利用量子遗传算法实现RNA二级结构预测

由于基于最小自由能模型的传统算法复杂度高且搜索效率低，故利用量子遗传算法提出了一种新的核糖核酸二级结构的预测算法．该算法将种群信息加载到量子比特上完成初始化，通过量子酉变换(量子逻辑门)实现种群的更新与演化，借助于量子计算的并行性优势使得核糖核酸二级结构预测所需种群规模相对经典遗传算法大为减少，同时还具有更强的搜索预测能力．基于国际核糖核酸标准数据库提供的序列进行了量子模拟实验计算，结果表明，在种群规模为经典遗传算法20%的条件下，该算法预测准确率仍优于经典遗传算法，且所需的进化轮数也得到了明显降低．

RNA secondary structure prediction using the quantum genetic algorithm

Due to the high complexity and low search efficiency of the traditional algorithm based on the minimum free energy model, a quantum genetic algorithm for RNA secondary structure prediction is proposed, where the group is initialized by coding with qubits and the corresponding evolutions are accomplished by quantum unitary operations (i.e., quantum gates). By using this strategy, the scale of the required groups is reduced significantly due to the parallelism of quantum computing which also leads to a more powerful searching ability compared with the classical genetic algorithm. Based on the sequences offered by RNA STRAND database, the algorithm was tested by quantum simulations. Numerical results show that, for even only 20% of groups exploited with respect to the classical genetic algorithm, the prediction accuracy yielded by this strategy is still superior to that of the classical one, and that the number of evolution rounds is also obviously reduced by using this algorithm.