J4 ›› 2017, Vol. 34 ›› Issue (4): 467-472.

• 量子光学 • 上一篇    下一篇

连续变量相干态量子神经网络模型的构建

陈珊琳,黄春晖   

  1. 福州大学物理与信息工程学院, 福建 福州 350116
  • 收稿日期:2016-11-28 修回日期:2017-02-28 出版日期:2017-07-28 发布日期:2017-08-29

Construction of continuous-variable coherent state quantum neural network model

1,   

  • Received:2016-11-28 Revised:2017-02-28 Published:2017-07-28 Online:2017-08-29

摘要: 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,根据量子神经网络原理,构建由输入层、隐藏层和输出层组成的量子电路,实现连续变量相干态量子神经网络(CSQNN)功能。模型通过多控CNOT门来实现量子态操作,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,对于阻尼系数为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。说明本文的CSQNN模型能有效地处理连续变量量子信息。

Abstract: In order to apply the powerful neural network to the continuous-variable quantum information processing, it is need to construct the continuous-variable quantum neural network model. In the basic element of coherent state quantum logic gates, quantum circuit is constructed with input layer, hidden layer and output layer according to the principle of quantum neural network, it realizes the function of continuous-variable coherent quantum neural network (CSQNN). Quantum states operation are realized by using a multi-bit CNOT gate, and the learning training of network parameters is completed by using phase rotation gates. The simulation results show that the fidelity of quantum teleportation system is significantly improved to approach 1 by the correction of CSQNN which through the amplitude damping channel with damping coefficient 0.5. It is showed that the CSQNN model can effectively deal with the quantum information of continuous variables.

Key words: quantum teleportation