Quantum-Guided Optimization for Non-Linear Differential Equations

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum-Guided Optimization (QGO) offers a promising approach to solving complex nonlinear differential equations, particularly those exhibiting significant curvature. This paper investigates the potential of quantum tunneling to accelerate the optimization process, addressing the limitations of classical methods in such scenarios. We present a novel QGO framework utilizing quantum entanglement and superposition to enhance the exploration of the solution space, demonstrating improved convergence speed and resilience to local minima compared to traditional optimization techniques. The core mechanism centers on leveraging quantum tunneling to facilitate a more efficient search, ultimately leading to faster and more robust solutions. The research explores the practical implications of this approach, focusing on the design of a quantum algorithm tailored for this specific problem.

DOI: 10.5281/zenodo.22485933

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