Quantum Machine Learning: A Geometric Expansion

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) represents a paradigm shift in computational science, leveraging quantum mechanics to unlock new possibilities across diverse fields. This paper explores the development of a novel geometric expansion technique, utilizing quantum superposition and entanglement to dramatically enhance the capabilities of quantum computation. We propose a framework where geometric calculations are intrinsically linked to quantum algorithms, allowing for the simulation of complex geometric structures and quantum systems with unprecedented efficiency. The core claim is that this geometric expansion can overcome the limitations of classical computation, opening avenues for advanced modeling and analysis in areas like materials science, drug discovery, and fundamental physics. This work details the underlying mechanisms, explores the potential applications, and provides a preliminary assessment of the technological feasibility of this approach.

DOI: 10.5281/zenodo.22516379

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