量子计算在增量式大数据并行挖掘中的应用

Application of Quantum Computing in Incremental Parallel Mining of Large Data

针对传统大数据并行挖掘方法是一次性对所有数据进行挖掘,导致挖掘时间较长,挖掘精度较低等问题,采用量子计算对增量式大数据并行挖掘方法进行优化设计。首先,按照数据挖掘的基本流程搭建并行数据挖掘模型;然后分别通过定义量子比特、量子搜索算法、量子神经网络处理以及量子映射变换4个步骤,实现增量式数据的预处理,利用矩阵向量相乘分解得到过滤权重组合,通过该组合实现预处理结果的并行协同过滤;最后通过量子模糊聚类得出增量式大数据并行挖掘结果。实验结果表明,应用量子计算的增量式大数据并行挖掘方法的平均召回率为97.25%,并行挖掘时间在2.1~3.2 s的范围内浮动,准确率超过95%,且该方法的收敛性最好,寻优能力强。

In view of the problem that the traditional big data parallel mining method always mines all the data at one time,resulting in a long mining time and low mining accuracy,the quantum computing is adopted to optimize the incremental big data parallel mining method.Firstly,the parallel data mining model is built according to the basic process of data mining.Then on the mining model respectively by defining a quantum bit,quantum search algorithm,quantum neural network processing and mapping transformation,the incremental data preprocessing,filtering weights are obtained by decomposition of matrix-vector multiplication,preprocessing results by using the combination of parallel collaborative filtering.Finally,by quantum fuzzy clustering,large incremental data parallel mining results are obtained.The experimental results show that the average recall rate of the incremental big data parallel mining method using quantum computing is 97.25%,the parallel mining time is within the range of 2.1~3.2 s,and the accuracy rate is always above 95%.And this method has the best convergence and strong optimization ability.