Quantum-Inspired Optimization via Adaptive Kernel Design

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum-inspired optimization aims to enhance the performance of machine learning algorithms by mimicking the principles of quantum mechanics. This paper investigates a novel approach to this field, utilizing adaptive kernel design to automatically optimize kernel functions for various tasks. Traditional kernel functions often rely on assumptions that may not hold true for all data, leading to suboptimal performance. We propose a quantum-inspired algorithm that dynamically adjusts kernel parameters to better represent the data's underlying structure, resulting in improved model accuracy and generalization. The core mechanism involves exploring the space of possible kernel functions, adapting them based on data characteristics through a novel optimization strategy. This work demonstrates the effectiveness of this approach in improving machine learning outcomes across a range of datasets, highlighting the potential for quantum-inspired optimization to address limitations in traditional algorithms.

DOI: 10.5281/zenodo.22583021

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