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,...
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| Published in | IEEE open journal of vehicular technology Vol. 3; pp. 375 - 387 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2644-1330 2644-1330 |
| DOI | 10.1109/OJVT.2022.3202876 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Shin, Hyundong Dobre, Octavia A. Duong, Trung Q. Ansere, James Adu Narottama, Bhaskara Sharma, Vishal |
| Author_xml | – sequence: 1 givenname: Trung Q. orcidid: 0000-0002-4703-4836 surname: Duong fullname: Duong, Trung Q. email: trung.q.duong@qub.ac.uk organization: School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, U.K – sequence: 2 givenname: James Adu surname: Ansere fullname: Ansere, James Adu email: jaansere@stu.edu.gh organization: Department of Electrical and Electronic Engineering, Sunyani Technical University, Sunyani, Ghana – sequence: 3 givenname: Bhaskara orcidid: 0000-0001-8596-1027 surname: Narottama fullname: Narottama, Bhaskara email: bhaskara@kumoh.ac.kr organization: Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea – sequence: 4 givenname: Vishal surname: Sharma fullname: Sharma, Vishal email: v.sharma@qub.ac.uk organization: School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, U.K – sequence: 5 givenname: Octavia A. orcidid: 0000-0001-8528-0512 surname: Dobre fullname: Dobre, Octavia A. email: odobre@mun.ca organization: Faculty of Engineering and Applied Science, Memorial University, St. John's, NL, Canada – sequence: 6 givenname: Hyundong orcidid: 0000-0003-3364-8084 surname: Shin fullname: Shin, Hyundong email: hshin@khu.ac.kr organization: Kyung Hee University, Gyeonggi-do, South Korea |
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| SubjectTerms | 6G mobile communication 6G networks Computational efficiency Logic gates Machine learning Machine learning algorithms Quantum computing quantum machine learning quantum security Qubit |
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| Title | Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions |
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