Adaptive Topology Transformation for Quantum Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Abstract: This paper presents a novel approach to quantum optimization leveraging adaptive topology transformation. Traditional optimization methods often rely on manually calibrated parameters, limiting their adaptability to complex data sets. We introduce a dynamically adjusted topology transformation strategy utilizing an adaptive neural network to learn optimal transformation parameters, resulting in significantly improved geometric modeling and simulation accuracy. The core mechanism centers around the network's ability to autonomously adjust parameter values based on input data, allowing for a more refined geometric representation and enhanced model performance. The research demonstrates the effectiveness of this approach through a series of simulations, showcasing its superior capabilities compared to existing methods.

DOI: 10.5281/zenodo.22272087

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