The research on dynamic optimization problems (DOPs) has attracted significant attention due to its wider presence in many areas and industries. However, the changing nature and generally limited computational resources in DOPs bring challenges to optimization algorithms that perform well in static environments, such as evolutionary algorithms. This paper introduces quantum entanglement into a landscape-based genetic algorithm, where both the quantum entanglement operator and genetic algorithm are used to search for the optimal solution, while a landscape-based strategy is employed to utilize the knowledge learned from previous problem environments. The performance of the proposed algorithm is tested on the bench-mark generator for IEEE CEC'2009 Competition on dynamic optimization with four types of landscape measures, and quantum entanglement has achieved average fitness improvements of 10.86%, 10.79%, 12.26% and 12.36%, respectively on the four landscane measures.