计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (6A): 250400088-7.doi: 10.11896/jsjkx.250400088

• 人工智能 • 上一篇    下一篇

多策略融合小龙虾优化算法在量子电路调度中的应用

李晖1,2, 鞠明媚1, 王杰鹏1, 姬迎松1   

  1. 1 哈尔滨商业大学计算机与信息工程学院 哈尔滨 150028
    2 黑龙江省电子商务与信息处理重点实验室 哈尔滨 150028
  • 出版日期:2026-06-16 发布日期:2026-06-12
  • 通讯作者: 鞠明媚(jumingmei66@163.com)
  • 作者简介:(hrbcu_lh@163.com)
  • 基金资助:
    黑龙江省自然科学基金(LH2024F042);黑龙江省普通本科高等学校青年创新人才培养计划(UNPYSCT-2020212);哈尔滨商业大学“青年科研创新人才”培育计划(2023-KYYWF-0983)

Application of Multi-strategy Fusion Crayfish Optimization Algorithm in Quantum CircuitScheduling

LI Hui1,2, JU Mingmei1, WANG Jiepeng1, JI Yingsong1   

  1. 1 School of Computer and Information Engineering,Harbin University of Commerce,Harbin 150028,China
    2 Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing,Harbin 150028,China
  • Published:2026-06-16 Online:2026-06-12
  • About author:LI Hui,born in 1985,Ph.D,professor,master's.supervisor,is a member of CCF(No.K9013M).His main research interests include quantum computing and quantum information processing,etc.
    JU Mingmei,born in 2000,postgra-duate.Her main research interest is quantum circuit optimization.
  • Supported by:
    Natural Science Foundation in Heilongjiang Province of China(LH2024F042),University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(UNPYSCT-2020212) and Science Foundation of Harbin Commerce University(2023-KYYWF-0983).

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摘要： 随着量子计算技术的快速发展,量子电路运行时间和额外门插入成本成为实现高效量子电路调度的主要挑战。为此,文中提出了多策略融合的小龙虾优化算法MPF-COA(Multi-strategy fusion-COA)。通过自定义优化初始种群并结合基于熵的自适应机制和信息素传递机制两个策略,实现了算法效率和精度的优化,并显著提升了复杂约束下量子电路调度的优化效率。初始种群的优化采用基于依赖关系的SWAP门插入策略DBSI(Dependency-Based SWAP Insertion Strategy),确保了更高质量的起始解。在此基础上,文中提出了基于熵的自适应温度调节机制,避免陷入局部最优解。信息素传递机制通过引导搜索方向,有效提高了全局最优解的搜索效率。实验验证采用2QAN量子计算框架,在包含4~22个量子比特规模的基准测试集上进行性能评估。结果表明,相较于2QAN,MPF-COA在t|ket〉中平均减少约3.2%的SWAP门数,CNOT门数量减少约4.69%,在Qiskit中平均减少约10.68%的SWAP门数,CNOT门数量减少约11.89%。文中展示了仿生算法与量子电路调度的深度融合潜力,为未来面向更大规模量子电路的调度与优化提供了可持续的研究基础。

关键词: 量子电路调度, 小龙虾优化算法, 多策略融合, 熵自适应, 量子比特依赖

Abstract: With the rapid development of quantum computing technology,the running time of quantum circuits and the cost of additional gate insertions have become the main challenges in achieving efficient quantum circuit scheduling.To address these,this paper proposes a multi-strategy fusion crayfish optimization algorithm(MPF-COA).By customizing the initial population and combining the two strategies of entropy-based adaptive mechanism and pheromone transmission mechanism,the efficiency and accuracy of the algorithm are optimized,and the optimization efficiency of quantum circuit scheduling under complex constraints is significantly improved.The optimization of the initial population employs dependency-based SWAP insertion strategy(DBSI),ensuring a higher-quality starting solution.On this basis,the entropy-based adaptive temperature adjustment mechanism avoids getting trapped in local optimal solutions.The pheromone transmission mechanism effectively improves the search efficiency for the global optimal solution by guiding the search direction.The performance evaluation is conducted using the 2QAN quantum computing framework on a benchmark test set with qubit scales ranging from 4 to 22.The results show that compared with 2QAN,MPF-COA reduces the average number of SWAP gates by approximately 3.2% in t|ket〉 and by approximately 10.68% in Qiskit,and reduces the number of CNOT gates by approximately 4.69% and 11.89%,respectively.This study demonstrates the potential for the deep integration of bionic algorithms and quantum circuit scheduling,providing a sustainable research foundation for future scheduling and optimization of larger-scale quantum circuits.

Key words: Quantum circuit scheduling, Crayfish optimization algorithm, Multi-strategy fusion, Entropy adaptive, Qubit dependence

中图分类号: 

  • TP391
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