Nonparametric Regression Quantum Neural Networks

• URL: http://arxiv.org/abs/2002.02818v1
• Date: Fri, 7 Feb 2020 14:44:45 GMT
• Title: Nonparametric Regression Quantum Neural Networks
• Authors: Do Ngoc Diep, Koji Nagata, and Tadao Nakamura
• Abstract summary: In this paper we analyze and implement the nonparametric quantum neural networks (LNR-QNN), non regression quantum neural networks (PNR-QNN) The implementation is constructed through the Gauss-Jordan Elimination neural networks (GJE-QNN)
• Score: 0.11470070927586014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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