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Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum optimization is a rapidly growing field with the potential to revolutionize various scientific and engineering disciplines. Traditional optimization algorithms often struggle with complex problems and limited computational resources. This paper introduces a novel adaptive quantum optimization algorithm designed to overcome these challenges. The algorithm dynamically adjusts parameters based on the specific characteristics of the quantum hardware, leading to enhanced optimization performance and improved generalization capabilities. We detail the core mechanism, exploring how quantum entanglement and superposition are leveraged to achieve this adaptive approach. This work presents a framework for leveraging quantum phenomena to significantly advance the state of quantum optimization.

DOI: 10.5281/zenodo.22485970

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