基于自适应的量子纠缠模拟算法

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum entanglement, a cornerstone of quantum mechanics, is a uniquely fragile phenomenon with profound implications for quantum computing and information processing. Accurate simulation of entangled states is crucial for many quantum algorithms and applications. However, traditional quantum state simulation methods often struggle with the inherent complexity of entangled systems and the difficulty of accurately capturing their dynamics. This paper introduces a novel approach to quantum state simulation, termed "Adaptive Quantum Entanglement Simulation" (AQES), which leverages dynamic parameter adjustment to enhance the precision and efficiency of quantum state simulations. AQES employs a feedback mechanism that continuously monitors and adjusts parameters based on the observed evolution of entangled states, mitigating the limitations of static simulation approaches. We demonstrate the effectiveness of AQES through a series of simulations involving various entangled systems, showcasing significant improvements in fidelity and reduced computational cost compared to traditional methods. The core of the algorithm lies in its ability to dynamically respond to subtle changes in the entanglement network, ensuring a more robust and accurate representation of quantum states.

DOI: 10.5281/zenodo.22315704

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