Quantum Machine Learning: Adaptive Parameter Adjustment Strategies

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers the potential to revolutionize artificial intelligence by leveraging quantum phenomena. This paper explores the application of QML to adaptive parameter adjustment strategies, a critical component for improving model performance and stability. We propose a novel quantum neural network architecture that dynamically adjusts model parameters based on inherent quantum properties, allowing for automated optimization and enhanced resilience. The core mechanism centers around exploiting quantum entanglement and tunneling to facilitate parameter adjustments without explicit algorithmic manipulation. We analyze the potential benefits of this approach, highlighting its advantages over traditional methods and outlining future research directions. The paper concludes with a discussion of the theoretical foundations and preliminary results demonstrating the efficacy of this adaptive parameter adjustment strategy.

DOI: 10.5281/zenodo.22327350

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