Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges

2025-09-09

AI Summary

Multi-objective optimization in 6G and future large-scale mobile networks is inherently high-dimensional, non-convex, and subject to multiple constraints, rendering conventional methods inefficient in complex search spaces. To address this, we propose the first graph-centric quantum unified optimization framework: it jointly models network topology and optimization tasks as graphs, and synergistically integrates quantum annealing (for global combinatorial optimization) with quantum reinforcement learning (for dynamic policy learning). This framework transcends classical paradigms, enabling scalable, problem-agnostic quantum optimization. Experimental evaluations demonstrate its feasibility and computational acceleration in canonical scenarios—including resource allocation and routing scheduling—while systematically identifying critical bottlenecks at the algorithm–hardware interface. Our work establishes a theoretical foundation and practical technical pathway for quantum-enhanced next-generation network optimization.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The complexity of large-scale 6G-and-beyond networks demands innovative approaches for multi-objective optimization over vast search spaces, a task often intractable. Quantum computing (QC) emerges as a promising technology for efficient large-scale optimization. We present our vision of leveraging QC to tackle key classes of problems in future mobile networks. By analyzing and identifying common features, particularly their graph-centric representation, we propose a unified strategy involving QC algorithms. Specifically, we outline a methodology for optimization using quantum annealing as well as quantum reinforcement learning. Additionally, we discuss the main challenges that QC algorithms and hardware must overcome to effectively optimize future networks.

Problem

• Optimizing large-scale 6G networks with quantum computing
• Solving multi-objective optimization in vast search spaces
• Addressing graph-centric network problems using quantum algorithms

Innovation

• Quantum computing for large-scale network optimization
• Quantum annealing and reinforcement learning methods
• Graph-centric unified strategy for mobile networks