Dynamic Entanglement Packet Scheduling for Quantum Networks

• URL: http://arxiv.org/abs/2605.28795v1
• Date: Wed, 27 May 2026 17:51:30 GMT
• Title: Dynamic Entanglement Packet Scheduling for Quantum Networks
• Abstract summary: We propose an online scheduler that dynamically schedules, defers, retries, or drops entanglement distribution reservations.<n>In our simulations, the dynamic scheduler achieves lower completion time, higher completion ratio, and higher throughput than the static baseline.
• Score: 0.7349727826230863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sharing entanglement among multiple users remains a central challenge for scalable quantum networks. Recent work proposed an on-demand entanglement packet architecture in which a controller uses a Time Division Multiple Access (TDMA) approach to allocate network resources. Quantum nodes are assigned a periodic schedule that probabilistically fulfills application requests for end-to-end entanglements. The schedule is recomputed periodically using well-known algorithms, such as Earliest Deadline First (EDF). However, a static schedule offers limited flexibility when outcomes are stochastic and arrivals are asynchronous. To overcome this limitation, we propose an online scheduler that dynamically schedules, defers, retries, or drops entanglement distribution reservations. In our simulations, the dynamic scheduler achieves lower completion time, higher completion ratio, and higher throughput than the static baseline. Furthermore, when the network is overloaded, the dynamic scheduler continues to construct deadline-feasible schedules and degrades gracefully.

Related papers

• Centralizing Task-based Approach to Quantum Network Control [71.48385446560893]
  layered architectures impose stringent design and timing constraints on quantum networks.<n>We simulate a resource-centric, task-based approach to quantum network control by utilizing a centralized controller.
  arXiv  Detail & Related papers  (2026-05-05T03:57:17Z)
• Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version) [50.894272363373126]
  In wireless networks characterized by dense connectivity, the significant signaling overhead generated by distributed link scheduling algorithms can exacerbate issues like congestion, energy consumption, and radio footprint expansion.<n>We propose a distributed link sparsification scheme employing graph neural networks (GNNs) to reduce scheduling overhead for delay-tolerant traffic while maintaining network capacity.<n>A GNN module is trained to adjust contention thresholds for individual links based on traffic statistics and network topology, enabling links to withdraw from scheduling contention when they are unlikely to succeed.
  arXiv  Detail & Related papers  (2025-09-05T18:59:14Z)
• CSGO: Generalized Optimization for Cold Start in Wireless Collaborative Edge LLM Systems [62.24576366776727]
  We propose a latency-aware scheduling framework to minimize total inference latency.<n>We show that the proposed method significantly reduces cold-start latency compared to baseline strategies.
  arXiv  Detail & Related papers  (2025-08-15T07:49:22Z)
• Entanglement Request Scheduling in Quantum Networks Using Deep Q-Network [3.1856756516735936]
  A novel Deep Q-Network (DQN) based scheduling method is proposed to optimize delay time and fairness among entanglement requests in quantum repeater networks.<n>Results show that the proposed technique achieves higher performance compared to Greedy, Proportional fair and FIFO scheduling schemes.
  arXiv  Detail & Related papers  (2025-05-18T15:32:21Z)
• Network-Aware Scheduling for Remote Gate Execution in Quantum Data Centers [8.528068737844364]
  We evaluate two entanglement scheduling strategies-static and dynamic-and analyze their performance.<n>We show that dynamic scheduling consistently outperforms static scheduling in scenarios with high entanglement parallelism.
  arXiv  Detail & Related papers  (2025-04-28T18:22:22Z)
• Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing [73.7522199491117]
  Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources. The fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning. We propose a resource allocation model to provision quantum computing and networking resources.
  arXiv  Detail & Related papers  (2023-07-25T00:38:46Z)
• Dynamic Scheduling for Federated Edge Learning with Streaming Data [56.91063444859008]
  We consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration.
  arXiv  Detail & Related papers  (2023-05-02T07:41:16Z)
• ESDI: Entanglement Scheduling and Distribution in the Quantum Internet [4.558581187459857]
  Quantum entanglement distribution between remote nodes is key to many promising quantum applications. This paper considers entanglement scheduling and distribution among many source-destination pairs with different requests over an entire quantum network topology. Inspired by optimal scheduling disciplines in conventional single-queue scenarios, we design a general optimization framework for entanglement scheduling and distribution called ESDI.
  arXiv  Detail & Related papers  (2023-03-30T17:09:59Z)
• Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning [11.007816552466952]
  This paper focuses on the problem of scheduling inference queries on Deep Neural Networks in edge networks at short timescales. By means of simulations, we analyze several policies in the realistic network settings and workloads of a large ISP. We design ASET, a Reinforcement Learning based scheduling algorithm able to adapt its decisions according to the system conditions.
  arXiv  Detail & Related papers  (2023-01-31T13:23:34Z)
• Better than the Best: Gradient-based Improper Reinforcement Learning for Network Scheduling [60.48359567964899]
  We consider the problem of scheduling in constrained queueing networks with a view to minimizing packet delay. We use a policy gradient based reinforcement learning algorithm that produces a scheduler that performs better than the available atomic policies.
  arXiv  Detail & Related papers  (2021-05-01T10:18:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.