Self-Adaptive Geometric Transformation Optimization via Quantum Computing

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm based on quantum computing designed to efficiently find optimal geometric transformations. Traditional optimization methods often struggle with complex geometric problems, while quantum computing offers the potential for significantly faster and more accurate solutions. We propose a framework that leverages quantum superposition and entanglement to accelerate the search process and enhance the precision of geometric transformations. The core mechanism involves a hybrid quantum-classical approach, utilizing quantum algorithms to explore the solution space and classical optimization techniques to refine the results. This work demonstrates the feasibility of applying quantum computing to geometric optimization, presenting a new approach with the potential to revolutionize the field. The proposed algorithm is designed to be scalable and adaptable to a wide range of geometric transformations.

DOI: 10.5281/zenodo.22627812

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