Geometric Topology Optimization - Based on Quantum Computing for Geometric Constraint Solving

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel geometric topology optimization algorithm leveraging quantum computing to address complex optimization problems. Traditional optimization methods rely on manual constraint design, limiting flexibility and computational efficiency. We propose a quantum-enhanced approach that dynamically adjusts geometric constraints and utilizes quantum entanglement to explore the intricate structure of geometric topologies. This algorithm demonstrates significant improvements in optimization speed and solution quality compared to classical methods for a range of geometric constraint problems. The core mechanism involves utilizing quantum entanglement to represent and manipulate the underlying topology, enabling efficient exploration of the solution space. This research presents a fundamentally new approach to geometric optimization, potentially revolutionizing the field through enhanced computational power and algorithmic efficiency.

DOI: 10.5281/zenodo.22607852

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