Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

By S. Nawaz, Shree Krishna Sharma, Shurjeel Wyne, Mohammad N. Patwary, Md. Asaduzzaman. IEEE Access, 2019

490 citations recorded by the source corpus.

The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic–orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users’ service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed.

Subfields: Biomedical Engineering, Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d682326dc1f552053ffbbee41e426da8170d8872

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In Biomedical Engineering

In Computational Theory and Mathematics

Mohamed-Slim Alouini

In Electrical and Electronic Engineering

Citations in the corpus

A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs (2024, background): “There are also the LiFi (light fidelity) [28] and quantum technologies [29], [30] that are in the conversation of the physical layer for 6G.”
6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions (2019, background): “Contribution and Research Direction [3] A comprehensive review of machine learning, quantum computing, and quantum machine learning and their potential benefits, issues, and use cases for their applications in the B5G networks is presented.”
A Survey on Beyond 5G Network With the Advent of 6G: Architecture and Emerging Technologies (2020, background): “It enhances the network efﬁciency for keeping the knowledge on a real-time basis and provides a rapid response to the user demand [73].”
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research (2021, background): “Many AI frameworks, including are machine learning [53], deep reinforcement learning [217], deep learning, distributed learning [68], FL [213], and computational intelligence [221], have been investigated to enable hyperintelligent networking such as AI-driven air interface, intelligent orchestratio”
What should 6G be? (2019, background): “crucial for realizing AI empowered 6G from the algorithmic perspective, which has been detailed in [29].”
The Roadmap to 6G Security and Privacy (2021, background): “This is going to be achieved with multiple technologies such as reconfigurable intelligent surfaces (RIS), visible light communications (VLC), electromagnetic-orbital angular momentum, cell-free communications, and quantum computing [4].”
A Survey of Rate-Optimal Power Domain NOMA With Enabling Technologies of Future Wireless Networks (2019, background): “The authors in [321] specifically discuss how QML can significantly speed”
The Road Towards 6G: A Comprehensive Survey (2021, background): “[11] checked the state-ofthe-art advances in the fields of machine learning (ML)”