量子蚁群算法是一种将量子理论与传统蚁群算法结合的高效生物进化算法，主要应用于故障诊断、路径规划、图像分割等问题的优化. 基于传统蚁群算法的流程，介绍量子蚁群算法中的量子理论基础以及量子理论如何应用于蚁群算法. 通过若干旅行商问题实例分析量子蚁群算法较传统蚁群算法的优势. 针对目前研究多以离散指标来评估不同算法、难以直观显示不同算法综合差别的问题，提出一种综合评估算法搜索效率的方法，成功应用于量子蚁群算法和传统蚁群算法的对比，具有一定的实践意义. 

Quantum ant colony algorithm (QACA) is an efficient biological evolutionary algorithm. It combines quantum theory with traditional ant colony optimization algorithm (ACO). It is mainly applied to the solving of fault diagnosis,path planning,image segmentation. Based on the process of ACO,this paper introduces the quantum theory foundation of QACA and how it is applied to QACA. The advantages of QACA over ACO are analyzed by applying them to several examples of traveling salesman problem. Current research often uses discrete indexes to evaluate different algorithms,and it is difficult to intuitively display the differences of algorithms. In view of that,a comprehensive evaluation method for algorithm search efficiency is proposed. It is successfully applied to the comparison between QACA and ACO.