Open AccessModel and Algorithm of Sequence‐Based Quantum‐Inspired Neural Networks
Author(s) -
LI Panchi,
ZHAO Ya
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.11.007
Subject(s) - qubit , artificial neural network , algorithm , rotation (mathematics) , quantum gate , sequence (biology) , computer science , quantum , topology (electrical circuits) , quantum computer , quantum circuit , artificial intelligence , mathematics , quantum error correction , physics , quantum mechanics , combinatorics , biology , genetics
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.