Distributed Computing Optimization with Quantum Entanglement

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper investigates the potential of quantum entanglement to revolutionize distributed computing optimization. Traditional approaches often rely on classical computation, limiting scalability and potential for efficient solutions. We propose a novel algorithm, leveraging quantum entanglement, designed to identify global optima more effectively. The core mechanism centers around creating entangled quantum states to represent the search space, allowing for parallel exploration and enhanced sensitivity to subtle variations in the solution. We analyze the algorithm's performance in a simulated environment, demonstrating its ability to outperform existing methods in specific test cases. The research explores the theoretical implications of entanglement for optimization, aiming to establish a foundation for future advancements in quantum-enhanced computation.

DOI: 10.5281/zenodo.22519093

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