Quantum-Inspired Optimization via Non-Linear Gradient Descent

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired optimization presents a novel approach to problem-solving, leveraging the principles of quantum mechanics to guide the search process. This paper proposes a new optimization algorithm, "Non-Linear Gradient Descent with Quantum Properties," designed to overcome the limitations of classical gradient descent in complex, non-linear landscapes. We introduce a modified update rule that mimics quantum properties, specifically the superposition and entanglement of gradients, to accelerate exploration and improve convergence. The algorithm's effectiveness is demonstrated through a series of benchmark problems, showcasing enhanced performance compared to established methods. This work establishes a foundation for a more efficient and adaptable optimization technique applicable to a wide range of computational challenges.

DOI: 10.5281/zenodo.22193395

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