To improve search ability and optimization efficiency and to avoid premature convergence for particle swarms optimization,a novel quantum particle swarm optimization for continuous space optimization is proposed. The positions of particles are encoded by the probability amplitudes of quantum bits,the movements of particles are performed by quantum rotation gates,which achieve particles searching. The mutations of particles are performed by quantum non-gate,which increase the diversity of particles. As each quantum bit contains two probability amplitudes,each particle occupies two positions in space. Hence,the process of searching is accelerated. The experimental results show that the algorithm proposed is superior to the basic particle swarms optimization.