Title: Adaptive Subspace Dynamics for Quantum Simulation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum simulation offers an unprecedented opportunity to study complex quantum systems, but current methods often struggle with inherent errors and limitations in fidelity. This paper introduces Adaptive Subspace Dynamics (ASD) – a novel algorithm designed to address these challenges through a dynamic adjustment of the quantum system's subspace dynamics, driven by a cognitive feedback loop. We demonstrate ASD's ability to achieve significantly improved accuracy and fidelity compared to static simulation techniques, particularly in simulating systems with intricate geometries and high degrees of disorder. The core mechanism leverages differential equations for continuous control, creating a self-optimizing simulation process. This research aims to provide a foundation for more robust and adaptable quantum simulation, unlocking new possibilities in materials science, drug discovery, and fundamental physics research.

DOI: 10.5281/zenodo.22191359

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