Dynamic Matrix-Based Adaptive Quantum Machine Learning

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum machine learning termed "Dynamic Matrix-Based Adaptive Quantum Machine Learning" (DMM-AQML). We propose a framework that leverages dynamic matrix computations and updates to emulate complex data and facilitate learning and prediction. The core innovation lies in the integration of quantum mechanics principles, specifically the inherent noise and dynamic nature of quantum systems, into the learning process. This allows for the modeling of intricate patterns and the generation of optimized probability outcomes. The proposed DMM-AQML offers advantages over traditional machine learning algorithms, particularly in handling high-dimensional data and complex non-linear relationships. We present a theoretical framework and preliminary experimental results demonstrating the framework's effectiveness in a simulated scenario. The paper concludes with a discussion of future research directions and potential applications.

DOI: 10.5281/zenodo.22446480

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