Optimization Of Grover Quantum Simulation Algorithm And Implementation Based On Cloud Platform

In recent years,the development of quantum computers has made great progress,but it has not been introduced into practical application.Therefore,quantum simulation has become an effective way to research and analyze quantum algorithms.The memory resources and computing power required to simulate quantum algorithms on classical computers grow exponentially with the increase of qubits,which becomes a major resistance to simulate the multi-qubit quantum algorithms.In this work,the Grover’s quantum search algorithm is the main research object,and the theory and method of how to simulate efficiently Grover’s quantum search algorithm are explored.At the same time,considering the problems such as high simulation hardware requirements,small simulation scale and poor versatility of current quantum algorithm simulation,we make full use of the easy-to-obtain cloud computing platform with massive storage space and powerful parallel computing capability to design the simulation model and research the simulation optimization algorithm about Grover’s algorithm.And we verify and analyze the efficiency and expandability of the efficient simulation method proposed in this paper.In this thesis,two methods for reducing memory cost and improving storage efficiency,and one method for improving simulation efficiency are proposed for the Grover’s quantum search algorithm simulation.We analyze and verifie the simulation methods on the cloud platform.The specific research results are as follows:1.By studying the characteristics of probability amplitude,a memory-efficient simulation method of Grover’s search algorithm is proposed.Theoretical analysis and simulation results show that this method saves nearly 87.5%of the storage space compared to the uncompressed method.Under the same hardware conditions,this method can simulate at least 3 qubits more than the uncompressed method,significantly reducing the required computational nodes,and effectively reducing the memory space required for the simulation.2.Through studying the structure of the unitary operators in the Grover’s search algorithm,we propose a memory compression method of Oracle operator and phase shift unitary operator.The theoretical analysis and experimental verification show that the compression ratio of this method is1:8,which greatly reduces the storage space of the unitary operators and improves utilization of the memory space.3.Based on the compression method above for unitary operators and combining with the characteristics of unitary operations,we propose a kind of optimization algorithm about unitary operations.Theoretical analysis shows that the time complexity of the algorithm is reduced by 2~n times(n represents quantum bits).The MapReduce parallel programming model is used on the cloud platform to further improve the computation speed of the algorithm.The experimental results show that the optimization method we proposed can significantly improve the efficiency of the simulation.4.We design the concurrent cluster simulation model in single-core virtual machines and in multi-core virtual machines on the cloud platform.Seven different quantum simulation schemes based on cloud platform are proposed.We verify the efficiency of the optimization method proposed above by experiments,and evaluate the performance of the proposed simulation model.At present,the qubits of quantum algorithms simulation in the experiment of this paper is temporarily 31 qubits.In this case,the speedup ratio has increased by 2030 times.In this thesis,the optimization method of Grover’s quantum search simulation algorithm is proposed for compressing the memory space and improving the efficiency of simulation.And the efficiency of simulation method under cloud platform is analyzed and verified.The analysis shows that the simulation method proposed has great versatility and provides ideas for efficient simulation of other quantum algorithms.