Self-Adaptive Quantum State Simulation Parameter Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel framework for quantum state simulation parameter optimization, driven by a self-adaptive module. Traditional quantum simulation often relies on manual parameter tuning, limiting the ability to effectively model complex quantum phenomena. Our approach introduces a dynamically adjusting parameter optimization module that leverages learned quantum state characteristics to refine simulation results. This leads to improved accuracy and efficiency in simulating quantum systems. The framework addresses a significant challenge in quantum simulation – the need for automated parameter adjustment – and offers a promising solution for tackling increasingly complex problems. The core mechanism centers around a self-learning algorithm that adapts the simulation parameters to best match the observed quantum state evolution. This results in a more robust and accurate simulation process.

DOI: 10.5281/zenodo.22201122

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