论文标题
非参数回归量子神经网络
Nonparametric Regression Quantum Neural Networks
论文作者
论文摘要
In two pervious papers \cite{dndiep3}, \cite{dndiep4}, the first author constructed the least square quantum neural networks (LS-QNN), and ploynomial interpolation quantum neural networks ( PI-QNN), parametrico-stattistical QNN like: leanr regrassion quantum neural networks (LR-QNN),多项式回归量子神经网络（PR-QNN），卡方量子神经netowrks（$χ^2 $ -QNN）。我们观察到该方法在这种情况下也可以使用非参数统计。在本文中，我们分析并实施了对QNN的非参数测试，例如：线性非参数回归量子神经网络（LNR-QNN），多项式非参数回归量子神经网络（PNR-QNN）。实施是通过高斯 - 约旦消除量子神经网络（GJE-QNN）构建的。训练规则是使用高概率置信区域或间隔。
In two pervious papers \cite{dndiep3}, \cite{dndiep4}, the first author constructed the least square quantum neural networks (LS-QNN), and ploynomial interpolation quantum neural networks ( PI-QNN), parametrico-stattistical QNN like: leanr regrassion quantum neural networks (LR-QNN), polynomial regression quantum neural networks (PR-QNN), chi-squared quantum neural netowrks ($χ^2$-QNN). We observed that the method works also in the cases by using nonparametric statistics. In this paper we analyze and implement the nonparametric tests on QNN such as: linear nonparametric regression quantum neural networks (LNR-QNN), polynomial nonparametric regression quantum neural networks (PNR-QNN). The implementation is constructed through the Gauss-Jordan Elimination quantum neural networks (GJE-QNN).The training rule is to use the high probability confidence regions or intervals.
