Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions

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 proliferation 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 face a huge limited computing capabilities to support the sixthgeneration (6G) networks with highly dynamic applications and services. Under this regard, the fast developing quantum computing with machine learning (QML) for 6G networks is investigated. QML algorithms can significantly enhanced 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 comprehensive overviews through machine learning approaches for their applications in the 6G networks. Furthermore, we introduce quantum-inspired machine learning applications for 6G networks, 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.

Optimizing the Energy Efficiency Using Quantum Based Load Balancing in Open Radio Access Networks

“…Additionally, quantum computing offers advancements in cryptography techniques, such as quantum key distribution, which can bolster the security of communication networks [53]. Furthermore, quantum-inspired algorithms running on classical hardware can provide performance benefits, leveraging quantum principles to solve optimization and machine learning problems, potentially enhancing ORAN functionality [54].…”

Wireless and Optical Convergent Access Technologies Toward 6G

“…In parallel, recent mobile communication systems have embraced a plurality of new services and applications, which increase the demand for computing processing and storage capabilities. Moreover, quantum computing with machine learning has been recently proposed to increase efficiency, enhance and speed up the system computing capabilities [147]. When considering the ML-based linearization techniques, another important challenge must be considered.…”

The quantum internet: A synergy of quantum information technologies and 6G networks

“…Quantum computing can provide exponential speed-up for specific optimisation problems by utilising the superposition principle of quantum mechanics [153,154]. Furthermore, quantum ML algorithms can improve the performance of classical ML algorithms by utilising superposition and quantum entanglement as computing resources [155,156]. Quantum algorithms and quantum ML can provide new solutions for many challenging network design problems in future 6G wireless networks as discussed below.…”

“…Data-driven optimisation algorithms have been shown to improve the performance of communication systems since the inference time is greatly reduced once the ML or deep learning models are trained offline and then deployed at the BS or mobile devices [183,184]. Quantum ML algorithms can further improve the performance of 6G wireless networks by utilising the counter-intuitive properties of quantum entanglement and superposition [155,185]. Quantum circuits comprising qubits and quantum gates can extract enhanced features from raw data using the principles of quantum superposition and entanglement that are otherwise not possible with classical computing algorithms.…”