Quantum Phase Field Simulation: Adaptive Optimization for Enhanced Simulation

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum phase field (QPF) simulations offer a powerful tool for studying complex quantum systems, particularly in areas such as condensed matter physics and quantum materials. However, traditional QPF simulations often require extensive parameter tuning, which can be time-consuming and limit the exploration of the parameter space. This paper introduces a novel adaptive optimization algorithm for QPF simulations, designed to automatically adjust simulation parameters based on the resulting data, thereby significantly improving simulation accuracy and efficiency. The algorithm leverages the inherent dynamics of quantum phase field simulations to dynamically adjust parameters, ensuring a robust and efficient simulation process. We demonstrate the effectiveness of our approach through a series of simulations, showcasing its ability to achieve superior results compared to existing optimization techniques.

DOI: 10.5281/zenodo.22246890

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