| title : |
Quantum Machine Learning Applied To Natural Language Processing : Thesis presented to obtain the degree of Doctorate in Computer Science Third cycle doctoral thesis Specialty: Networks and Distributed Systems |
| Type de document : |
electronic document |
| Auteur : |
Nour El Houda, OUAMANE, Author ; Nardjes BOUCHEMAL(President), Hacene BELHADEF(Supervisor), Dr. Abdelhalim SAADI(Co-Supervisor)Madjed BENCHEIKHLEHOCINE(Examiner),Mourad BOUZENADA(Examiner),Mounira BOUZAHZAH(Examiner), Author |
| Editeur : |
جامعة عبد الحفيظ بوالصوف ميلة |
| Date de publication : |
2026 |
| Nombre de pages : |
120p. |
| Dimensions : |
PDF |
| Matériel d'accompagnement : |
قرص مضغوط |
| ISBN (ou autre code) : |
D.N00404 |
| Langue : |
English (eng) Langue originale : English (eng) |
| Mots clé : |
Natural language processing, Quantum machine learning, Sentiment analysis, Quantum convolutional neural networks, Quantum computing. |
| Résumé : |
s social networking platforms continue to grow, users increasingly share their thoughts and opinions through various forms of text. Despite this active engagement, sentiment analysis remains a significant challenge due to the vast volume of
textual data generated from diverse sources. This task has gained considerable attention within the field of Natural Language Processing (NLP), as it provides valuable insights into public opinion, user experiences, and customer feedback. Meanwhile, quantum mechanics has reshaped our understanding of the world, and one of its emerging branches Quantum Machine Learning (QML) has shown promising theoretical results. However,
the application of QML in sentiment analysis is still limited and remains largely at an experimental or theoretical stage. In this thesis, we evaluate one such QML approach:Quantum Convolutional Neural Networks (QCNNs) for movie review classification. QCNNs are quantum neural network architectures inspired by classical Convolutional Neural Networks (CNNs), but implemented entirely using parameterized quantum circuits. We investigate how different QCNN configurations with different number of qubits and structure of their parameterized circuits affect sentiment classification performance on both artificial and real movie review datasets. |
| Lien vers la ressource électronique : |
https://syngeb.univ-mila.dz/fr/opac/result_details/949873 |
Quantum Machine Learning Applied To Natural Language Processing : Thesis presented to obtain the degree of Doctorate in Computer Science Third cycle doctoral thesis Specialty: Networks and Distributed Systems [electronic document] / Nour El Houda, OUAMANE, Author ; Nardjes BOUCHEMAL(President), Hacene BELHADEF(Supervisor), Dr. Abdelhalim SAADI(Co-Supervisor)Madjed BENCHEIKHLEHOCINE(Examiner),Mourad BOUZENADA(Examiner),Mounira BOUZAHZAH(Examiner), Author . - جامعة عبد الحفيظ بوالصوف ميلة, 2026 . - 120p. ; PDF + قرص مضغوط. ISSN : D.N00404 Langue : English ( eng) Langue originale : English ( eng)
| Mots clé : |
Natural language processing, Quantum machine learning, Sentiment analysis, Quantum convolutional neural networks, Quantum computing. |
| Résumé : |
s social networking platforms continue to grow, users increasingly share their thoughts and opinions through various forms of text. Despite this active engagement, sentiment analysis remains a significant challenge due to the vast volume of
textual data generated from diverse sources. This task has gained considerable attention within the field of Natural Language Processing (NLP), as it provides valuable insights into public opinion, user experiences, and customer feedback. Meanwhile, quantum mechanics has reshaped our understanding of the world, and one of its emerging branches Quantum Machine Learning (QML) has shown promising theoretical results. However,
the application of QML in sentiment analysis is still limited and remains largely at an experimental or theoretical stage. In this thesis, we evaluate one such QML approach:Quantum Convolutional Neural Networks (QCNNs) for movie review classification. QCNNs are quantum neural network architectures inspired by classical Convolutional Neural Networks (CNNs), but implemented entirely using parameterized quantum circuits. We investigate how different QCNN configurations with different number of qubits and structure of their parameterized circuits affect sentiment classification performance on both artificial and real movie review datasets. |
| Lien vers la ressource électronique : |
https://syngeb.univ-mila.dz/fr/opac/result_details/949873 |
|
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