mlsanityData to 14 Oct 2026

Adaptive Entanglement Routing with Deep Q-Networks in Quantum Networks

arXiv 2503.02895 ↗First posted 1 Mar 2025quant-ph cs.AIcited by 1

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

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.

Key facts. Cited by 1 paper 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.

Builds on

6 of 39 references.

References · 6

  1. 2017Active learning machine learns to create new quantum experimentscited by 41
  2. 2021Fidelity-Guarantee Entanglement Routing in Quantum Networkscited by 4
  3. 2022Multi-Entanglement Routing Design over Quantum Networkscited by 4
  4. 2020Policies for elementary links in a quantum networkcited by 3
  5. 2022Resource Allocation in Quantum Networks for Distributed Quantum Computingcited by 1
  6. 2023ESDI: Entanglement Scheduling and Distribution in the Quantum Internetcited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Robust Belief-State Policy Learning for Quantum Network Routing Under Decoherence and Time-Varying Conditionscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks
  2. 2026Learning and interpreting policies for simultaneous entanglement requests in quantum networks
  3. 2026SatQNet: Satellite-assisted Quantum Network Entanglement Routing Using Directed Line Graph Neural Networks
  4. 2025Reinforcement Learning for Optimizing Large Qubit Array based Quantum Sensor Circuits
  5. 2026QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning
  6. 2025Quantum Decision Transformers (QDT): Synergistic Entanglement and Interference for Offline Reinforcement Learning
  7. 2024AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search
  8. 2025Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing