In this paper, a quantum neural network with multilayer activation function is proposed by using multilayer Sigmoid function superposition and learning algorithm to adjust quantum interval. On this basis, the quasiuniform stability of fractional quantum neural networks with mixed delays is studied. According to the order of two different cases, the conditions of quasi uniform stability of networks are given by using the techniques of linear matrix inequality analysis, and the sufficiency of the conditions is proved. Finally, the feasibility of the conclusion is verified by experiments.

中文翻译：

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基于Caputo型分数阶量子神经网络的稳定性分析

本文利用多层Sigmoid函数叠加和学习算法调整量子间隔，提出了一种具有多层激活函数的量子神经网络。在此基础上，研究了具有混合时滞的分数阶量子神经网络的拟均匀稳定性。根据两种不同情况的顺序，利用线性矩阵不等式分析技术，给出了网络拟一致稳定的条件，并证明了条件的充分性。最后通过实验验证了结论的可行性。