Dynamic Quantum Entanglement Topological Network Optimization

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper presents a novel approach to optimizing quantum entanglement topological networks for dynamic computation and data transmission. The core claim centers around the ability to dynamically adjust the physical connections within an entanglement network in real-time, surpassing the limitations of traditional distributed computing. This is achieved by leveraging the inherent fragility of entangled states and employing controlled physical perturbations to modify node connections, thereby altering the transmission pathways and resource allocation of quantum information. A key component of this system is the integration of machine learning algorithms to predict and proactively adjust the entanglement topology, adapting to fluctuating computational demands. The proposed method offers a fundamentally new paradigm for quantum computing, moving beyond static architectures towards adaptable, resource-optimized networks. The underlying mathematical framework is detailed, focusing on the manipulation of entangled states and the optimization algorithms employed. This work demonstrates a pathway towards scalable and efficient quantum computation by capitalizing on the unique properties of quantum entanglement.

DOI: 10.5281/zenodo.22152671

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