量子模糊信息管理数学模型研究

Research on Mathematical Model of Quantum Fuzzy Information Management

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.