Abstract: 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．

Key words: quantum computing, incremental, big data, parallel mining