A hybrid quantum-inspired neural networks with sequence inputs

Neurocomputing

Volumn 117, Issue , 2013, Pages 81-90

  Li, Panchi ^a   Xiao, Hong ^a   Shang, Fuhua ^a   Tong, Xifeng ^a   Li, Xin ^a   Cao, Maojun ^a  

^a NORTHEAST PETROLEUM UNIVERSITY   (China)

Author keywords

Controlled Hadamard gates; Quantum computation; Quantum neural networks; Quantum neuron; Quantum rotation gates

Indexed keywords

BP NEURAL NETWORKS; CLASSICAL NEURAL NETWORKS; CONTROLLED-HADAMARD GATES; DISCRETE SEQUENCES; NEURAL NETWORKS MODEL; QUANTUM NEURAL NETWORKS; QUANTUM NEURON; QUANTUM ROTATION;

LEARNING ALGORITHMS; NEURONS; QUANTUM COMPUTERS;

NEURAL NETWORKS;

QUANTUM DOT;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLINICAL EFFECTIVENESS; CONTROLLED HADAMARD QUANTUM NEURAL NETWORK; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MATHEMATICAL PHENOMENA; MULTI QUBIT CONTROLLED HADAMARD GATE; PRIORITY JOURNAL; PROBABILITY; PROCESS DEVELOPMENT; PROCESS MODEL; QUANTUM NEURON MODEL; QUANTUM ROTATION GATE; QUANTUM THEORY;

EID: 84878909063     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.01.029     Document Type: Article

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