Block Search For Large Data Set Optimization Based On Grover Algorithm And Implementation On IBM Quantum Cloud Platform

With the realization and development of quantum computer,more and more quantum algorithms are created based on quantum computer.The most important part of quantum algorithms is the quantum search algorithm,which has been proved to have exponential acceleration effect on the corresponding classical algorithms.However,quantum computers are used to calculate the number of quantum bits always exists limitation,when need to deal with large data set containing more than the number of bits in the quantum processor can deal with the largest amount of data,and cannot be directly found in the whole search space search problem of the solution,in order to solve this problem,this article will do the following research:1.Through the study of quantum processor and quantum computation,quantum processors found in the application aspects of quantum search has certain limits,on the one hand is due to the quantum entanglement is difficult to control,so the quantum processor contains the number of qubits are always limited,so the practical application of quantum search algorithm,must want to consider using limited bits to solve the problem of the search the large data sets.On the other hand,due to the decoherence of the quantum system,it is necessary to consider how to reduce the computation steps and computation time when designing the quantum search algorithm to ensure the stability and high accuracy of the system.2.Starting from the search problem to be solved,the quantum algorithm of block search based on Grover algorithm is designed.Furthermore,through the analysis of the quantum search algorithm of block,it is proved that a more optimized search scheme can be obtained by performing incomplete Grover operation on each block appropriately,and the optimal scheme appears in the case that the number of iterations increases with the position of the block further back,so an optimized block search algorithm is proposed.Finally,the optimization solution and simulation are carried out in Matlab environment.The simulation results show that the optimized block search algorithm can reach a maximum optimization rate of about 6%,and the feasibility of the optimized block search algorithm is verified experimentally.3.Design quantum circuits.Considering the application of Grover algorithm in the optimization of block search algorithm,the algorithm is divided into modules,the matrix form of unitary operator in the module is derived from the operator function of the submodule,and then the appropriate quantum gate is selected from the matrix form of the unitary operator to form the quantum circuit to realize the function of the submodule.Then the quantum circuit of the submodule is combined to realize the function of Grover iteration.Finally,the quantum circuit of the iterative module is called to complete the search according to the number of iterations of the optimized block search algorithm.4.Code the quantum circuit,use the QISIKT library in Python to connect the interface of IBM Q,and realize the optimized block search algorithm to run successfully on the quantum cloud platform of IBM Q.The program running time of optimized block search was recorded and compared with the whole Grover search.Then,the code running time and algorithm iteration times of the optimized block search algorithm were respectively used to calculate the optimization rate and analyze the performance of the algorithm.By comparing the optimization rate,it was found that the two trends were the same,indicating that the results met the theoretical expectation.