A new parallel adaptive quantum particle swarm opitimzation algorithm is proposed in this paper to solve the problem that standard particle swarm optimization(PSO) algorithm may easily trap into local optimal points and may obtain exact solutions at the late of the iteration with difficultly. By sharing the two extreme values of the particles, the proposed method is able to adaptively search their optimum solutions in parallel by combination of an improved adaptive PSO with a quantum Particle Swarm Optimization of boundary variation. It is proved effectively to overcome the shortcomings of standard PSO. Test results show that the accuracy and the velocity of global search for optimal solutions have been greatly improved.