Abstract: Aiming at the question that the result trained by quantum neural networks is easy to fall into the local least value,the Levenberg-Marquardt algorithm was introduced into the original training algorithm to increase the training speed and improve the performance of the network．In addition,the conflict between different objective functions used for the training synaptic weights and quantum intervals can cause mean square error fluctuates．In order to solve this problem,the iteration order of the original training algorithm was adjusted．The experimental results show that,compared with the original training algorithm,the algorithm using LM can significantly reduce the number of iterations,significantly decrease the mean square error when the network convergences,and improve the classification results of quantum neural network.

Key words: Quantum neural networks,Levenberg-marquardt algorithm,Steepest descent,Quantum intervals