Dynamic Adaptive Quantum State Modeling

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum state modeling is a crucial field in quantum information science, demanding sophisticated tools for analyzing and predicting complex quantum phenomena. This paper introduces a novel dynamic adaptive quantum state modeling approach, leveraging continuous geometric transformations to enhance the accuracy of quantum state analysis and prediction. The core mechanism centers around a feedback loop that dynamically adjusts geometric operations based on observed quantum state changes, offering a significant advantage over existing methods. We demonstrate the effectiveness of this approach through a series of simulations focusing on the representation of quantum states in specific scenarios. The proposed method significantly improves the fidelity of quantum state estimation and provides a pathway towards more robust and adaptable modeling strategies. The study highlights the potential of dynamic geometry in advancing quantum state understanding.

DOI: 10.5281/zenodo.22265981

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