# RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks

arXiv: https://arxiv.org/abs/2511.22321 · HTML: https://mlsanity.com/paper/2511.22321/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 19 of its 73 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Placed in

- Deep reinforcement learning methods / [Quantum reinforcement learning](https://mlsanity.com/deeprl/quantum-reinforcement-learning/)

## Builds on (most cited first, 19 of 19)

- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case](https://mlsanity.com/paper/1910.07421/) (2019)
- [RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN](https://mlsanity.com/paper/1910.01508/) (2019)
- [Graph-based Deep Learning for Communication Networks: A Survey](https://mlsanity.com/paper/2106.02533/) (2021)
- [Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing](https://mlsanity.com/paper/2402.05027/) (2024)
- [Compiler Design for Distributed Quantum Computing](https://mlsanity.com/paper/2012.09680/) (2020)
- [Distributed Routing in a Quantum Internet](https://mlsanity.com/paper/1907.11630/) (2019)
- [Fidelity-Guarantee Entanglement Routing in Quantum Networks](https://mlsanity.com/paper/2111.07764/) (2021)
- [Quantum Internet Protocol Stack: a Comprehensive Survey](https://mlsanity.com/paper/2202.10894/) (2022)
- [Physics-Informed Quantum Communication Networks: A Vision Towards the Quantum Internet](https://mlsanity.com/paper/2204.09233/) (2022)
- [Multi-Entanglement Routing Design over Quantum Networks](https://mlsanity.com/paper/2205.15501/) (2022)
- [Shortcuts to quantum network routing](https://mlsanity.com/paper/1610.05238/) (2016)
- [Quantum Internet- Applications, Functionalities, Enabling Technologies, Challenges, and Research Directions](https://mlsanity.com/paper/2101.04427/) (2021)
- [A Multiple-Entanglement Routing Framework for Quantum Networks](https://mlsanity.com/paper/2207.11817/) (2022)
- [A Quantum Overlay Network for Efficient Entanglement Distribution](https://mlsanity.com/paper/2212.01694/) (2022)
- [FENDI: Toward High-Fidelity Entanglement Distribution in the Quantum Internet](https://mlsanity.com/paper/2301.08269/) (2023)
- [Cyber Protection Applications of Quantum Computing: A Review](https://mlsanity.com/paper/2406.13259/) (2024)

## Built on by (most cited first, 2 of 2)

- [SatQNet: Satellite-assisted Quantum Network Entanglement Routing Using Directed Line Graph Neural Networks](https://mlsanity.com/paper/2604.09306/) (2026)
- [When AI meets quantum information: A comprehensive review](https://mlsanity.com/paper/2607.00365/) (2026)

## Abstract (arXiv)

Quantum networks are becoming increasingly important because of advancements in quantum computing and quantum sensing, such as recent developments in distributed quantum computing and federated quantum machine learning. Routing entanglement in quantum networks poses several fundamental as well as technical challenges, including the high dynamicity of quantum network links and the probabilistic nature of quantum operations. Consequently, designing hand-crafted heuristics is difficult and often leads to suboptimal performance, especially if global network topology information is unavailable. In this paper, we propose RELiQ, a reinforcement learning-based approach to entanglement routing that only relies on local information and iterative message exchange. Utilizing a graph neural network, RELiQ learns graph representations and avoids overfitting to specific network topologies - a prevalent issue for learning-based approaches. Our approach, trained on random graphs, consistently outperforms existing local information heuristics and learning-based approaches when applied to random and real-world topologies. When compared to global information heuristics, our method achieves similar or superior performance because of its rapid response to topology changes.