Self-Adaptive Quantum Machine Learning: Topological Structure Learning

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel self-adaptive quantum machine learning framework based on quantum potential fields and topological structures. We propose a method for learning complex machine learning models by dynamically optimizing the quantum system's parameters through the interplay of these structural elements. The framework aims to enhance model performance and generalization capabilities by leveraging the unique properties of quantum mechanics. This work addresses the challenges of training deep, complex models, particularly in scenarios requiring high fidelity and robustness. The core mechanism focuses on creating and manipulating quantum states to iteratively refine the model's parameters, ultimately leading to improved results. We demonstrate the effectiveness of this approach through theoretical analysis and preliminary experimental results on a subset of benchmark datasets.

DOI: 10.5281/zenodo.22292391

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