基于量子势场的自适应密度矩阵优化

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper presents a novel algorithm for density matrix optimization based on quantum fields, aiming to enhance computational efficiency and accuracy. Traditional density matrix optimization methods often rely on manual adjustments, limiting automation. This work leverages the dynamic nature of quantum fields to dynamically adjust the density matrix, optimizing the system for specific tasks. The core mechanism involves a quantum-guided optimization process that adapts to the field's changing properties. We demonstrate the effectiveness of this approach through simulations and preliminary experiments, showcasing improvements in both speed and precision compared to conventional methods. This research provides a new pathway toward more robust and adaptable density matrix optimization, potentially applicable to various scientific and engineering applications.

DOI: 10.5281/zenodo.22296712

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