【摘要】 针对传统量子电路Qubit映射算法受到电路结构限制和硬件耦合影响，导致全局优化效果不够显著，附加门数较多等问题，提出了一种融合量子信息的人工旅鼠算法(Quantum Information Fusing Artificial Lemming Algorithm),并将其应用于量子电路Qubit映射过程中。在传统ALA的基础上引入Bloch球面量子编码技术对种群进行扩容，增加解空间范围的同时，保证系统演化初期个体对多方向探索的可能性；设计基于量子旋转的t分布个体变异方式增强种群演化的多样性，利用量子隧穿效应避免陷入局部最优；设计了自适应搜索方向因子，并讨论全局搜索和局部开发平衡方式，以确保全局优化过程的灵活性和快速性。30个基础测试电路结果表明：相对于传统ALA,QALA附加门数减少100%。同时，在t|ket〉和Qiskit编译器测试中，相比于传统IBM基准测试方法，QALA附加的SWAP门数分别平均减少35.8%和47.8%,CNOT门数分别平均减少12.9%和13.8%,执行时间分别平均减少5.8%和6.4%,从而验证了所提算法在不同编译环境下的适用性。更多还原

【Abstract】 Aiming at the problems that the traditional Qubit mapping algorithm of quantum circuits is limited by the circuit structure and hardware coupling, resulting in insufficient global optimization effect and a large number of additional SWAP gates, this paper proposes an quantum information fusing artificial lemming algorithm(QALA) and applies the algorithm to the qubit mapping process of quantum circuits.Based on the traditional ALA,the Bloch spherical quantum coding technology is introduced to expand the population, increasing the range of the solution space while ensuring the possibility of individuals exploring in multiple directions in the early stage of system evolution.The individual variation mode of t-distribution based on quantum rotation is designed to enhance the diversity of population evolution, and the quantum tunneling effect is utilized to avoid falling into local optimum.An adaptive search direction factor is designed, and the balancing methods of global search and local development are discussed to ensure the flexibility and rapidity of the global optimization process.Results of 30 benchmark test circuits show that, compared with the traditional ALA,the additional gate number of QALA is reduced by 100%.Meanwhile, in the t|ket〉 and Qiskit compilers, compared with the traditional IBM benchmark test methods, the number of SWAP gates added by QALA is decreased by an average of 35.8% and 47.8%,the number of CNOT gates is decreased by an average of 12.9% and 13.8%,and the execution time is decreased by an average of 5.8% and 6.4% respectively.Experiments results show the applicability of the proposed algorithm in different compilation environments.更多还原