Background: The prediction of RNA secondary structure using optimization algorithms is key to understand the real structure of an RNA. Evolutionary algorithms (EAs) are popular strategies for RNA secondary structure prediction. However, compared to most state-of-the-art software based on DPAs, the performances of EAs are a bit far from satisfactory.

Objective: Therefore, a more powerful strategy is required to improve the performances of EAs when applied to the prediciton of RNA secondary structures.

Methods: The idea of quantum computing is introduced here yielding a new strategy to find all possible legal paired-bases with the constraint of minimum free energy. The sate of a stem pool with size N is encoded as a population of QGA, which is represented by N quantum bits but not classical bits. The updating of populations is accomplished by so-called quantum crossover operations, quantum mutation operations and quantum rotation operations.

Results: The numerical results show that the performances of traditional EAs are significantly improved by using QGA with regard to not only prediction accuracy and sensitivity but also complexity. Moreover, for RNA sequences with middle-short length, QGA even improves the state-of-art software based on DPAs in terms of both prediction accuracy and sensitivity.

Conclusion: This work sheds an interesting light on the applications of quantum computing on RNA structure prediction.

背景：使用优化算法预测RNA二级结构是理解RNA真实结构的关键。进化算法（EA）是RNA二级结构预测的流行策略。但是，与大多数基于DPA的最新软件相比，EA的性能差强人意。

目的：因此，在应用于RNA二级结构的前提时，需要一种更强大的策略来提高EA的性能。

方法：这里介绍了量子计算的思想，从而产生了一种新的策略，可以在最小自由能的约束下找到所有可能的合法配对碱基。大小为N的茎池的状态被编码为QGA的种群，由N个量子位表示，而不由经典位表示。通过所谓的量子交叉操作，量子突变操作和量子旋转操作来完成种群的更新。

结果：数值结果表明，通过使用QGA，传统EA的性能不仅在预测准确性和灵敏度方面，而且在复杂性方面都得到了显着提高。此外，对于中等长度的RNA序列，QGA甚至在预测准确性和灵敏度方面都改进了基于DPA的最新软件。

结论：这项工作为量子计算在RNA结构预测中的应用提供了有趣的启示。