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Title: Fractal Dimensionality Reduction via Quantum Entanglement

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

Fractal dimensionality reduction is a crucial task in many scientific domains, particularly in image processing, data analysis, and computational modeling. Traditional methods often struggle with high-dimensional data, leading to information loss and computational bottlenecks. This paper introduces a novel algorithm, "Quantum Entanglement-Enhanced Dimensionality Reduction," that leverages the principles of quantum entanglement to achieve a significant reduction in dimensionality while maintaining a high degree of fidelity. We propose a technique where entangled qubits are strategically utilized to identify and isolate key features within the data, effectively filtering out irrelevant information and preserving crucial structural elements. The core mechanism centers around creating a 'quantum feature map' – a representation where the data's structure is encoded through the entanglement of qubits. This allows for a more targeted and efficient dimensionality reduction process compared to existing approaches. We demonstrate the effectiveness of this algorithm through a series of rigorous experiments on a dataset of complex geometric shapes, showcasing its superior performance and robustness. The study highlights the potential of entanglement as a transformative resource for dimensionality reduction, offering a fundamentally new perspective on this computationally intensive problem.

DOI
10.5281/zenodo.22185167

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