Quantum-Enhanced Gradient Descent for Optimization

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum-enhanced gradient descent (QED) is a novel optimization algorithm that leverages the principles of quantum annealing and quantum-enhanced sampling to accelerate the optimization process, particularly for high-dimensional problems. Traditional gradient descent methods often struggle to converge to optimal solutions, while the inherent quantum properties of these techniques offer the potential for exponential speedups. This paper explores the implementation of a hybrid quantum-classical algorithm that combines the strengths of both approaches to address challenges in optimization. The core mechanism focuses on intelligently adapting the gradient descent process to quantum phenomena, leading to improved convergence rates and the ability to tackle complex, high-dimensional optimization tasks. This work presents a detailed theoretical analysis and practical implementation of QED, demonstrating its effectiveness through simulations and preliminary experiments.

DOI: 10.5281/zenodo.22156180

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