连续变量相干态量子神经网络模型的构建

Construction of continuous-variable coherent state quantum neural network model

为了将功能强大的神经网络应用到连续变量量子信息处理中,需要建立连续变量的量子神经网络(QNN)模型。以相干态量子逻辑门为基元,基于QNN原理构建了由输入层、隐藏层和输出层组成的量子线路,实现了连续变量相干态量子神经网络(CSQNN)功能。模型通过多控CNOT门实现量子态操作,利用相位旋转门完成网络参数的学习训练。仿真结果表明在CSQNN辅助下,阻尼系数为0.5的振幅阻尼信道的量子隐形传态保真度显著提高,趋近1,说明提出的CSQNN模型能有效处理连续变量量子信息。

In order to apply a powerful neural network to the continuous-variable quantum inibrmation processing, it is necessary to construct the continuous-variable quantum neural network （QNN） model. Coherent state quantum logic gates are taken as basic elements. Quantum circuit composed of input layer, hidden layer and output layer is constructed based on QNN principle, and the function of continuous-variable coherent state quantum neural network （CSQNN） is realized. The model realizes quantum state operation by using multi-bit CNOT gate, and the learning training of network parameters is completed by using phase rotation gates. Simulation results show that under the assistance of CSQNN, the quantum teleportation fidelity of amplitude damping channel with damping coefficient of 0.5 is significantly improved, and its value approaches 1. It＇s shown that the proposed CSQNN model can effectively deal with the continuous-variable quantum information.