Quantum Machine Learning Adaptive Optimizer

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) presents a paradigm shift in computational paradigms, offering the potential for breakthroughs in various fields, including machine learning. This paper explores the application of Quantum Neural Networks (QNNs) to a novel adaptive optimization framework, termed "Quantum Adaptive Optimizer" (QAO). We detail the fundamental principles of QAO, emphasizing its reliance on quantum error correction and quantum entanglement to enhance the optimization process. The core mechanism centers around leveraging QNNs' inherent ability to represent and manipulate complex data through quantum algorithms, allowing for automatic adaptation to evolving problem landscapes. This research investigates the potential of QAO to overcome limitations of classical optimization techniques, particularly in scenarios demanding high precision and resilience in dynamic environments. The paper concludes with a discussion of the challenges and future directions of this innovative approach.

DOI: 10.5281/zenodo.22520850

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