# Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing

arXiv: https://arxiv.org/abs/2508.04288 · HTML: https://mlsanity.com/paper/2508.04288/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Parametrized quantum policies for reinforcement learning](https://mlsanity.com/paper/2103.05577/) (2021)
- [Quantum Graph Neural Networks](https://mlsanity.com/paper/1909.12264/) (2019)
- [CubeSat Communications: Recent Advances and Future Challenges](https://mlsanity.com/paper/1908.09501/) (2019)
- [SatAIOps: Revamping the Full Life-Cycle Satellite Network Operations](https://mlsanity.com/paper/2305.08722/) (2023)
- [On-Demand Routing in LEO Mega-Constellations with Dynamic Laser Inter-Satellite Links](https://mlsanity.com/paper/2406.01953/) (2024)

## Abstract (arXiv)

Applying near-term variational quantum algorithms to the problem of dynamic satellite network routing represents a promising direction for quantum computing. In this work, we provide a critical evaluation of two major approaches: static quantum optimizers such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA) for offline route computation, and Quantum Reinforcement Learning (QRL) methods for online decision-making. Using ideal, noise-free simulations, we find that these algorithms face significant challenges. Specifically, static optimizers are unable to solve even a classically easy 4-node shortest path problem due to the complexity of the optimization landscape. Likewise, a basic QRL agent based on policy gradient methods fails to learn a useful routing strategy in a dynamic 8-node environment and performs no better than random actions. These negative findings highlight key obstacles that must be addressed before quantum algorithms can offer real advantages in communication networks. We discuss the underlying causes of these limitations, including barren plateaus and learning instability, and suggest future research directions to overcome them.