Abstract：To enhance the approximation capability of neural networks,a quantum neural networks model is proposed whose input of each dimension is in discrete sequence. This model includes three layers,in which the hidden layer consists of quantum neurons,and the output layer consists of common neurons. The quantum neuron consists of the quantum rotation gates and the multi-qubits controlled-not gates. By using the information feedback of target qubit from output to input in multi-qubits controlled-not gate,the overall memory of input sequences is realized. The output of quantum neuron is obtained from the entanglements of multi-qubits in controlled-not gates. The learning algorithm is designed in detail according to the basis principles of quantum computation. The characteristics of input sequence can be effectively obtained from the width and the depth. The simulation results show that,when the input nodes and the length of the sequence satisfy a certain relations,the proposed model is superior to the common artificial neural networks.
李盼池，施光尧. 基于序列输入的量子神经网络模型及算法[J]. 模式识别与人工智能, 2013, 26(3): 247-253.
LI Pan-Chi,SHI Guang-Yao. Sequence-Input Based Quantum Neural Networks Model and Its Algorithm. , 2013, 26(3): 247-253.
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