Non-Linear Geometric Transformations: A Quantum Optimization Approach

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates a novel quantum optimization algorithm leveraging the unique properties of quantum entanglement to enhance geometric transformation optimization. Traditional optimization methods often struggle with the computational complexity of large-scale geometric problems. This work proposes a framework that utilizes quantum entanglement to dynamically adjust the transformation parameters, potentially mitigating these limitations. We present a theoretical foundation for this approach, detailing the underlying mechanisms and illustrating its potential for improved efficiency. The core claim centers on the demonstration of automated optimization through the application of quantum entanglement, aiming to surpass the performance of existing classical optimization techniques. This research explores the practical implications of quantum entanglement in geometric transformations, presenting a viable pathway towards more efficient and scalable optimization processes.

DOI: 10.5281/zenodo.22267938

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