摘要
量子门线路神经网络(QGCNN)是一种直接利用量子理论设计神经网络拓扑结构或训练算法的量子神经网络模型。动量更新是在神经网络的权值更新中加入动量,在改变权值向量的同时提供一个特定的惯量,从而避免权值向量在网络训练过程中持续振荡。在基本的量子门线路神经网络的学习算法中引入动量更新原理,提出了一种具有动量更新的量子门线路网络算法(QGCMA)。研究表明,QGCMA保持了网络100%的收敛率,同时,相对于基本算法,在具有相同学习速率的情况下,提高了网络的收敛速度。
Quantum Gate Circuit Neural Network（QGCNN）is a kind of quantum neural network model, which directly uses quantum theory to design the neural network topology or training algorithms. In the neural network, Momentum update is adding momentum parameter in weight renew and provides a specific inertia while renewing weight vector. It avoids sustained oscillation of weight vector in network training. It introduces the principle of momentum update in the basic learning algorithm of Quantum Gate Circuit Neural Network, proposes Quantum Gate Circuit neural network Momentum update Algorithm（QGCMA）. The research shows that QGCMA has 100% convergence rate and enhances convergence speed compared to the basic algorithm with the same learning rate.
出处
《计算机工程与应用》
CSCD
2014年第6期213-218,共6页
Computer Engineering and Applications
关键词
量子神经网络
量子计算
量子门
动量更新
学习算法
权值
Quantum Neural Network （QNN）
quantum computation
quantum gate
momentum update
leaming algo-rithm
weight
