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中文摘要：
本文采用粒子群优化（PSO）方法代替量子门来更新量子比特状态,得到一种改进的量子遗传算法（QGA）--PSQGA,并根据QGA自身概率特性,引入了最优解方差函数来评价该算法的稳定性能.利用四种典型连续函数寻优问题和0/1背包问题,分别对PSQGA和改进的使用量子门的量子遗传算法（IQGA）进行了测试;并将它们应用到图像稀疏分解的实例中.结果表明,PSQGA算法的寻优能力及稳定性均优于IQGA,且具有更好的收敛性以及更强的连续空间搜索能力,适合于求解复杂优化问题.
英文摘要：
This paper proposes a novel quantum genetic algorithm （QGA） PSQGA, which uses particle swarm optimization method instead of quantum gate to update the state of quantum bit. It has the advantages of particle swarm optimization and quantum genetic algorithm. A variance function is introduced to estimate the stability of the algorithm. Though the experiments of four continuous functions and combination optimization problems, as well as its application to image sparse decomposition. Compared with the improved algorithm which involved quantum gate （ IQGA）, the ability of finding the best solution and the stability of PSQGA are greatly improved. PSQGA has better convergent property and ability of searching more extensive space. It is fit for the solution of complex optimization problems.
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