Quantum Potential-Based Adaptive Machine Learning Framework

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum potential-based machine learning framework designed to dynamically adjust model parameters for optimal performance. Traditional machine learning models often employ fixed parameters, limiting their ability to adapt to complex data. We propose a quantum mechanics-driven approach to model the relationship between input features and model parameters, allowing for adaptive parameter adjustments. This framework aims to surpass the capabilities of conventional machine learning by enabling a level of optimization unattainable through traditional methods. The core mechanism utilizes quantum entanglement and superposition to represent and manipulate the input space, facilitating a sophisticated and adaptive learning process. This research presents a framework that promises to enhance model accuracy, efficiency, and robustness.

DOI: 10.5281/zenodo.22281147

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