In 'INTERNET + TIME', cloud computing represents a novel business model. However, the cloud user tasks in the system and compute node scheduling problem significantly affects the system performance and competitiveness of cloud. An improved algorithm of quantum particlesadaptive quantum particle swarm optimization (RAQPSO), based on the inertia weight adjustment of parameters and reverse learning to improve the global search ability of the algorithm, and applied to cloud computing resource scheduling problem to verify the effectiveness of the algorithm. With cloud computing resource scheduling model is established. And then uses the adaptive mechanism, the change of the fitness function as update of inertia weight factor, avoids simply value according to the linear function of the number of iterations. Add the particle reverse learning operator, to strengthen the global search ability particles. The experimental results show that the RAQPSO algorithm greatly save the task completion time, and keep a good computing nodes load balancing.