Title: Dynamic Quantum Entanglement Distribution for Distributed Computing

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum entanglement is a fundamental resource for distributed quantum computing, enabling complex computations that are intractable for classical computers. However, establishing and maintaining entanglement across a network of nodes presents significant challenges due to decoherence and communication overhead. This paper explores the concept of dynamic entanglement distribution, a novel framework utilizing reinforcement learning to intelligently allocate entanglement among nodes in a distributed computing system, optimizing performance based on workload and resource constraints. We introduce a reinforcement learning agent trained to dynamically adjust entanglement distribution to minimize errors and maximize computational efficiency. The proposed approach addresses key limitations of existing entanglement distribution techniques, offering a more adaptive and intelligent solution for harnessing the power of quantum entanglement in distributed computing. This research aims to develop a practical implementation of a dynamic entanglement distribution strategy, paving the way for more efficient and robust quantum computations.

DOI: 10.5281/zenodo.22516949

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