Self-Adaptive Geometric Transformations for Topological Optimization via Quantum Computing

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper investigates the application of quantum computing to enhance topological optimization, specifically focusing on automated optimization of complex topological structures and their resulting data storage and computational efficiency. We present a novel quantum-based algorithm designed to explore and refine topological landscapes, offering a pathway towards more efficient data management and high-precision numerical simulations. The core mechanism centers on leveraging quantum entanglement and superposition to facilitate the exploration of a vast space of potential topological transformations. We demonstrate the effectiveness of this algorithm through illustrative examples and analyze the potential benefits in terms of computational speed and solution quality.

DOI: 10.5281/zenodo.22221809

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