Since the multi-level activation function quantum neural network (QNN) for pattern recognition was firstly proposed by Purushothaman and Karayiannis, more and more researches have been conducted on improving it. However, they all ignore that the QNN only uses multi-level activation function to simulate the concept of quantum superposition rather than really using quantum computing. In this paper, we propose a real QNN model based on multi-layer activation function. In addition, we present algorithms for updating weight parameters and quantum intervals, and also improve the learning algorithm for weight parameters using famous Levenberg–Marquardt algorithm. We also use the QNN for lie detection, and the simulation results of MATLAB prove that the performance of the model based on the corresponding algorithm are very strong.

由于Purushothaman和Karayiannis首次提出了用于模式识别的多级激活函数量子神经网络（QNN），因此进行了越来越多的研究。但是，他们都忽略了QNN仅使用多级激活函数来模拟量子叠加的概念，而不是真正使用量子计算。在本文中，我们提出了一个基于多层激活函数的真实QNN模型。此外，我们提出了用于更新权重参数和量子间隔的算法，并使用著名的Levenberg-Marquardt算法改进了权重参数的学习算法。我们还使用QNN进行测谎，并且MATLAB的仿真结果证明，基于相应算法的模型的性能非常强。