Quantum-Inspired Optimization with Adaptive Kernel Functions

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm, Quantum-Inspired Optimization with Adaptive Kernel Functions, designed to enhance the efficiency of complex optimization problems. We leverage quantum-inspired kernel functions to guide the evolution of the optimization process, fostering faster convergence and improving solution quality. The algorithm's core mechanism centers around dynamically adapting kernel parameters to navigate the problem space, mimicking the principles of quantum mechanics. This adaptation is achieved through a self-tuning process that continuously evaluates the impact of each kernel parameter on the optimization landscape. The paper explores the potential benefits of this approach, particularly in scenarios involving high dimensionality and complex non-convex functions, and presents preliminary results demonstrating improved performance compared to traditional optimization methods.

DOI: 10.5281/zenodo.22266863

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