Quantum Informatics View of Statistical Data Processing
Yu. I. Bogdanov, N. A. Bogdanova
TL;DR
This paper demonstrates how quantum-inspired root density estimators can be applied to statistical data analysis, using various polynomial basis functions for reconstructing distributions from experimental data.
Contribution
It introduces the application of root density estimators with multiple polynomial bases for improved statistical distribution reconstruction.
Findings
Effective reconstruction of distributions demonstrated through numerical modeling.
Multiple polynomial bases can be used for flexible data analysis.
Quantum-inspired methods enhance statistical data processing.
Abstract
Application of root density estimator to problems of statistical data analysis is demonstrated. Four sets of basis functions based on Chebyshev-Hermite, Laguerre, Kravchuk and Charlier polynomials are considered. The sets may be used for numerical analysis in problems of reconstructing statistical distributions by experimental data. Examples of numerical modeling are given.
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Quantum Mechanics and Applications