Quantum Machine Learning: Topological Optimization for Enhanced Model Parameter Discovery

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly evolving field that leverages the principles of quantum mechanics to develop novel algorithms and computational models. This paper explores the synergistic potential of combining Quantum Machine Learning with Topological Optimization, a paradigm shift in model parameter discovery. We propose a novel approach wherein quantum algorithms are employed to identify and refine the optimal parameter configurations within machine learning models, resulting in improved generalization performance and computational efficiency. The core mechanism centers on utilizing the inherent topological properties of certain quantum systems to guide the optimization process, offering a distinct advantage over traditional methods. We detail the theoretical foundations, proposed algorithms, and preliminary results demonstrating the efficacy of this combined methodology. The paper concludes with a discussion of the challenges and future directions for this exciting research area.

DOI: 10.5281/zenodo.22158333

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