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Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel quantum algorithm optimization approach leveraging high-Gaussian-self-relevant-graph (GSRG) principles to enhance the efficiency and accuracy of quantum computations. Traditional optimization methods often struggle with complex environments, prompting the development of a new framework centered around GSRG. Our work introduces a robust optimization strategy that dynamically adapts to the quantum system's state, resulting in improved performance metrics across various quantum algorithms. The core mechanism hinges on constructing a GSRG representation of the quantum system, employing a machine learning-guided optimization loop to refine the algorithm's parameters. We demonstrate the efficacy of this method through rigorous analysis and preliminary results on several benchmark quantum algorithms, highlighting its potential for significant advancements in quantum computing.

DOI: 10.5281/zenodo.22185045

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