, based on the quantum rotation gates and the quantum controlled-non gates. In the proposed approach, the discrete process inputs are expressed by the qubits, which, as the control qubits of controlled-non gates after being rotated by the quantum rotation gates, control the target qubits to reverse. The model outputs are described by the probability amplitude of state |1 in the target qubits. Then the quantum process neural networks (QPNNs) are designed by the quantum process neurons for the hidden layer and the normal neurons for the output layer. The algorithm of QPNN is derived through the quantum computing. The proposed approach is utilized to predict the smoothed yearly mean sunspot numbers, and the results indicate that the QPNN has higher prediction accuracy than the normal PNN, thus it has a certain theoretical meaning and practical value for the complex prediction.

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