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

Abstract

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.

The Potential Short- and Long-Term Disruptions and Transformative Impacts of 5G and Beyond Wireless Networks: Lessons Learnt From the Development of a 5G Testbed Environment

“…The MNOs may deploy a core 5G radio access network (RAN) infrastructure to coordinate the network heterogeneity [2]. The application of machine Learning (ML) methods for achieving the harmonization and learning of the network state is a potential enabler [10].…”

“…In this direction, this paper proposes the development of a unified data hub with layered structured privacy and security along with blockchain and encrypted off-chain based ownership/royalty deployment cost, ii) the explosion of connected devices caused by the advent of mMTCs may very rapidly lead towards reaching the network capacity limit, iii) the rate and volume of the data generated in hyper massively connected 5G networks may need new data analytic innovations, and iv) the privacy and security provisions in massively connected networks -to name a few. The telecommunication engineers, industries, and researchers from around the globe have also already initiated the speculative propositions for network requirements and candidate technologies for beyond 5G (B5G) networks, see e.g., [10][11][12][13][14].…”

What should 6G be?

“…Additionally, AI empowered 6G would unlock the full potential of radio signals and enable the transformation from cognitive radio (CR) to intelligent radio (IR) [28]. Machine learning is in particular crucial for realizing AI empowered 6G from the algorithmic perspective, which has been detailed in [29]. Besides the algorithms, reconfigurable intelligent surfaces are supposed to be used to construct the hardware foundation of AI in wireless communications [30].…”

Learning-Driven Wireless Communications, towards 6G

“…In the following, we will explain how ML algorithms including supervised learning, unsupervised learning, and reinforcement learning, can be utilized in different network layers [9].…”