Quantum-Enhanced Gradient Descent for Optimization Algorithms

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum-enhanced gradient descent represents a novel approach to optimization algorithms by integrating quantum mechanics principles to accelerate convergence and enhance stability. This paper explores the implementation of a modified gradient descent method that employs quantum superposition to explore multiple solution paths concurrently, thereby reducing computational cost and improving the robustness of optimization processes. We detail the theoretical framework underpinning this approach, outlining the quantum properties leveraged and the specific algorithm architecture designed to maximize this benefit. The paper presents a detailed analysis of the performance improvements achieved through this implementation, demonstrating a significant acceleration in convergence and a demonstrable improvement in stability compared to traditional gradient descent techniques. The research focuses on the potential for harnessing quantum phenomena to overcome limitations inherent in classical optimization, offering a promising avenue for advancing machine learning and optimization workflows.

DOI: 10.5281/zenodo.22255186

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