Dynamic Quantum System Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel dynamic quantum system optimization framework designed to dynamically adjust quantum system parameters in real-time, optimizing quantum systems for enhanced efficiency and improved results. Traditional optimization methods often rely on static parameter settings, limiting adaptability in dynamic environments. We propose a system-state-driven optimization approach utilizing machine learning to automatically tune quantum system parameters, incorporating continuous monitoring and feedback mechanisms. This framework addresses the limitations of static optimization, offering a robust and adaptable solution for complex quantum system design and control. The core mechanism leverages the inherent dynamics of the quantum system itself to guide parameter adjustments, leading to significant improvements in performance. This research presents a practical implementation and analyzes the benefits of this dynamic approach.

DOI: 10.5281/zenodo.22301819

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