In traditional quantum-inspired evolutionary algorithm (QEA)

a quantum rotate gate is the main operator in a quantum population evolution.However

the choice of rotate angle is also discrete and constant

which makes the search of the problem easy to fall into local optimum.Therefore

a modified quantum rotate gate is proposed in this paper.The new gate uses adaptive method of calculation of rotation

which makes the population have a relatively good global search capability.At the same time

the probability amplitude is modified after rotation to enable population to jump out of local optimum.For data clustering problem

a quantum-inspired evolutionary algorithm based on the modified quantum rotate gate is proposed.The simulation experiment results show

compared with the algorithm based on a normal quantum rotate gate and some of other evolutional algorithms

the proposed algorithm increases correct rate of data clustering.At the same time

the simulation experiment results on the data sets of symmetrical distribution property show

compared with the algorithms adopting a symmetrical distance measure

our algorithm also achieves better results.