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Quantum-behaved Particle Swarm Optimization algorithm (QPSO) is a new variant of Particle Swarm Optimization (PSO). It is also a population-based search strategy, which has good performance on well-known numerical test problems. QPSO is based on the standard PSO and inspired by the theory of quantum physics. In this paper, we explore the parallelism of QPSO and implement the parallel QPSO based on the Neighborhood Topology Model, which is much closer to the nature world. The performance of the parallel QPSO is compared to PSO and QPSO on a set of benchmark functions. The results show that the parallel QPSO outperforms the other two algorithms.