Quantum Machine Learning: Adaptive Optimization Strategies

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) offers a paradigm shift in the field of artificial intelligence, leveraging the unique properties of quantum mechanics to potentially accelerate computations and unlock new algorithmic capabilities. This paper explores the application of QML to optimization, specifically focusing on adaptive optimization strategies designed to enhance model parameter tuning and improve overall performance. We present a novel approach that utilizes quantum fields to dynamically adjust optimization parameters, resulting in a more efficient and accurate optimization process. The core mechanism centers around the concept of quantum-enhanced feedback loops, allowing the system to self-adapt and refine its search parameters in real-time. This paper argues that this approach represents a significant step towards automated and highly effective model optimization, offering a promising avenue for advancing the capabilities of machine learning.

DOI: 10.5281/zenodo.22298751

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