Improvement Of Grover Quantum Search Algorithm And Its Application To Image Retrieval

To search a unordered database with a length of N using Grover algorithm, which is based on the quantum state of parallel computation, the computational complexity is O(J-N).When search database using Grover algorithm, with the increase of target number the probability of obtaining the correct results declines significantly. And when the searching target is half of the total database, the resulting probability is1/2, no matter how many times we search, and then the algorithm is invalid. Grover quantum search algorithm and its improvement are researched in this paper and applied to image retrieval. The main research works of this dissertation can be summarized as follows:First of all, the Grover quantum searching algorithm is investigated and achieved by simulation on classic computer. Some problems existing in basic Grover searching algorithm are analyzed. And improved adaptive phase rotation Grover quantum search algorithm is proposed.In addition, image retrieval based on texture features is studied. Image texture features are extracted using three methods.Finally, image retrieve based on texture feature is researched and the improved Grover quantum searching algorithm is applied to image retrieve. The simulation results show that we can effectively reduce the computational complexity in image retrieve and obtain better retrieval results when we use this algorithm.