Quantum Computing Based Geometric Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Computing Based Geometric Optimization explores the potential of quantum algorithms to revolutionize optimization problems, specifically focusing on achieving enhanced efficiency compared to classical methods. This paper delves into the theoretical underpinnings and potential practical applications of this approach, emphasizing the unique capabilities offered by quantum computation in manipulating geometric data and solving complex optimization challenges. We present a framework for designing quantum-enhanced optimization algorithms, highlighting the key mathematical concepts and potential benefits across diverse domains. The core claim of this research centers on the utilization of quantum mechanics to significantly accelerate optimization processes, leading to improvements in computational speed and solution quality. The paper will outline the fundamental principles of quantum geometric optimization, detailing the advantages over conventional algorithms and exploring specific implementation strategies.

DOI: 10.5281/zenodo.22280533

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