为提高神经网络的逼近能力，提出一种各维输入为离散序列的量子神经网络模型及算法。该模型为3层结构，隐层为量子神经元，输出层为普通神经元。量子神经元由量子旋转门和多位受控非门组成，利用多位受控非门中目标量子位的输出向输入端的反馈，实现对输入序列的整体记忆，利用受控非门输出中多位量子比特的纠缠获得量子神经元的输出。基于量子计算理论设计该模型的学习算法。该模型可从宽度和深度两方面获取输入序列的特征。仿真结果表明，当输入节点数和序列长度满足一定关系时，该模型明显优于普通神经网络。%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.
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