为提高粒子群算法的搜索能力和优化效率并避免早熟收敛，将量子进化算法融合到粒子群算法中，提出一种求解连续空间优化问题的量子粒子群优化算法。用量子位的概率幅对粒子位置编码，用量子旋转门实现粒子移动，完成粒子搜索；用量子非门实现变异，提高种群多样性。因每个量子位有两个概率幅，故每个粒子同时占据空间两个位置，在粒子数目相同时，能加速粒子的搜索进程。实验结果表明，本算法优于基本粒子群算法。

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.