Abstract: Quantum bacterial foraging optimization algorithm is an quantum intelligence algorithm which is based on the concept of quantum computing and bacteria foraging optimization algorithm. However, this algorithm exists the defects of poor robustness and the problem of long running time in optimization. To solve these problems, this paper designs a quantum rotation gate which has a adaptive phase rotation. Using this rotation gate simulating the bacterial chemotaxis operation, this paper proposes a quantum foraging algorithm based on adaptive phase rotation. To test the new algorithm’s optimization performance, a research based on sixteen benchmark functions is conducted. The results indicate that in the situation of low dimension, the new AQBFO algorithm shows better results than QBFO in convergence precision and stability, say nothing of QGA and BFO. Further research shows that, average convergence probability of the proposed algorithm is the highest and the average running time and average running steps are the shortest among the four algorithms when reach the specified convergence precision. While in the situation of high dimension, this algorithm is suitable for bowl shape and plate shape benchmark functions.