Quantum-Enhanced Adaptive Topology Geometry Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-Enhanced Adaptive Topology Geometry Optimization is a novel framework for geometry optimization that leverages quantum algorithms to dynamically adjust parameters based on input data. The core principle centers on utilizing quantum circuits to iteratively refine geometric configurations, resulting in improved efficiency and accuracy compared to traditional optimization methods. This approach offers a promising pathway for tackling complex problems that benefit significantly from sophisticated geometric manipulation. The paper details the design of a quantum-enhanced framework, including the implementation of a specific quantum algorithm, and explores its potential for enhancing optimization performance across a range of geometric modeling tasks. We present preliminary results demonstrating the framework's effectiveness in optimizing complex shapes and demonstrating improved convergence speed and accuracy.

DOI: 10.5281/zenodo.22267557

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