Quantum Algorithm-Based Nonlinear Geometry Modeling

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel approach to geometric modeling utilizing quantum algorithms, specifically designed for the precise representation and optimization of complex geometric structures. Traditional methods often struggle with intricate geometries, necessitating computationally intensive approaches. We propose a framework that leverages quantum computation's unique properties – superposition, entanglement, and quantum tunneling – to achieve enhanced modeling accuracy and efficiency. The core mechanism centers around a quantum-enhanced iterative refinement process applied to geometric primitives, minimizing computational cost while maximizing geometric fidelity. This research explores the potential of quantum algorithms to address limitations in traditional geometric modeling, offering a promising pathway toward advanced geometric analysis and design. The work presents a preliminary demonstration of the method's capabilities, emphasizing the potential for significant improvements in geometric representation and optimization.

DOI: 10.5281/zenodo.22302697

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