Quantum Mirage: A Dynamic Parameter Adjustment Algorithm for Complex Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Mirage is a novel algorithm designed to dynamically adjust the parameters of a quantum machine learning model, offering a significant enhancement in optimization performance, particularly for complex problems. The core of this approach lies in leveraging the inherent dynamic properties of quantum mechanics, specifically the concept of quantum annealing, to automatically refine the model's parameters. This dynamic adjustment mechanism allows the algorithm to effectively navigate through the parameter space, leading to superior solutions compared to traditional static parameter optimization techniques. This paper introduces a detailed explanation of the algorithm's architecture, explores its performance evaluations, and discusses its potential applications across diverse optimization domains. The focus is on demonstrating the effectiveness of the Quantum Mirage in tackling intricate problems, showcasing its robustness and efficiency.

DOI: 10.5281/zenodo.22255110

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