Qsimulation V2.0:An Optimized Quantum Simulator

Due to quantum effect,the development of classical computer components has reached the limit of modern physics.Thanks to quantum superposition states,quantum computers can accelerate computation remarkably compared with classical computers.Therefore,research and development of quantum computers to solve specific problems has started a worldwide craze among countries.In the past few decades,quantum computation has made continuous progress with a series of quantum algorithms proposed.However,the research of quantum computers is still in their infancy,and there is still a long way to go to develop a scalable quantum computer.Therefore,it is of great importance to simulate quantum computation on classical computers,just like circuit simulation in VLSI design.As a quantum simulator software,Qsimulation allows a user to write quantum programs,draw quantum circuts,execute the programs and view the results.Similar to many other quantum simulation tools,the performance of Qsimulation largely depends on its capacity of dealing with matrix operations.In this thesis we present Qsimulation V2.0.s,an optimized quantum simulator that implements a new algorithm for accelerating matrixvector multiplications.The algorithm is based on matrix decomposition using tensor products and suitable for simulating the execution of quantum circuits.Experimental results show that Qsimulation V2.0.s outperforms the open source frameworks Qiskit and Project Q.Since matrix multiplication is a computationally intensive task,we then propose an optimization method of multithreading parallel computing to get Qsimulation V2.0.Experimental results show that the efficiency of Qsimulation is further improved.By using a more intuitive idea,we design a heuristic algorithm to perform quantum simulation.The algorithm stores the quantum superposition states in key-value pairs,and changes the states directly when they evolve.Experimental results show that the algorithm can simulate more quantum bits,and the execution efficiency is higher when the number of superposition states is small.