Quantum Machine Learning: Adaptive Geometric Mapping

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) represents a paradigm shift in computational science, leveraging quantum mechanics to accelerate algorithms and solve previously intractable problems. This paper focuses on a novel approach within QML: Adaptive Geometric Mapping. We propose a framework that dynamically adjusts the geometric representation of a machine learning model through a self-adaptive process, exploiting the unique quantum properties of states to enhance performance. This methodology aims to overcome limitations of traditional approaches by enabling automated optimization and the exploration of complex solutions. The core mechanism centers around mapping the model's parameters and constraints into a high-dimensional space, guided by quantum algorithms, ultimately leading to improved outcomes. The paper presents a detailed exploration of this framework, including mathematical formulations and illustrative examples demonstrating its potential to surpass existing methods. We will analyze the benefits of this approach in specific machine learning tasks and discuss future research directions.

DOI: 10.5281/zenodo.22278725

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