Nonparametric Regression Quantum Neural Networks

2020/02/07 by Do Ngoc Diep, Diep, Do Ngoc, Koji Nagata +3
Computer Science · Physics and Astronomy · #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Neural Networks and Applications #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph) #cs.ET #cs.LG #quant-ph

paper · pdf · doi:10.48550/arxiv.2002.02818

4 pages, no figure, LaTeX2e

arxiv created 2020/02/07 · openalex publication_date 2020/02/07 · arxiv updated 2020/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In two pervious papers \citedndiep3, \citedndiep4, 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 (χ²-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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