A Model Of Granular Computing Theory And Application Based On Quantum Computing Algorithms

With the development of computer network technology and widely available，Amount of data is growing at an explosive trend growth, the complexity of the datais also more diverse.To obtain valuable information from the vast amounts of datahave become very difficult.that presents a challenge to bring certain data miningtechnique and granular computing is a new data mining technology, it is mainlyapplied to data sets of attribute reduction.Based on quantum computing, granular computing, machine learning and datamining theory, this paper proposes a model of granular computing based on quantumcomputing, it is also at the micro perspective of granular computing theory importantsupplement and perfection, for some of the problems difficult to understand withtraditional technology provides another useful way of thinking, it is mainly used theparallelism of quantum computing, exponential storage capacity and exponentialacceleration characteristics, expressed in solving some problems of huge operationeffect, at the same time, there are more and more scientists are beginning to interestedin a wide range of quantum computing.The main contents of this study are as follows:(1) An improved algorithm of Grover quantum search algorithm. Groveralgorithm for a number of shortcomings in the classical target number of solutions tosolve more problems in the search space, this paper presents an approach based on π/3phase rotation improved quantum search algorithm is very effective and ingenioussolution when the target Compare the number of solutions for a long time the basicGrover algorithm search success probability is0or algorithm failure problem, theprobability of success of its search has also been a considerable extent.(2) A model of granular proposed based on granular computing model ofquantum computing. Studied a lot about concepts, theories and definitions granularcomputing model of quantum information Reap, in the practical application of thismodel are full advantage of the parallelism of quantum computing, diversity and stratification characteristics of granular computing.(3) The use of improved data Grover algorithm acceleration Rough SetAttribute Reduction. Based on some of the problems some of the traditional attributereduction algorithms exist, this paper first presents the improved algorithm is appliedto the Rough Set Grover data attribute reduction process, the experimental resultsshow that the improved algorithm to accelerate and improve the Grover onclassification performance of massive data prove that the algorithm is feasible andeffective.