Nonparametric Regression Quantum Neural Networks

Do Ngoc Diep; Koji Nagata; Tadao Nakamura

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arXiv · 2002.02818

Nonparametric Regression Quantum Neural Networks

Do Ngoc Diep·Koji Nagata·Tadao Nakamura

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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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Do Ngoc Diep, Koji Nagata, Tadao Nakamura. 2020-02-07. Nonparametric Regression Quantum Neural Networks. https://arxiv.org/abs/2002.02818

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