With the overwhelming success in the field of quantum computing, much attention has been paid to constructing a quantum neural network by combining a classical neural network with quantum computing. In this paper, we propose a novel quantum neural network model based on a quantum version of the sigmoid function, which skillfully combines the non-linear dissipation dynamics of neural computation with the linear unitary dynamics of quantum computation. Moreover, we also add connections from the input layer to the output layer to increase the non-linear expression ability of the network and the similarity to the human brain’s information processing. The specific steps and relevant formulas of the conjugate gradient algorithm in the learning stage of the quantum network parameters are also given in this paper. Finally, the feasibility and properties of the model are demonstrated by MATLAB simulation with a encryption and decryption experiment.

中文翻译：

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新型量子神经网络的设计

随着量子计算领域的巨大成功，通过将经典神经网络与量子计算相结合来构建量子神经网络已引起了很多关注。在本文中，我们提出了一种基于S型函数量子形式的新型量子神经网络模型，该模型巧妙地将神经计算的非线性耗散动力学与量子计算的线性unit动力学结合起来。此外，我们还增加了从输入层到输出层的连接，以增加网络的非线性表达能力以及与人脑信息处理的相似性。本文还给出了共轭梯度算法在量子网络参数学习阶段的具体步骤和相关公式。最后，