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

arXiv: https://arxiv.org/abs/2509.07773 · HTML: https://mlsanity.com/paper/2509.07773/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Joint Uplink-Downlink Capacity and Coverage Optimization via Site-Specific Learning of Antenna Settings](https://mlsanity.com/paper/2210.15732/) (2022)
- [X-ResQ: Reverse Annealing for Quantum MIMO Detection with Flexible Parallelism](https://mlsanity.com/paper/2402.18778/) (2024)

## Built on by (most cited first, 1 of 1)

- [Neutral-Atom-based Quantum Optimization for Resource Allocation in NOMA Networks](https://mlsanity.com/paper/2609.26556/) (2026)

## Abstract (arXiv)

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.