Title: Algorithmic Constraint Optimization for Quantum Entanglement Distribution

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement distribution across vast distances remains a significant challenge in quantum communication, demanding highly efficient and robust algorithms. This paper introduces an algorithmic constraint optimization framework for creating and maintaining entangled states, utilizing a self-optimizing approach. We propose a 'quantum blueprint' – a high-level representation of the entangled state that evolves based on feedback from local measurements, enhanced by reinforcement learning. This framework addresses a critical bottleneck – the need for continuous distribution – and introduces a fundamental shift in the approach to entanglement management. The core mechanism centers around dynamically adjusting the blueprint's parameters to minimize decoherence and maximize entanglement fidelity. This paper details the algorithm's design, implementation, and preliminary results, demonstrating its potential for significantly improving the reliability of quantum communication systems.

DOI: 10.5281/zenodo.22188238

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