Title: Quantum Gradient Descent for Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Gradient Descent for Optimization Quantum optimization is a rapidly growing field that seeks to utilize quantum mechanical phenomena to improve the efficiency of optimization algorithms. This paper investigates the application of quantum gradient descent to complex systems, proposing a hybrid approach that combines the strengths of both quantum and classical computation. We explore the potential of quantum annealing and quantum error correction to enhance gradient calculations, thereby accelerating the search for optimal solutions. The core mechanism involves leveraging quantum properties to perform gradient computations with significantly reduced computational complexity compared to traditional classical methods. This research aims to provide a novel algorithm that demonstrates substantial performance gains for challenging optimization problems, particularly those exhibiting inherent quantum behavior. The paper details the design of the algorithm, its implementation within a simulated environment, and preliminary results demonstrating improved convergence speed and solution quality. Further work will focus on refining the quantum-enhanced gradient calculation to maximize efficiency and scalability.

DOI: 10.5281/zenodo.22263202

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