Title: Adaptive Topology for Data Compression – Quantum-Inspired Algorithms

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper investigates the application of adaptive topology in data compression, inspired by the principles of quantum mechanics, to achieve significantly higher compression rates than conventional methods. Traditional compression algorithms rely on static mapping and iterative compression. We propose a novel approach that dynamically adjusts the topology of data structures, identifying and exploiting patterns that maximize information density, leveraging quantum-inspired algorithms to achieve this optimization. The core mechanism involves utilizing quantum algorithms to subtly modify the underlying structure of data representations, leading to a more efficient compression process. The paper details the theoretical framework for this adaptive topology, explores the implementation challenges, and presents initial results demonstrating the potential for substantial compression gains. We argue that this approach represents a significant step toward quantum-inspired data compression, offering a pathway to unprecedented compression efficiency.

DOI: 10.5281/zenodo.22242302

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