Title: Non-Euclidean Data Compression via Quantum Iteration

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper explores the potential of quantum data compression through a novel algorithm predicated on the principles of quantum superposition and entanglement. Traditional data compression techniques rely on sequential processing, inherently limiting efficiency. We propose a quantum-based approach that leverages these principles to achieve exponential compression of data, exploiting inherent parallelism within quantum systems. The core mechanism involves quantum algorithms designed to systematically explore the data space, identifying and exploiting redundancies and correlations that classical algorithms struggle to detect. The resulting compression method offers a fundamentally different paradigm, potentially surpassing the capabilities of existing techniques in terms of compression ratios and computational speed. This research investigates the feasibility of implementing this quantum algorithm and analyzes its potential impact on various data types. The ultimate goal is to demonstrate a practical application of quantum data compression and explore its implications for future data storage and transmission.

DOI: 10.5281/zenodo.22499336

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