Quantum-Driven Geometric Transformations for Data Compression

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum-driven geometric transformations represent a novel approach to data compression, capitalizing on the fundamental properties of quantum mechanics. This paper explores the design of a quantum algorithm, termed the "Geometric Resonance Algorithm" (GRA), to achieve unprecedented compression rates by exploiting the inherent geometric structure of data. We detail the core mechanisms underlying GRA, emphasizing the role of quantum entanglement and superposition in creating a 'quantum blueprint' of the data, enabling faster and more efficient data representation. This work investigates the potential for exponential compression gains through the manipulation of fundamental quantum phenomena. The proposed algorithm's theoretical foundation and experimental investigation will demonstrate the feasibility of quantum compression with significant advantages over classical methods.

DOI: 10.5281/zenodo.22156756

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