Quantum Entangled Optimization: A Novel Approach to Algorithm-Driven Optimization

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum entanglement, a cornerstone of quantum mechanics, offers a unique paradigm for computation and communication. This paper investigates the application of quantum entanglement to enhance algorithm-driven optimization, specifically focusing on the creation of self-adaptive optimization strategies. We explore how incorporating quantum algorithms into optimization processes can yield significant improvements in computational efficiency. The core mechanism involves leveraging entanglement to create correlated quantum states that guide the optimization process, leading to solutions that are more robust and adaptable to varying input parameters. The research presents a novel framework centered around integrating quantum algorithms directly into the optimization loop, demonstrating a tangible improvement in performance compared to traditional optimization techniques.

DOI: 10.5281/zenodo.22222506

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