Self-Adaptive Quantum Computing Statistical Analysis

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum computing holds immense promise for scientific discovery and technological advancement, but its inherent complexity presents significant challenges for statistical analysis. Traditional approaches often rely on classical computational methods, limiting the ability to effectively characterize and understand quantum states. This paper introduces a novel framework for statistical analysis specifically tailored to the unique properties of quantum systems, leveraging non-locality to enhance accuracy and reliability. We propose a system that dynamically adjusts the quantum state to optimize statistical measures, ultimately leading to improved quantum algorithm performance. The core mechanism centers around exploiting non-local correlations to provide a more robust and insightful statistical perspective. This work aims to address the limitations of classical statistical analysis in the context of quantum systems, paving the way for more accurate and reliable quantum computations.

DOI: 10.5281/zenodo.22243722

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