دورية أكاديمية
Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead
| العنوان: | Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead |
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| المؤلفون: | Bhaskara Narottama, Zina Mohamed, Sonia Aissa |
| المصدر: | IEEE Open Journal of the Communications Society, Vol 4, Pp 2204-2224 (2023) |
| بيانات النشر: | IEEE, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Telecommunication LCC:Transportation and communications |
| مصطلحات موضوعية: | Next-generation wireless communications, quantum machine learning, optimization of wireless systems, Telecommunication, TK5101-6720, Transportation and communications, HE1-9990 |
| الوصف: | A comprehensive coverage of the state-of-the-art in quantum machine learning (QML) methodologies, with a unique perspective on their applications for wireless communications, is presented. The paper begins by delving into the fundamental principles of quantum computing, and then goes through different operations and techniques that are involved in QML deployments. Subsequently, it provides an in-depth look at various methods peculiar to quantum computing, such as quantum search algorithms, and discusses their potentials towards maximizing the performance of wireless systems. The integration of quantum-based learning models into the existing machine learning methodologies, such as within the frameworks of unsupervised learning and reinforcement learning, are then examined. Taking the viewpoint of wireless communications, diverse studies in the literature that employ QML-based optimization methods are also highlighted. Finally, to ensure the applicability and feasibility of QML for optimizing wireless systems, potential solutions for deployment challenges are addressed. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2644-125X |
| Relation: | https://ieeexplore.ieee.org/document/10233127/; https://doaj.org/toc/2644-125X |
| DOI: | 10.1109/OJCOMS.2023.3309268 |
| URL الوصول: | https://doaj.org/article/edc96dd16d804258a89736fac950e61f |
| رقم الأكسشن: | edsdoj.96dd16d804258a89736fac950e61f |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 2644125X |
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| DOI: | 10.1109/OJCOMS.2023.3309268 |