Quantum Machine Learning Model Building

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel framework for adaptive model construction leveraging quantum machine learning algorithms. Traditional model building processes rely heavily on human design, limiting efficiency and potentially hindering the discovery of optimal models. Our proposed framework automates the model construction process, incorporating data feature analysis to iteratively refine the model's parameters and ultimately achieve superior performance. We present a foundational algorithm demonstrating the potential for significant efficiency gains and improved model quality in various machine learning tasks. The core mechanism centers around a quantum optimization loop that dynamically adjusts model parameters based on observed data characteristics, fostering a more adaptive and robust approach. We explore the theoretical implications and preliminary experimental results showcasing the framework's capabilities.

DOI: 10.5281/zenodo.22196524

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