Dynamic Quantum Entanglement Networks (DQN)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel dynamic quantum entanglement network (DQN) framework designed to optimize quantum computation by dynamically adjusting the scale and structure of entangled qubits. Traditional quantum computing architectures often rely on static, inflexible arrangements, limiting their adaptability to varying computational demands. DQN proposes a self-adaptive system that facilitates efficient utilization of quantum resources through intelligent network reconfiguration. The core mechanism involves adjusting entanglement parameters based on real-time analysis of computational requirements. This adaptive approach significantly enhances the performance and scalability of quantum algorithms. The paper explores the design and implementation of DQN, detailing its key components, the underlying mathematical principles, and the resulting improvements in quantum entanglement management. The benefits of this framework are demonstrated through a simulation, showcasing its ability to maintain coherence and achieve higher fidelity for a range of quantum circuits.

DOI: 10.5281/zenodo.22204546

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