Quantum Machine Learning Algorithm Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Machine Learning Algorithm Optimization presents a novel approach to accelerate machine learning model training and optimization through the strategic application of quantum algorithms. This research explores the potential of leveraging quantum phenomena, such as superposition and entanglement, to enhance the efficiency of computationally intensive machine learning tasks. The core mechanism focuses on designing and implementing quantum optimization algorithms specifically tailored to address the unique challenges posed by classical machine learning models. This paper outlines a framework for evaluating the efficacy of such algorithms, highlighting key theoretical foundations and preliminary experimental results demonstrating significant improvements in training speed and parameter space exploration.

DOI: 10.5281/zenodo.22296554

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