Title: Quantum-Inspired Optimization for Non-Linear Systems

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-inspired optimization presents a novel approach to solving complex, non-linear optimization problems by incorporating quantum phenomena into the optimization process. This paper investigates the potential of utilizing quantum fluctuations to enhance the exploration of the solution space, addressing a persistent challenge in classical optimization algorithms. We present a framework that integrates quantum-like elements, specifically through the introduction of probabilistic adjustments to the gradient, to improve the robustness and efficiency of optimization in scenarios where traditional methods demonstrate limitations. The core claim is that this method can significantly improve performance, especially when dealing with highly non-linear systems, offering a promising pathway for tackling challenges across diverse fields. This work details the theoretical foundation, implementation, and preliminary results demonstrating the effectiveness of this quantum-inspired optimization strategy.

DOI: 10.5281/zenodo.22270219

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