LI Panchi and ZHAO Ya, “Model and Algorithm of Sequence-Based Quantum-Inspired Neural Networks,” Chinese Journal of Electronics, vol. 27, no. 1, pp. 9-18, 2018, doi: 10.1049/cje.2017.11.007
Citation: LI Panchi and ZHAO Ya, “Model and Algorithm of Sequence-Based Quantum-Inspired Neural Networks,” Chinese Journal of Electronics, vol. 27, no. 1, pp. 9-18, 2018, doi: 10.1049/cje.2017.11.007

Model and Algorithm of Sequence-Based Quantum-Inspired Neural Networks

doi: 10.1049/cje.2017.11.007
Funds:  This work is supported by the National Natural Science Foundation of China (No.61170132, No.61402099), the Natural Science Foundation of Heilongjiang Province, China (No.F2015021), and the Scientific Technology Research Project of the Education Department of Heilongjiang Province, China (No.12541059).
  • Received Date: 2015-11-30
  • Rev Recd Date: 2016-03-30
  • Publish Date: 2018-01-10
  • To enhance the approximation ability of traditional Artificial neural network (ANN), by introducing the quantum rotation gates and the multi-qubits controlled-NOT gates to ANN, we proposed a Sequence input-based quantum-inspired neural network (SIQNN). In our model, the hidden nodes are composed of some multi-qubits controlled-NOT gates, the inputs are described by the multi-dimensional discrete qubits sequences, the output nodes are the traditional neurons. The model parameters include the rotation angles of quantum rotation gates in hide layer and the weights in output layer. The learning algorithms were derived by employing the Levenberg-Marquardt algorithm. Simulation results of predicting the runoff of the Hongjiadu Reservoir show that, the SIQNN is obviously superior to the ANN.
  • loading
  • A.C. Tsoi and A.D. Back, "Locally recurrent globally feed forward network:A critical review of architectures", IEEE Transactions on Neural Network, Vol.7, No.1, pp.229-239, 1994.
    M. Maria, A. Marios and C. Chris, "Artificial neural network for earthquake prediction using time series magnitude data or seismic electric signals", Expert Systems with Applications, Vol.38, No.9, pp.15032-15039, 2011.
    S. Kak, "On quantum neural computing", Information Sciences, Vol.83, No.7, pp.143-160, 1995.
    P. Gopathy and B.K. Nicolaos, "Quantum neural network (QNN's):Inherently fuzzy feed-forward neural network", IEEE Transactions on Neural Network, Vol.8, No.2,pp.679-693,1997.
    M. Zak and C.P. Williams, "Quantum neural nets", International Journal of Theoretical Physics, Vol.3, No.11, pp.651-684, 1998.
    M. Maeda, M. Suenaga and H. Miyajima, "Qubit neuron according to quantum circuit for XOR problem", Applied Mathematics and Computation, Vol.185, No.10, pp.1015-1025, 2007.
    S. Gupta and R.K. Zia, "Quantum neural network", Journal of Computer and System Sciences, Vol.63, No.12, pp.355-383, 2001.
    F. Shafee, "Neural network with quantum gated nodes", Engineering Applications of Artificial Intelligence, Vol.20, No.5, pp.429-437, 2007
    P.C. Li and S.Y. Li, "Learning algorithm and application of quantum BP neural network based on universal quantum gates", Journal of Systems Engineering and Electronics, Vol.19, No.1, pp.167-174, 2008.
    P.C. Li, K.P. Song and E.L. Yang, "Model and algorithm of neural network with quantum gated nodes", Neural Network World, Vol.11, No.4, pp.189-206, 2010.
    J. Adenilton, R. Wilson amd B. Teresa, "Classical and superposed learning for quantum weightless neural network", Neurocomputing, Vol.75, No.8, pp.52-60, 2012.
    G.C. Israel, G.C. Angel and R.M. Belen, "An optimization methodology for machine learning strategies and regression problems in ballistic impact scenarios", Applied Intelligence, Vol.36, No.5, pp.424-441, 2013.
    P.C. Li and H. Xiao, "Model and algorithm of quantum-inspired neural network with sequence input based on controlled rotation gates", Applied Intelligence, Vol.40, No.1, pp.107-126, 2014.
    T.H. Martin, B.D. Howard and H.B. Mark, Neural Network Design, PWS Publishing Company, New York, USA, pp.391-399, 1996.
  • 加载中


    通讯作者: 陈斌,
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (442) PDF downloads(273) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint