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

arXiv: https://arxiv.org/abs/2509.01008 · HTML: https://mlsanity.com/paper/2509.01008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 57 references that are in the map.
- First posted on arXiv 31 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [Differentiable Convex Optimization Layers](https://mlsanity.com/paper/1910.12430/) (2019)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning](https://mlsanity.com/paper/2205.00293/) (2022)
- [Quantum-Inspired Machine Learning: a Survey](https://mlsanity.com/paper/2308.11269/) (2023)
- [Tensor networks in machine learning](https://mlsanity.com/paper/2207.02851/) (2022)
- [Measuring and Estimating Key Quality Indicators in Cloud Gaming services](https://mlsanity.com/paper/2212.14073/) (2022)
- [Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework](https://mlsanity.com/paper/2306.01787/) (2023)
- [A Cost and Power Feasibility Analysis of Quantum Annealing for NextG Cellular Wireless Networks](https://mlsanity.com/paper/2109.01465/) (2021)
- [Modeling of Key Quality Indicators for End-to-End Network Management: Preparing for 5G](https://mlsanity.com/paper/2402.07071/) (2024)

## Abstract (arXiv)

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.