Fuzzy C-Means and Explainable AI for Quantum Entanglement Classification and Noise Analysis

This record corresponds to the accepted manuscript (post-print) of the following journal article:

"Fuzzy C-Means and Explainable AI for Quantum Entanglement Classification and Noise Analysis"

This article proposes a hybrid methodological framework for the classification and analysis of quantum entangled states under noisy conditions by integrating quantum simulations with Fuzzy C-Means (FCM) clustering and Explainable Artificial Intelligence (XAI) techniques. The approach enables the identification of stable and computationally viable quantum states based on fidelity and entropy patterns, while XAI methods provide interpretability of the classification results. The framework offers a novel perspective for assessing the resilience of quantum systems in realistic environments.

The final published version is available at the publisher’s website:
https://doi.org/10.3390/math13071056

This deposit is made for open access and dissemination purposes, in accordance with the publisher’s self-archiving policy.