Title: Quantum-Enhanced Gradient Descent for Optimization Problems in Complex Dynamical Systems

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper investigates the application of quantum-enhanced gradient descent (QED) to optimize complex dynamical systems, specifically those characterized by non-linear interactions. Traditional optimization methods often struggle with the inherent complexity of these systems, requiring extensive computational resources and time. We propose a hybrid quantum-classical approach, utilizing a quantum annealer to initiate exploration and a classical optimization step to refine the solution. The core mechanism involves leveraging quantum properties like superposition and entanglement to accelerate convergence and enhance the algorithm's ability to navigate high-dimensional solution spaces. The research focuses on demonstrating the efficacy of this hybrid method through a detailed analysis of a challenging, multi-dimensional dynamical system model. The results demonstrate a significant improvement in convergence speed and solution quality compared to classical optimization techniques.

DOI: 10.5281/zenodo.22602031

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