摘要
量子神经计算是传统神经计算自然演化发展的产物,它有可能成为新的信息处理技术。本文介绍了与量子神经网络相关的量子计算基础,描述了一种量子神经元模型,提出了一种量子学习算法,通过理论推导和仿真证明了算法的收敛性并给出了几种收敛特性曲线。应用量子计算方法分析了该量子神经元模型的量子逻辑运算功能,分析和实验证明单个量子神经元能实现经典神经元无法实现的XOR函数,并具有与两层前向神经网络相当的非线性映射能力。
Quantum Neural Networks (QNN) is naturally the next step in the evolution of neuro-computing system. QNN may give us unprecedented possibilities in creating new system for information processing. The fundamentals of QNN are introduced, a model for quantum neuron is described. A quantum learning algorithm is proposed and its convergence property is discussed. The computational power of the quantum neuron is explored. Numerical and graphical results show that this single quantum neuron can perform XOR function unrealizable with a classical neuron and has the same computational power as the two-layer NN, it can be used to realize nonlinear mapping.
出处
《电路与系统学报》
CSCD
2004年第4期76-80,共5页
Journal of Circuits and Systems
基金
国家自然科学基金资助项目(60272066)
江苏省教育厅科研基金资助项目(2001省19)
关键词
量子神经网络
量子神经元
量子学习
非线性映射
quantum neural networks
quantum neuron
quantum learning
nonlinear mapping
