Quantum State Superposition Distributed Algorithm Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper proposes a novel approach to algorithm optimization leveraging the principles of quantum state superposition within a distributed computing framework. The core idea centers around decomposing complex optimization problems into a series of smaller, interconnected sub-problems, each represented by a specific quantum state superposition. Quantum entanglement and interference are then strategically employed to facilitate rapid exploration of the global optimization landscape. Unlike traditional optimization methods, this approach aims to harness the inherent parallelism and potential speedup offered by quantum mechanics. The theoretical framework outlines the key mathematical components, including the representation of quantum states, the design of entanglement protocols, and the analysis of interference effects. The paper demonstrates the potential for improved efficiency and accuracy in optimization tasks, particularly those exhibiting high dimensionality or complex dependencies. Further research will focus on developing practical implementations and exploring the scalability of this methodology.

DOI: 10.5281/zenodo.22243271

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