Self-Adaptive Topological Information Theory for Quantum Acceleration Computing

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the potential of self-adaptive topological information theory to revolutionize quantum acceleration computation. Traditional quantum algorithms often rely on manually tuned parameters, limiting their adaptability. We propose a novel approach that leverages the inherent topological structure of quantum systems to optimize model parameters for enhanced machine learning and data compression. The core mechanism involves dynamically adjusting quantum system parameters based on topological analysis, offering a potential pathway to significantly improve efficiency. The paper details the theoretical framework, presents a preliminary implementation, and discusses future research directions. The key claim is that this integration of self-adaptive topological information theory can unlock substantial gains in both machine learning and data compression through the utilization of quantum acceleration.

DOI: 10.5281/zenodo.22202542

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