Adaptive Topological Transformation Algorithm for Quantum Algorithms

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel adaptive topological transformation algorithm designed to enhance geometric modeling and simulation capabilities. Traditional topological transformations often rely on manual parameter adjustment, limiting flexibility and precision. We propose a method leveraging VAEs to dynamically adjust transformation parameters based on input data, enabling more accurate and refined geometric representations. The core mechanism centers around a self-learning neural network trained to extract and adjust key topological features from the data. This approach addresses the limitations of existing algorithms, offering a robust and automated solution for complex geometric tasks. We demonstrate the effectiveness of this algorithm through a series of simulations, showcasing improved accuracy and efficiency in modeling intricate geometric shapes.

DOI: 10.5281/zenodo.22272056

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