  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
($\chi^2$-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.
