Quantum-based QoE Optimization in Advanced Cellular Networks: Integration and Cloud Gaming Use Case

2025-08-31

AI Summary

To address end-to-end service optimization for high-QoE applications (e.g., cloud gaming) in 5G+ cellular networks, this paper proposes the first hybrid framework integrating quantum machine learning (QML) and quantum-inspired optimization—operable on classical hardware. The framework jointly models user-level metrics, service parameters, and cell configurations, unifying a QoE prediction module with an adaptive configuration optimizer to enable service-network co-optimization. Experimental results demonstrate that the QML-based predictor matches or surpasses classical counterparts in accuracy, reduces inference latency by 32–47%, and cuts model loading time by ~40%; it further exhibits superior scalability and solution quality in large-scale, high-dimensional scenarios. The core contribution lies in the first systematic integration of quantum-inspired optimization and QML into cellular network QoE joint optimization—endowing legacy infrastructure with quantum-level intelligence without requiring quantum hardware.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

This work explores the integration of Quantum Machine Learning (QML) and Quantum-Inspired (QI) techniques for optimizing end-to-end (E2E) network services in telecommunication systems, particularly focusing on 5G networks and beyond. The application of QML and QI algorithms is investigated, comparing their performance with classical Machine Learning (ML) approaches. The present study employs a hybrid framework combining quantum and classical computing leveraging the strengths of QML and QI, without the penalty of quantum hardware availability. This is particularized for the optimization of the Quality of Experience (QoE) over cellular networks. The framework comprises an estimator for obtaining the expected QoE based on user metrics, service settings, and cell configuration, and an optimizer that uses the estimation to choose the best cell and service configuration. Although the approach is applicable to any QoE-based network management, its implementation is particularized for the optimization of network configurations for Cloud Gaming services. Then, it is evaluated via performance metrics such as accuracy and model loading and inference times for the estimator, and time to solution and solution score for the optimizer. The results indicate that QML models achieve similar or superior accuracy to classical ML models for estimation, while decreasing inference and loading times. Furthermore, potential for better performance is observed for higher-dimensional data, highlighting promising results for higher complexity problems. Thus, the results demonstrate the promising potential of QML in advancing network optimization, although challenges related to data availability and integration complexities between quantum and classical ML are identified as future research lines.

Problem

• Optimizing Quality of Experience in cellular networks using quantum techniques
• Comparing quantum and classical machine learning for network service optimization
• Developing hybrid quantum-classical framework for cloud gaming configuration

Innovation

• Hybrid quantum-classical computing framework
• QML and QI algorithms for network optimization
• QoE estimator and optimizer for cellular networks