福建电脑2024，Vol.40Issue(1)：21-26,6.DOI:10.16707/j.cnki.fjpc.2024.01.004
量子自组织特征映射神经网络
Quantum Self-Organizing Feature Mapping Neural Network Algorithm
摘要
Abstract
Self-organizing feature mapping is a typical unsupervised neural network algorithm.It adopts competitive learning strategies to achieve data classification.However,when the number of neurons in the network is polynomial the training of self-organized feature mapping algorithm will be challenged by computational power.In order to reduce the time complexity of the algorithm,a quantum-classical hybrid self-organizing feature mapping neural network model is proposed.It provides a parallel scheme for similarity calculation and label extraction by using quantum superposition and quantum entanglement during the training process of neural network.Theoretical analysis shows that the proposed algorithm has exponential acceleration in data dimension compared with the classical algorithm.关键词
量子神经网络/量子相位估计/Grover搜索算法/自组织特征映射Key words
Quantum Neural Network/Quantum Phase Estimation/Grover Search Algorithm/Self-Organizing Feature Mapping分类
计算机与自动化引用本文复制引用
叶梓..量子自组织特征映射神经网络[J].福建电脑,2024,40(1):21-26,6.基金项目
本文得到国家自然科学基金(No.62171131、No.61976053、No.61772134)、福建省自然科学基金(No.2018J01776)、福建省高等学校新世纪优秀人才支持计划资助. （No.62171131、No.61976053、No.61772134）
