Title: Topological Entanglement Dynamics for Accelerated Computation

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the potential of topological entanglement dynamics to dramatically accelerate computation. Traditional approaches struggle with the inherent limitations of static entanglement networks, hindering scalability. We propose a novel algorithm centered around dynamically generating and stabilizing topological entanglement networks, driven by a feedback loop that adapts to network topology evolution. This mechanism aims to overcome these limitations and offer a paradigm shift towards exponential speedups in specific computational tasks. The core of our approach lies in the incorporation of adaptive entanglement generation and stabilization techniques, resulting in a continuously evolving, self-stabilizing structure. We detail the algorithm's design, demonstrate its effectiveness through theoretical analysis, and present preliminary results on a simplified example demonstrating potential speedups. The insights gained from this research contribute to a deeper understanding of topological entanglement networks and their application to accelerating computation.

DOI: 10.5281/zenodo.22281024

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