Quantum Entanglement Algorithm Optimization

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel algorithm optimization framework based on quantum entanglement. Traditional algorithm optimization methods rely on human-designed strategies, while this framework leverages the unique properties of quantum entanglement to accelerate computation and enable new algorithmic design. We propose a method for manipulating entangled states to enhance the performance of optimization algorithms, demonstrating significant improvements in efficiency and accuracy across a range of benchmark problems. The core mechanism involves the creation and utilization of entangled states to guide the search process and facilitate rapid convergence. We present a detailed theoretical analysis and experimental validation of the proposed algorithm, highlighting its potential to revolutionize the field of algorithm optimization.

DOI: 10.5281/zenodo.22325884

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