摘要
为了高效处理大数据所具有的复杂性和不确定问题,将“不确定性问题+直觉模糊集理论+量子计算”交叉融合,构建基于直觉模糊集理论的量子模糊信息管理数学模型。为了验证该模型的可行性、合理性和有效性,设计了不确定性环境下基于参数化量子线路的量子模糊神经网络仿真实验。实验结果表明,基于该模型的量子模糊神经网络模型能更客观、准确、全面地反映不确定性问题中各对象所蕴含的知识信息,从而提高算法处理大数据的准确性。
In order to efficiently deal with the complexity and uncertainty of big data,this paper integrates“uncertainty problem+intuitionistic fuzzy set theory+quantum computing”,to build a quantum fuzzy information management mathematical model based on intuitionistic fuzzy set theory.To verify the feasibility,rationality and validity of this model,a simulation experiment of quantum fuzzy neural network based on parameterized quantum circuit is designed under uncertainty environment.The experimental results show that the quantum fuzzy neural network based on this model can more objectively,accurately and comprehensively reflect the knowledge information contained in each object in the uncertainty problem,and improve the accuracy of the algorithm processing big data.
作者
张仕斌
黄晨猗
李晓瑜
郑方聪
李闯
刘兆林
杨咏熹
ZHANG Shibin;HUANG Chenyi;LI Xiaoyu;ZHENG Fangcong;LI Chuang;LIU Zhaolin;and YANG Yongxi(School of Cybersecurity(Xin Gu Industrial College),Chengdu University of Information Technology,Chengdu 610225,China;Advanced Cryptography and System Security Key Laboratory of Sichuan Province,Chengdu 610225,China;Kashi Research Institute of Electronic Information Industry Technology,Kashi 844199,China;School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China)
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2024年第2期284-290,共7页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(62076042)
国家重点研发计划“网络空间安全治理”重点专项课题(2022YFB3103103)
四川省重点研发计划(2022YFS0571)
成都市重点研发项目(2023-XT00-00002-GX)。
关键词
大数据
量子计算
直觉模糊集理论
量子模型信息管理
量子模糊神经网络
big data
quantum computing
intuitionistic fuzzy set theory
quantum model information management
quantum fuzzy neural networks