Quantum Adaptive Geometry Search Algorithm

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel quantum adaptive geometry search algorithm designed to efficiently explore constrained geometric spaces. The algorithm leverages quantum simulation to iteratively refine a search path, offering enhanced flexibility and potential for improved performance compared to traditional geometric search methods. We explore the core mechanisms and theoretical implications of this approach, emphasizing its potential for scalability and adaptability. The algorithm's design is grounded in the concept of quantum-enhanced exploration, aiming to find optimal solutions within defined parameters. This work introduces a framework for exploring complex geometric configurations with a focus on dynamically adjusting search parameters based on simulation results. The resulting algorithm demonstrates promising results in a simplified geometric context, highlighting the potential for wider applicability.

DOI: 10.5281/zenodo.22266698

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