Geometric Data Compression and Acceleration via Quantum Entanglement

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Geometric data, encompassing complex geometric shapes and their associated properties, represents a significant challenge in various fields, from computer graphics and scientific visualization to data analysis and pattern recognition. Traditional data compression techniques often struggle to effectively handle the inherent complexities of geometric data, leading to limitations in compression ratios and computational efficiency. This paper explores the potential of leveraging quantum entanglement to revolutionize geometric data compression and acceleration. We introduce a novel algorithm, Quantum Geometric Compression (QGC), that utilizes entanglement to efficiently transfer and manipulate geometric data, resulting in significantly improved compression ratios and accelerated geometric computations. The core mechanism centers on exploiting the correlated properties of entangled qubits to represent and manipulate geometric data in a fundamentally different manner than conventional methods. We demonstrate the effectiveness of QGC through extensive experimental analysis, showcasing substantial compression gains and speedups in key geometric operations. The research contributes to a paradigm shift in data compression and computation, paving the way for more efficient and robust geometric processing.

DOI: 10.5281/zenodo.22221379

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