Abstract: In order to solve the problem that the process of cross and mutation in traditional genetic algorithm is too cumbersome， and the extreme value judgment and the convergence rate is limited， a parallel quantum genetic algorithm(QGA) is proposed to optimize the weights of the neural network. The concept of quantum computing is firstly introduced. In the process of quantum computation， the quantum rotation gate is used to train the chromosomes. Then the quantum cross is introduced to overcome the precocious convergence and to avoid the cumbersome cross and mutation process in the genetic algorithm. Finally， the parallel convolution neural network is designed and implemented. The parallel quantum genetic algorithm is used to optimize the weights of the convolution neural network， and a facial recognition system based on parallel quantum genetic neural network is realized. Experimental results show that compared with the original genetic algorithm， the quantum genetic neural network algorithm has obvious improvements in terms of robustness and processing speed.

Key words: multi-core parallel, quantum computing, genetic algorithm, neural network