基于动态的自适应量子模拟的自适应优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum simulation offers a powerful tool for exploring complex quantum systems, but the inherent computational cost and limitations of traditional simulation methods can hinder progress. This paper proposes a novel optimization technique, leveraging dynamic self-adaptive quantum simulation, designed to mitigate these challenges. We introduce a method that dynamically adjusts simulation parameters based on the simulation's output, enabling a more refined and efficient exploration of quantum systems. The core mechanism involves iterative parameter refinement guided by the simulation's feedback, leading to improved accuracy and reduced computational time. This approach offers a promising pathway towards more robust and scalable quantum simulation.

DOI: 10.5281/zenodo.22265943

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