Core Claim: Design a quantum state self-adaptation self-mapping algorithm based on an adaptive multi-dimensional space, for extracting key features from high-dimensional data and implementing effective data distribution modeling.

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum information processing has witnessed significant advancements, driven by the need to efficiently handle complex data and explore novel quantum phenomena. This research presents a novel quantum state self-adaptation self-mapping algorithm designed to address the challenges of data extraction and distribution modeling in high-dimensional spaces. The algorithm leverages an adaptive multi-dimensional space to dynamically adjust parameters, enabling the extraction of critical features and improved data distribution modeling. This approach offers a potential paradigm shift in quantum data analysis, offering enhanced efficiency and robustness compared to traditional methods. The core mechanism focuses on the iterative refinement of the adaptive space to achieve optimal feature extraction and data distribution representation. This work demonstrates the potential of this algorithm through theoretical analysis and preliminary experimental validation, showcasing its efficacy in extracting key features and modeling data distribution.

DOI: 10.5281/zenodo.22632683

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