Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing

TL;DR

This paper critically evaluates the current limitations of variational quantum algorithms for dynamic satellite network routing, revealing significant challenges in solving even simple problems and learning effective strategies.

Contribution

It provides the first comprehensive assessment of static and reinforcement learning quantum algorithms in satellite routing, identifying key obstacles like barren plateaus and instability.

Findings

Static optimizers fail on simple 4-node problems.

QRL agents do not outperform random in 8-node environments.

Major challenges hinder practical quantum routing solutions.

Abstract

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.