Title: Topological Entanglement Dynamics (TED)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel algorithmic framework for the generation of topological entanglement, utilizing a dynamically-adjusted thermodynamic process to stabilize and expand entanglement structures. Traditional approaches often rely on predefined entanglement models, limiting the potential for novel configurations and hindering the exploration of complex topological states. We introduce a "Feedback-Driven Dissipation" mechanism, inspired by non-equilibrium thermodynamics, which actively minimizes local disorder and maximizes entanglement connectivity, thereby enabling the system to self-correct and expand entanglement. The core of this design is to simulate a dynamic environmental influence on the entangled system, promoting stability and facilitating the exploration of previously inaccessible topological landscapes. This work offers a fundamentally new approach to topological entanglement, moving beyond static models and embracing a real-time, adaptive system.

DOI: 10.5281/zenodo.22199837

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