Quantum Adaptive Adaptation of Dynamic Parameter Adjustment

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel quantum adaptive learning framework leveraging the inherent self-adaptative properties of quantum states. Traditional machine learning methods often rely on fixed parameters, limiting their effectiveness, particularly in scenarios with sparse data. We propose a quantum-enhanced learning paradigm that dynamically adjusts key parameters based on quantum state fluctuations, enabling more efficient learning and improved performance in data-scarce environments. The core mechanism involves manipulating the quantum state to induce a feedback loop that optimizes the learning process. This approach offers a fundamentally different approach to parameter optimization, potentially surpassing the limitations of conventional techniques. The paper details the theoretical framework, implementation of the quantum adaptation algorithm, and preliminary experimental results demonstrating its efficacy.

DOI: 10.5281/zenodo.22265019

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