Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions
Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. Recently, there has been a proliferating growth of the size of multi-dimensional datasets, the input-output space dimensionality,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9870532/ |
| _version_ | 1818020084071792640 |
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| author | Trung Q. Duong James Adu Ansere Bhaskara Narottama Vishal Sharma Octavia A. Dobre Hyundong Shin |
| author_facet | Trung Q. Duong James Adu Ansere Bhaskara Narottama Vishal Sharma Octavia A. Dobre Hyundong Shin |
| author_sort | Trung Q. Duong |
| collection | DOAJ |
| description | Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. Recently, there has been a proliferating growth of the size of multi-dimensional datasets, the input-output space dimensionality, and data structures. Hence, the conventional machine learning approaches in data training and processing have exhibited their limited computing capabilities to support the sixth-generation (6G) networks with highly dynamic applications and services. In this regard, the fast developing quantum computing with machine learning for 6G networks is investigated. Quantum machine learning algorithm can significantly enhance the processing efficiency and exponentially computational speed-up for effective quantum data representation and superposition framework, highly capable of guaranteeing high data storage and secured communications. We present the state-of-the-art in quantum computing and provide a comprehensive overview of its potential, via machine learning approaches. Furthermore, we introduce quantum-inspired machine learning applications for 6G networks in terms of resource allocation and network security, considering their enabling technologies and potential challenges. Finally, some dominating research issues and future research directions for the quantum-inspired machine learning in 6G networks are elaborated. |
| first_indexed | 2024-04-14T08:00:11Z |
| format | Article |
| id | doaj.art-55207f74cce84ea18b6daa215ca93d38 |
| institution | Directory Open Access Journal |
| issn | 2644-1330 |
| language | English |
| last_indexed | 2024-04-14T08:00:11Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj.art-55207f74cce84ea18b6daa215ca93d382022-12-22T02:04:55ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302022-01-01337538710.1109/OJVT.2022.32028769870532Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research DirectionsTrung Q. Duong0https://orcid.org/0000-0002-4703-4836James Adu Ansere1Bhaskara Narottama2https://orcid.org/0000-0001-8596-1027Vishal Sharma3Octavia A. Dobre4https://orcid.org/0000-0001-8528-0512Hyundong Shin5https://orcid.org/0000-0003-3364-8084School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, U.K.Department of Electrical and Electronic Engineering, Sunyani Technical University, Sunyani, GhanaDepartment of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South KoreaSchool of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, U.K.Faculty of Engineering and Applied Science, Memorial University, St. John's, NL, CanadaKyung Hee University, Gyeonggi-do, South KoreaQuantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. Recently, there has been a proliferating growth of the size of multi-dimensional datasets, the input-output space dimensionality, and data structures. Hence, the conventional machine learning approaches in data training and processing have exhibited their limited computing capabilities to support the sixth-generation (6G) networks with highly dynamic applications and services. In this regard, the fast developing quantum computing with machine learning for 6G networks is investigated. Quantum machine learning algorithm can significantly enhance the processing efficiency and exponentially computational speed-up for effective quantum data representation and superposition framework, highly capable of guaranteeing high data storage and secured communications. We present the state-of-the-art in quantum computing and provide a comprehensive overview of its potential, via machine learning approaches. Furthermore, we introduce quantum-inspired machine learning applications for 6G networks in terms of resource allocation and network security, considering their enabling technologies and potential challenges. Finally, some dominating research issues and future research directions for the quantum-inspired machine learning in 6G networks are elaborated.https://ieeexplore.ieee.org/document/9870532/6G networksmachine learningquantum machine learningquantum security |
| spellingShingle | Trung Q. Duong James Adu Ansere Bhaskara Narottama Vishal Sharma Octavia A. Dobre Hyundong Shin Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions IEEE Open Journal of Vehicular Technology 6G networks machine learning quantum machine learning quantum security |
| title | Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions |
| title_full | Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions |
| title_fullStr | Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions |
| title_full_unstemmed | Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions |
| title_short | Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions |
| title_sort | quantum inspired machine learning for 6g fundamentals security resource allocations challenges and future research directions |
| topic | 6G networks machine learning quantum machine learning quantum security |
| url | https://ieeexplore.ieee.org/document/9870532/ |
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