计算机科学 ›› 2025, Vol. 52 ›› Issue (11A): 250200097-10.doi: 10.11896/jsjkx.250200097

• 人工智能 • 上一篇    下一篇

基于量子萤火虫算法的2QAN量子电路调度优化

李晖1,2, 王杰鹏1, 姬迎松1, 陈禹彤3   

  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
    3 北京理工大学信息与电子学院 北京 100081
  • 出版日期:2025-11-15 发布日期:2025-11-10
  • 通讯作者: 王杰鹏(ekkr18510@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(UNPYSCT2020212);哈尔滨商业大学“青年科研创新人才”培育计划(2023KYYWF0983)

2QAN Quantum Circuit Scheduling Optimization Based on Quantum Firefly Algorithm

LI Hui1,2, WANG Jiepeng1, JI Yingsong1, CHEN Yutong3   

  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
    3 School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    Natural Science Foundation of Heilongjiang Province,China(LH2024F042),University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(UNPYSCT2020212) and Cultivation Program for Young Scholars with Creative Talents of Harbin University of Commerce(2023KYYWF0983).

摘要: 针对传统量子调度策略缺乏对线路结构特征和层次需求的细致考量,优化执行过程中易产生冲突,导致并行度降低以及电路深度增加等问题,提出了量子萤火虫算法,并将其应用于2QAN量子电路调度优化。在传统萤火虫算法基础上引入量子信息,使得个体能够同时探索多个位置,增加搜索空间覆盖范围,通过波函数演化和坍缩机制,实现了对新解探索与已知解开发之间的平衡;同时引入随机扰动增强搜索多样性,利用量子隧穿效应避免陷入局部最优。通过4个基准测试函数进行测试,测试结果表明,与萤火虫算法相比,量子萤火虫算法的收敛速度提升约40%,解的质量约提升67%,搜索效率提升45%。算法通过评估不同调度方案适应度值,优化量子门操作顺序,减少电路深度和移动操作,进而提高了电路并行度。实验结果表明,在量子电路调度优化中,量子萤火虫算法相较于传统算法、2QAN电路、2HQAA算法以及LCRA与 LTSA的结合算法,SWAP门数平均减少42%,6.7%,10.4%和3%,CNOT门数平均减少15.6%,10.8%,11%和2.2%。

关键词: 量子电路, 量子门调度, 量子萤火虫算法, 波函数演化, 量子隧穿效应

Abstract: Aiming at the underconsideration of line structure characteristics and hierarchical demands of the traditional quantum scheduling strategy,and the execution will be occurred to reduce parallelism and increase the circuit depth during optimization execution process,this paper proposes the Quantum Firefly Algorithm(QFA) to apply it to 2QAN quantum circuit scheduling optimization.Quantum information is introduced to explore multiple locations simultaneously,and it increases the coverage of the search space.A balance between the exploration of the new solutions and the development of known solutions through the wave function evolution and collapse mechanism,meanwhile,the random perturbations is imported to enhance the search diversity,and the solutions will be jump out of the local optimum with quantum tunneling effect.The algorithm optimizes the order of quantum gate operations by evaluating the fitness values of different scheduling schemes to reduce the circuit depth and move operations,which in turn improves the circuit parallelism.Tests are conducted on four benchmark functions.The test results show that,compared with the firefly algorithm,the convergence speed of the quantum firefly algorithm is improved by approximately 40%,the quality of the solutions is enhanced by about 67%,and the search efficiency is increased by 45%.In the optimization of quantum circuit scheduling,compared with the traditional algorithm,the 2QAN circuit,the 2HQAA algorithm,and the combined algorithm of LCRA and LTSA,the number of SWAP gates of the quantum firefly algorithm is on average reduced by 42%,6.7%,10.4%,and 3% respectively,and the number of CNOT gates is on average decreased by 15.6%,10.8%,11%,and 2.2% respectively.

