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 ($\chi^2$-QNN). We use the corresponding solution or tests as the threshold for the corresponding training rules.

中文翻译：

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统计测试和保密区间作为量子神经网络的阈值

一些基本的量子神经网络在作者 \cite{dndiep3} 的近期工作中进行了分析和构建，该工作发表在 International Journal of Theoretical Physics (2020) 上。特别讨论了最小二乘问题 (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乘量子神经网络（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）和卡方量子神经网络神经网络（$\chi^2$-QNN）。我们使用相应的解决方案或测试作为相应训练规则的阈值。