# Adaptive Entanglement Routing with Deep Q-Networks in Quantum Networks

arXiv: https://arxiv.org/abs/2503.02895 · HTML: https://mlsanity.com/paper/2503.02895/

## Key facts

- Cited by 1 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 6 of its 39 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Placed in

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

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

- [Active learning machine learns to create new quantum experiments](https://mlsanity.com/paper/1706.00868/) (2017)
- [Fidelity-Guarantee Entanglement Routing in Quantum Networks](https://mlsanity.com/paper/2111.07764/) (2021)
- [Multi-Entanglement Routing Design over Quantum Networks](https://mlsanity.com/paper/2205.15501/) (2022)
- [Policies for elementary links in a quantum network](https://mlsanity.com/paper/2007.03193/) (2020)
- [Resource Allocation in Quantum Networks for Distributed Quantum Computing](https://mlsanity.com/paper/2203.05844/) (2022)
- [ESDI: Entanglement Scheduling and Distribution in the Quantum Internet](https://mlsanity.com/paper/2303.17540/) (2023)

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

- [Robust Belief-State Policy Learning for Quantum Network Routing Under Decoherence and Time-Varying Conditions](https://mlsanity.com/paper/2509.08654/) (2025)

## Abstract (arXiv)

The quantum internet holds transformative potential for global communication by harnessing the principles of quantum information processing. Despite significant advancements in quantum communication technologies, the efficient distribution of critical resources, such as qubits, remains a persistent and unresolved challenge. Conventional approaches often fall short of achieving optimal resource allocation, underscoring the necessity for more effective solutions. This study proposes a novel reinforcement learning-based adaptive entanglement routing framework designed to enable resource allocation tailored to the specific demands of quantum applications. The introduced QuDQN model utilizes reinforcement learning to optimize the management of quantum networks, allocate resources efficiently, and enhance entanglement routing. The model integrates key considerations, including fidelity requirements, network topology, qubit capacity, and request demands.