Statistical Tests and Confidential Intervals as Thresholds for Quantum Neural Networks

2020/01/30 by Do Ngoc Diep, Diep, Do Ngoc
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.2001.11844

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arxiv created 2020/01/30 · openalex publication_date 2020/01/30 · arxiv updated 2020/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Some basic quantum neural networks were analyzed and constructed in the recent work of the author \citedndiep3, published in International Journal of Theoretical Physics (2020). In particular the Least Quare Problem (LSP) and the Linear Regression Problem (LRP) was discussed. In this second paper we continue to analyze and construct the least square quantum neural network (LS-QNN), the polynomial interpolation quantum neural network (PI-QNN), the polynomial regression quantum neural network (PR-QNN) and chi-squared quantum neural network (χ²-QNN). We use the corresponding solution or tests as the threshold for the corresponding training rules.

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