Quantum-Enhanced Gradient Descent for Machine Learning

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the integration of quantum mechanics into gradient descent algorithms for machine learning. We propose a novel approach that leverages quantum tunneling to accelerate convergence and utilizes quantum-enhanced sampling to broaden the search space, ultimately aiming to improve the training speed and robustness of machine learning models. The core mechanism involves employing quantum tunneling to guide the gradient updates, while simultaneously utilizing quantum-enhanced sampling techniques to navigate the complex landscape of the search space. This research offers a potential paradigm shift in the training process, potentially unlocking significant advancements in machine learning.

DOI: 10.5281/zenodo.22264693

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