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

作　　者：LI Panchi ZHAO Ya 

机构地区：[1]School of Computer and Information Technology, Northeast Petroleum University

出　　处：《Chinese Journal of Electronics》2018年第1期9-18,共10页电子学报（英文版）

基　　金：supported by the National Natural Science Foundationof China(No.61170132,No.61402099);the Natural Science Foundation of Heilongjiang Province,China(No.F2015021);the Scientific Technology Research Project of the Education Department of Heilongjiang Province,China(No.12541059)

摘　　要：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.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.

关 键 词：Quantum computation Quantum rotation gate Multi-qubits controlled-NOT gate Quantum inspired neuron Quantum-inspired neural network 

分 类 号：TN[电子电信]