Title: Quantum Topology-Guided Adaptive Geometric Optimization Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum topology-guided adaptive geometric optimization algorithm designed to tackle complex environments and find optimal geometric configurations. Traditional optimization methods often struggle with high-dimensional spaces and intricate constraints. We explore the application of quantum topology, specifically through the concept of topological maps, to enhance the search process and guide the optimization towards more globally optimal solutions. The algorithm's core mechanism centers on dynamically adapting the optimization strategy based on the inherent topological structure of the problem space, thereby overcoming limitations of conventional approaches. This work proposes a framework for efficient and robust geometric optimization, offering a significant advancement over existing techniques.

DOI: 10.5281/zenodo.22201636

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