Quantum-Enhanced Gradient Descent (QGD)

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-Enhanced Gradient Descent (QGD) presents a novel algorithmic approach to optimization, leveraging quantum computing to accelerate the gradient descent process. This paper explores the potential of quantum algorithms to significantly expedite convergence in machine learning and other optimization problems. We detail the core mechanism of QGD, outlining its integration of quantum parallelism and its impact on the traditional iterative nature of gradient descent. We provide a comprehensive discussion of the theoretical foundations, potential implementation challenges, and preliminary experimental results demonstrating the efficacy of QGD. The core claim is that QGD can provide a substantial speedup compared to classical gradient descent, particularly in scenarios involving complex landscapes and large datasets.

DOI: 10.5281/zenodo.22269802

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