一种改进的Grover量子搜索算法

经分析发现,Grover量子搜索算法及Long的改进算法均无法达到100%成功概率的搜索结果,为此在Long的改进算法基础上提出了一种新的搜索算法.它主要将相位取反替换成具有自适应调整特点的、与目标数据量和数据总量有关的相位旋转,当目标数据量为数据总量的1/2时,将数据总量扩展2倍,这样算法的搜索可以做到100%的成功概率.通过对背包问题的仿真研究表明,所提算法优于Grover算法和Long的改进算法,其求解速度快、准确率高,在带有数据误差的实际问题求解中进行相位匹配能够得到满意的效果.

Improved Grover's Quantum Searching Algorithm

The analysis shows that the Grover's algorithm and the improved algorithm of Long are hard to attain 100% success probability.So a new searching algorithm is presented based on the improved algorithm of Long.The main contributions are to replace the inverting phase with a rotation phase which has the characteristic of self-adaption related with the data total amount and the target data quantity,and expand twice as big as the total amount when the target data quantity is half of the total amount.The new algorithm can attain 100% searching success rate in any case.The simulation result of knapsack problem indicates that the new algorithm is superior to Grover's algorithm and the improved algorithm of Long,it possesses of quick speed and high accuracy in searching,and gets good effect to solve actual problems with data error for phase matching.