Quantum-Based Intelligence for Cellular Network Management: Use Cases, Challenges, and O-RAN Integration

As cellular networks evolve towards 5G and beyond, the increasing complexity demands advanced approaches for efficient management and network intelligence. While classical machine learning (ML) plays a vital role in network planning, optimization and resilience, it faces limitations in handling largescale data and dynamic environments. Quantum Technologies (QTs) offer a promising way forward in this area, leveraging quantum computing principles to potentially enhance processing power and scalability. Toward this end, the present article explores the application of the Quantum Machine Learning (QML) and Quantum-Inspired (QI) algorithms in the management of cellular networks. A novel framework for the integration of such approaches in 5G / 6G networks is developed with a special focus on Open RAN architectures. The proposed application of QTs is then assessed based on real cellular data and Quantum algorithms for three main areas of cellular management: network metrics estimation, classification of failures, and resource optimization, showing the capabilities of the approach. Finally, the main challenges and future R&D lines in the area are identified.