利用遗传算法对基于半经典模型的量子细胞自动机进行仿真时,通常会遇到多个极值,容易陷入局部最优。为将量子遗传算法用于量子细胞自动机仿真,对量子遗传算法进行改进,将二进制量子位改为多进制量子位,重新设计了量子旋转门的调整策略,并给出了具体实现步骤。通过对测试函数寻优和量子细胞自动机电路的仿真,结果表明,改进后的量子遗传算法平均误差低,不易陷入局部极值,收敛速度较快,适用于量子细胞自动机仿真。 When using genetic algorithm to simulate quantum cellular automaton based on semiclassical model, it often encounters multiple extremums and is easy to fall into the local optimum. In order to apply quantum genetic algorithm to quantum cellular automata simulation, the quantum genetic algorithm is improved, the binary quantum bits are changed into multi-quantum qubits, and the adjustment strategy of quantum revolving door is redesigned, and the concrete implementation steps are given. Through the optimization of the test function and the simulation of the quantum cellular automata circuit, the results show that the improved quantum genetic algorithm is low in average error, less likely to fall into the local extremum and has faster convergence rate, and is suitable for quantum cellular automaton simulation.