Abstract: Background: Traditional genetic algorithm has low robustness and easily falling into local solution with a long convergence time. Materials and Methods: In order to improve the work efficiency and decrease large scale applications power in Network-on-chip (NoC), it puts forward a new Network-on-chip low-power mapping method based on hybrid quantum genetic algorithm in this study. This new method adopts communication weight of task node and structure characteristics of mapping platform to make priority compartment for task node. According to the priority and its connections, it gets better initial mapping solutions set. In solving process, combining adaptive rotating angle adjustment strategy, quantum bit crossover mutation operation and group catastrophe idea, it proposes a new hybrid quantum genetic algorithm. It adds roulette, optimal neighborhood selection and evolution reversal operation into genetic algorithm. What’s more, it chooses a initial solution with a certain probability in each iteration to prevent stagnation of algorithm. Results: Finally, the experiments show that hybrid quantum genetic algorithm greatly reduces the power consumption than traditional genetic algorithm and random mapping methods when used for Network-on-chip in the same task model and mapping platform. Conclusion: The hybrid quantum genetic algorithm is an effective method for Network-on-chip low-power mapping, which can effectively improve the accuracy of Network-on-chip.