Quantum Gradient Descent – Beyond Classical Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Gradient Descent (QGD) presents a novel approach to optimization, leveraging the inherent quantum properties of systems to achieve solutions beyond the limitations of classical methods. This paper explores the potential of QGD, focusing on the implementation of a "Quantum State Collapse" mechanism to dynamically shift and explore multiple solution spaces simultaneously. We detail the theoretical framework underpinning this approach, outlining the challenges and opportunities presented by this paradigm shift. The core claim is that QGD offers a fundamentally different path toward finding optimal solutions, potentially unlocking new capabilities in complex optimization problems across diverse fields.

DOI: 10.5281/zenodo.22263926

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