论文标题
统计测试和机密间隔作为量子神经网络的阈值
Statistical Tests and Confidential Intervals as Thresholds for Quantum Neural Networks
论文作者
论文摘要
在《国际理论物理学杂志》（2020年）上发表的作者{dndiep3}的最新作品（2020），分析和构建了一些基本的量子神经网络。特别是讨论了最小的拼写问题（LSP）和线性回归问题（LRP）。在第二篇论文中，我们继续分析和构建最小二平方量子神经网络（LS-QNN），多项式插值量子神经网络（PI-QNN），多项式回归量子神经网络（PR-QNN）和Chi-Squared量子神经网络（$χ^2 $ -QNN）。我们使用相应的解决方案或测试作为相应培训规则的阈值。
Some basic quantum neural networks were analyzed and constructed in the recent work of the author \cite{dndiep3}, published in International Journal of Theoretical Physics (2020). In particular the Least Quare Problem (LSP) and the Linear Regression Problem (LRP) was discussed. In this second paper we continue to analyze and construct the least square quantum neural network (LS-QNN), the polynomial interpolation quantum neural network (PI-QNN), the polynomial regression quantum neural network (PR-QNN) and chi-squared quantum neural network ($χ^2$-QNN). We use the corresponding solution or tests as the threshold for the corresponding training rules.