Key words: Quantum circuits, Quantum gate scheduling, Quantum firefly algorithm, Wave function evolution, Quantum tunneling

中图分类号: 

  • TP391
[1]PRESKILL J.Quantum computing in the NISQ era and beyond [J].Quantum,2018,2:79.
[2]HUANG H K,ZHANG X S.Qubit Mapping Algorithm forNoisy Intermediate-Scale Quantum Computers[J].Computer Engineering and Applications,2024,60(24):110-118.
[3]ITOKO T,IMAMICHI T.Scheduling of operations in quantum compiler[C]//2020 IEEE International Conference on Quantum Computing and Engineering(QCE).IEEE,2020:337-344.
[4]VENTURELLI D,DO M,RIEFFEL E,et al.Temporal planning for compilation of quantum approximate optimizationcircuits[C]//Scheduling and Planning Applications Workshop(SPARK).2017:58.
[5]METODI T S,THAKER D D,CROSS A W,et al.Scheduling physical operations in a quantum information proces-sor[C]//Quantum Information and Computation IV.SPIE,2006:210-221.
[6]SHI Y,LEUNG N,GOKHALE P,et al.Optimized compilationof aggregated instructions for realistic quantum computers[C]//Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems.2019:1031-1044.
[7]GUERRESCHI G G,PARK J.Gate scheduling for quantum algorithms[J].Quantum Science and Technology,2018,3(4):045003.
[8]JOHNSON M W,AMIN M H S,GILDERT S,et al.Quantum annealing with manufactured spins[J].Nature,2011,473(7346):194-198.
[9]DEVITT S J.Performing quantum computing experiments inthe cloud[J].Physical Review A,2016,94(3):032329.
[10]BARENDS R,SHABANI A,LAMATA L,et al.Digitized adiabatic quantum computing with a superconducting circuit[J].Nature,2016,534(7606):222-226.
[11]VERSLUIS R,POLETTO S,KHAMMASSI N,et al.Scalablequantum circuit and control for a superconducting surface code[J].Physical Review Applied,2017,8(3):034021.
[12]SETE E A,ZENG W J,RIGETTI C T.A functional architecture for scalable quantum computing[C]//2016 IEEE International Conference on Rebooting Computing(ICRC).IEEE,2016:1-6.
[13]GUERRESCHI G G,PARK J.Two-step approach to scheduling quantum circuits[J].Quantum Science and Technology,2018,3(4):045003.
[14]LAO L,VAN SOMEREN H,ASHRAF I,et al.Timing and resource-aware mapping of quantum circuits tosupercon ducting processors[J].IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2021,40(2):359-371.
[15]ALAM M,ASH-SAKI A,GHOSH S.Circuit compilation methodologies for quantum approximate optimization algorithm[C]//Proceedings of the 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture.2020:215-228.
[16]ALAM M,ASH-SAKI A,GHOSH S.An efficient circuit com pilation flow for quantum approximate optimization algorithm [C]//Proceedings of the 2020 57th ACM/IEEE Design Auto mation Conference,2020:1-6.
[17]LIU L,DOU X.QuCloud+:A holistic qubit mapping scheme for single/multi-programmingon 2D/3D NISQ quantum computers[J].ACM Transactions on Architecture and Code Optimization,2024,21(1):1-27.
[18]SILVA A,ZHANG X,WEBB Z,et al.Multi-qubit Lattice Surgery Scheduling[J].arXiv:2405.17688,2024.
[19]LAO L,BROWNE D E.2qan:A quantum compiler for 2 local qubit hamiltonian simulation algo rithms[C]//Proceedings of the 49th Annual International Symposium on Computer Architecture.2022:351-365.
[20]YANG X S.Firefly algorithm,stochastic test functions and de-sign optimisation[J].International journal of bio-inspired computation,2010,2(2):78-84.
[21]CHAUDHARY K.A Modified Firefly Algorithm for SolvingOptimization Problems[J].International Journal of Computational Intelligence and Applications,2024:2450012.
[22]YANG X S.Firefly algorithms for multimodal optimization[C]//Internationalsymposium on stochastic algorithms.Berlin:Springer,2009:169-178.
[23]LIU X N,AN J L,HE M,et al.Chaotic Adaptive QuantumFirefly Algorithm[J].Computer Science,2023,50(4):204-211.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!