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

LI Panchi; ZHAO Ya

doi:10.1049/cje.2017.11.007

Volume 27 Issue 1

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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

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

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

doi: 10.1049/cje.2017.11.007

LI Panchi,
ZHAO Ya

School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China

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

Abstract

Abstract

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.
- Quantum computation,
- Quantum rotation gate,
- Multi-qubits controlled-NOT gate,
- Quantuminspired neuron,
- Quantum-inspired neural network

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References(14)

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