Fuzzy C-means and explainable AI for quantum entanglement classification and noise analysis

Quantum entanglement plays a fundamental role in quantum mechanics, with applications in quantum computing. This study introduces a new approach that integrates quantum simulations, noise analysis, and fuzzy clustering to classify and evaluate the stability of quantum entangled states under noisy co...

Full description

Bibliographic Details
Author: Marín Díaz, Gabriel
Format: article
Publication Date:2025
Country:España
Institution:Universidad Complutense de Madrid (UCM)
Repository:Docta Complutense
Language:English
OAI Identifier:oai:docta.ucm.es:20.500.14352/129673
Online Access:https://hdl.handle.net/20.500.14352/129673
Access Level:Open access
Keyword:004.8
311
530.145.8
quantum entanglement
quantum decoherence mitigation
fuzzy clustering in quantum systems
Explainable Artificial Intelligence
XAI
Física de materiales
Teoría de los quanta
Inteligencia artificial (Informática)
Estadística aplicada
2212.12 Teoría Cuántica de Campos
2208.07 Física de Partículas
1203.04 Inteligencia Artificial
1209.01 Estadística Analítica
id ES_9c245176e0430fdfc2cc6cf8dcfb1e54
oai_identifier_str oai:docta.ucm.es:20.500.14352/129673
network_acronym_str ES
network_name_str España
repository_id_str
spelling Fuzzy C-means and explainable AI for quantum entanglement classification and noise analysisMarín Díaz, Gabriel004.8311530.145.8quantum entanglementquantum decoherence mitigationfuzzy clustering in quantum systemsExplainable Artificial IntelligenceXAIFísica de materialesTeoría de los quantaInteligencia artificial (Informática)Estadística aplicada2212.12 Teoría Cuántica de Campos2208.07 Física de Partículas1203.04 Inteligencia Artificial1209.01 Estadística AnalíticaQuantum entanglement plays a fundamental role in quantum mechanics, with applications in quantum computing. This study introduces a new approach that integrates quantum simulations, noise analysis, and fuzzy clustering to classify and evaluate the stability of quantum entangled states under noisy conditions. The Fuzzy C-Means clustering model (FCM) is applied to identify different categories of quantum states based on fidelity and entropy trends, allowing for a structured assessment of the impact of noise. The presented methodology follows five key phases: a simulation of the Bell state, the introduction of the noise channel (depolarization and phase damping), noise suppression using corrective operators, clustering-based state classification, and interpretability analysis using Explainable Artificial Intelligence (XAI) techniques. The results indicate that while moderate noise levels allow for partial state recovery, strong decoherence, particularly under depolarization, remains a major challenge. Rather than relying solely on noise suppression, a classification-based strategy is proposed to identify states that retain computational feasibility despite the effects of noise. This hybrid approach combining quantum-state classification with AI-based interpretability offers a new framework for assessing the resilience of quantum systems. The results have practical implications in quantum error correction, quantum cryptography, and the optimization of quantum technologies under realistic conditions.MDPIUniversidad Complutense de Madrid20252025-03-2420252025-03-24journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/129673reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1296732026-06-02T12:44:21Z
dc.title.none.fl_str_mv Fuzzy C-means and explainable AI for quantum entanglement classification and noise analysis
title Fuzzy C-means and explainable AI for quantum entanglement classification and noise analysis
spellingShingle Fuzzy C-means and explainable AI for quantum entanglement classification and noise analysis
Marín Díaz, Gabriel
004.8
311
530.145.8
quantum entanglement
quantum decoherence mitigation
fuzzy clustering in quantum systems
Explainable Artificial Intelligence
XAI
Física de materiales
Teoría de los quanta
Inteligencia artificial (Informática)
Estadística aplicada
2212.12 Teoría Cuántica de Campos
2208.07 Física de Partículas
1203.04 Inteligencia Artificial
1209.01 Estadística Analítica
title_short Fuzzy C-means and explainable AI for quantum entanglement classification and noise analysis
title_full Fuzzy C-means and explainable AI for quantum entanglement classification and noise analysis
title_fullStr Fuzzy C-means and explainable AI for quantum entanglement classification and noise analysis
title_full_unstemmed Fuzzy C-means and explainable AI for quantum entanglement classification and noise analysis
title_sort Fuzzy C-means and explainable AI for quantum entanglement classification and noise analysis
dc.creator.none.fl_str_mv Marín Díaz, Gabriel
author Marín Díaz, Gabriel
author_facet Marín Díaz, Gabriel
author_role author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 004.8
311
530.145.8
quantum entanglement
quantum decoherence mitigation
fuzzy clustering in quantum systems
Explainable Artificial Intelligence
XAI
Física de materiales
Teoría de los quanta
Inteligencia artificial (Informática)
Estadística aplicada
2212.12 Teoría Cuántica de Campos
2208.07 Física de Partículas
1203.04 Inteligencia Artificial
1209.01 Estadística Analítica
topic 004.8
311
530.145.8
quantum entanglement
quantum decoherence mitigation
fuzzy clustering in quantum systems
Explainable Artificial Intelligence
XAI
Física de materiales
Teoría de los quanta
Inteligencia artificial (Informática)
Estadística aplicada
2212.12 Teoría Cuántica de Campos
2208.07 Física de Partículas
1203.04 Inteligencia Artificial
1209.01 Estadística Analítica
description Quantum entanglement plays a fundamental role in quantum mechanics, with applications in quantum computing. This study introduces a new approach that integrates quantum simulations, noise analysis, and fuzzy clustering to classify and evaluate the stability of quantum entangled states under noisy conditions. The Fuzzy C-Means clustering model (FCM) is applied to identify different categories of quantum states based on fidelity and entropy trends, allowing for a structured assessment of the impact of noise. The presented methodology follows five key phases: a simulation of the Bell state, the introduction of the noise channel (depolarization and phase damping), noise suppression using corrective operators, clustering-based state classification, and interpretability analysis using Explainable Artificial Intelligence (XAI) techniques. The results indicate that while moderate noise levels allow for partial state recovery, strong decoherence, particularly under depolarization, remains a major challenge. Rather than relying solely on noise suppression, a classification-based strategy is proposed to identify states that retain computational feasibility despite the effects of noise. This hybrid approach combining quantum-state classification with AI-based interpretability offers a new framework for assessing the resilience of quantum systems. The results have practical implications in quantum error correction, quantum cryptography, and the optimization of quantum technologies under realistic conditions.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-03-24
2025
2025-03-24
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/129673
url https://hdl.handle.net/20.500.14352/129673
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869414632122220544
score 15,198674

Similar Items