Research on quantum gate circuit neural network and improved learning algorithm
Hou Xua · Computer Engineering and Applications Journal · 2014
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.