Self-Adaptive Quantum Entanglement Network Topology Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel algorithm for optimizing quantum entanglement network topology, addressing the limitations of static optimization methods. We propose a dynamic approach that dynamically adjusts the distance and strength of entangled nodes to enhance quantum information transfer efficiency and security. The core mechanism centers around leveraging the inherent properties of quantum entanglement networks to iteratively refine the network's structure. This iterative process, guided by a feedback loop, allows for a highly adaptable solution compared to traditional static optimization techniques. The resulting network topology is designed to maximize the fidelity of quantum states and minimize decoherence. The algorithm incorporates a self-consistency constraint to mitigate instability, ensuring a robust and reliable network configuration. We evaluate the performance of the proposed algorithm on a simulated quantum network, demonstrating significant improvements in key metrics including entanglement fidelity and data transmission speed.

DOI: 10.5281/zenodo.22263735

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