由于说话人的语音信号具有时变性、随机性，其特征参数也呈现出高维及相邻帧变化较大等特点。从量子信息处理理论出发，将一帧语音信号视为一个量子态，在传统神经网络的基础上，利用量子逻辑线路构造神经网络，实现说话人语音信号的有效聚类，探索一种基于量子逻辑线路神经网络的说话人识别模型与方法。利用模型固有的大量全局吸引子，可有效降低语音信号处理的时间及复杂度。通过在经典计算机上模拟仿真，并与BP神经网络说话人识别模型进行对比，表明该方法能够加快说话人识别模型的收敛速率，对参数变化具有更好的鲁棒性，且其系统识别率比BP神经网络方法平均提高了3.34%。

Under the same spatial and temporal conditions, the quantum computing is superior to the traditional computing. Because a speaker's speech signal features the time-varying property and the randomness, its characteristic parameters also show high-dimensional characters and large changes in adjacent frames. This paper, based on the quantum information processing theory, takes a frame of the speech signal as a quantum state, and uses quantum logic gate circuits to construct the neural network according to the traditional neural network, and obtains an efficient clustering of the speaker's speech signal. A speaker recognition model is built and a method based on the quantum logic circuit neural network is proposed. This model has a large number of global attractors, and the method can use them to effectively reduce the complexity of the speech signal processing. Through simulations on a classical computer and a comparison with the BP neural network speaker recognition model, it is shown that this method not only can accelerate the convergence rate of the model but also has a better robustness with respect to the parameter changes. The system's recognition rate with the method proposed in this paper is 3.34% in average higher than that with the BP neural network method.