Quantum Adaptive Machine Learning Framework (QAMLF)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Adaptive Machine Learning Framework (QAMLF) is a novel machine learning framework designed to automatically adjust machine learning model parameters and optimize model performance based on data features. Traditional machine learning parameter tuning often involves manual intervention, limiting adaptability to complex data characteristics. QAMLF introduces a quantum-inspired adaptive learning mechanism that dynamically optimizes model parameters by leveraging quantum self-adaptation algorithms. This framework aims to achieve more efficient machine learning through automated parameter adjustment and enhanced performance. This paper presents a comprehensive overview of the QAMLF architecture, detailing its core mechanisms, rationale, and potential applications.

DOI: 10.5281/zenodo.22187880

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