基于自适应拓扑的量子机器学习验证

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel quantum machine learning verification framework based on adaptive topology. Quantum computing offers unique capabilities, including the ability to represent and manipulate geometric properties, which can be leveraged to validate the stability of machine learning models. We propose a method that utilizes quantum algorithms to assess the robustness of training data and model parameters, ultimately contributing to a deeper understanding of quantum machine learning's potential. The framework focuses on leveraging the inherent properties of quantum geometry to detect subtle shifts in the model's behavior, providing a more rigorous and potentially more efficient verification process than traditional methods. The core mechanism involves applying quantum transformations to data representations and model parameters to identify patterns indicative of instability. This paper explores the theoretical foundations and practical implementation of this framework, highlighting its advantages and potential impact on the field of quantum machine learning.

DOI: 10.5281/zenodo.22295642

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