Quantum Machine Learning for 6G Space-Air-Ground Integrated Networks: A Comprehensive Tutorial and Survey
Hassan, Sheikh Salman Khan, Latif U. Park, Yu Min Guizani, Mohsen Han, Zhu Ratnarajah, Tharmalingam Hong, Choong Seon
Hassan, Sheikh Salman
Khan, Latif U.
Park, Yu Min
Guizani, Mohsen
Han, Zhu
Ratnarajah, Tharmalingam
Hong, Choong Seon
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Abstract
Space-air-ground integrated networks (SAGINs) are emerging as a fundamental architecture for 6G systems to enable massive connectivity, novel applications, extreme data rates, ultra-low latency, and multi-dimensional networking. The complex nature of SAGINs has driven researchers to explore classical machine learning (CML) as a powerful tool for modeling and optimizing these multi-layer networks. Although CML offers many benefits, it will suffer from exponential complexity for SAGIN design, mainly due to high-dimensional data and problem spaces. Therefore, the NP-hard problems are challenging for CML to enable SAGINs efficiently. To address this critical challenge, we present a comprehensive survey examining the integration of quantum ML (QML) with SAGINs. We systematically evaluate quantum-enhanced SAGIN technologies through detailed performance metrics and create a new classification system for hybrid network architectures. We thoroughly examine key integration strategies and analyze major technical challenges, which include how quantum states degrade in space, how to distribute resources optimally, and how to develop security protocols. We show how QML can solve the growing complexity of 6G networks by improving network capacity, intelligence, and security. The research also creates a comprehensive framework for quantum-enhanced SAGINs that researchers and industry professionals can use as a guide when developing next-generation communication systems. Our findings examine the transformative potential of QML in reshaping global communications infrastructure, which presents a structured approach to navigating the intricate challenges of advanced networking technologies and also provides a critical pathway for addressing the exponentially growing computational demands of future communication ecosystems.
Citation
S. S. Hassan et al., "Quantum Machine Learning for 6G Space-Air-Ground Integrated Networks: A Comprehensive Tutorial and Survey," in IEEE Communications Surveys & Tutorials, doi: 10.1109/COMST.2025.3610357
Source
IEEE Communications Surveys and Tutorials
Conference
Keywords
6g, And Quantum Federated And Split Learning, Classical And Quantum Machine Learning, Intelligent Networking, Quantum Communication, Quantum Computing, Space-air-ground Integrated Networks, Complex Networks, Computer Architecture, Intelligent Networks, Internet Protocols, Learning Systems, Machine Learning, Network Architecture, Network Layers, Network Security, Np-hard, Quantum Computers, Quantum Theory, 6g, Air Grounds, And Quantum Federated And Split Learning, And Splits, Classical And Quantum Machine Learning, Integrated Networks, Intelligent Networking, Machine-learning, Quantum Computing, Quantum Machines, Space-air-ground Integrated Network, Quantum Communication
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Publisher
IEEE