The quantum network that allows users to communicate in a quantum way will be available in the foreseeable future. The network capable of distributing Bell state entangled pairs faces many challenges due to entanglement decoherence and limited network performance, especially when the network scale is enormous. Many entanglement distribution protocols have been proposed so far, and most of them are in a centralized and synchronized manner, which may be infeasible in large-scale networks. As such, in this paper, we propose a full spontaneous version of quantum networks in which the quantum nodes autonomously manage multiple entanglement distribution requests. However, one major issue is that quantum nodes have little knowledge about the network, especially the congestion (e.g., some nodes may have no usable quantum memories). We present a routing algorithm to adaptive evaluate the congestion on the neighbor nodes to avoid potential congestion. We use SimQN, the new network layer simulation platform built by our research team, to evaluate our proposed design. The result demonstrates that it can adapt to changes in network resources and reduce the drop rate that eventually leads to a higher entanglement distribution rate but remains fair for multiple requests to use the network resources fairly and achieve a more balanced throughput.
The quantum network that allows users to communicate in a quantum way will be available in the foreseeable future. The network capable of distributing Bell state entangled pairs faces many challenges due to entanglement decoherence and limited network performance, especially when the network scale is enormous. Many entanglement distribution protocols have been proposed so far, and most of them are in a centralized and synchronized manner, which may be infeasible in large-scale networks. As such, in this paper, we propose a full spontaneous version of quantum networks in which the quantum nodes autonomously manage multiple entanglement distribution requests. However, one major issue is that quantum nodes have little knowledge about the network, especially the congestion (e.g., some nodes may have no usable quantum memories). We present a routing algorithm to adaptive evaluate the congestion on the neighbor nodes to avoid potential congestion. We use SimQN, the new network layer simulation platform built by our research team, to evaluate our proposed design. The result demonstrates that it can adapt to changes in network resources and reduce the drop rate that eventually leads to a higher entanglement distribution rate but remains fair for multiple requests to use the network resources fairly and achieve a more balanced throughput.
The quantum network is designed to connect numerous quantum nodes and support various ground-breaking quantum applications. Most of these applications require communicating parties to share entangled pairs. Therefore, entanglement routing, a technology distributing entangled pairs between distant quantum nodes, plays a vital role in realizing quantum networks’ capability. However, due to the limitation of quantum memory size and quantum decoherence, the entangled pairs shared by adjacent quantum nodes can hardly satisfy concurrent entanglement routing requests, thus leading to severe network congestion. In this paper, we propose a novel congestion mitigation (CM) scheme to tackle such bottleneck problems. The basic idea of CM is to “recycle” idle link-level entanglement resources from well-resourced links to bottleneck links utilizing a unique enabling technology of quantum networks, called entanglement swapping. CM can increase the capacity of each bottleneck link, thus overcoming resource limitations to improve resource utilization and network throughput. To complete our work, we also propose a swapping-based entanglement routing design, including path selection and resource allocation algorithms. Extensive simulations show that our design can significantly alleviate network congestion and improve the request service rate of quantum networks compared to the traditional entanglement routing designs.
Quantum network can provide end-to-end entanglements through quantum operations, supporting various quantum applications such as quantum key distribution and distributed quantum computing. However, many previous proposals for quantum networks assumed the division of quantum network into time slots and batch processing of network communication tasks, performing poorly in dynamic network scenarios and not fully utilizing network resources. This work focuses on the routing problem in entanglement quantum networks, aiming to provide more efficient end-to-end entanglement establishment for upper-layer applications. We propose an on-demand routing algorithm that dynamically adjusts the optimal path based on the current network conditions. Specifically, we consider the benefits of path width for quantum networks and propose an algorithm for finding the shortest wide path. Using this algorithm, each node establishes its own routing table. Additionally, to dynamically avoid network hotspots, we introduce the concept of traffic to reflect the busyness of nodes. Finally, using a dynamic cost calculation method as the routing metric, we select the optimal path for the current request. To evaluate the performance of the proposed quantum routing algorithm, we conducted simulation experiments on SimQN quantum simulation platform. The results show that our approach improves network throughput by 14 percent compared to existing solutions.
Abstract Future quantum networks will have to support different applications on shared infrastructure, like fidelity-critical quantum key distribution (QKD) and latency-sensitive distributed quantum computing (DQC). The existing approaches for entanglement routing either optimise for a single application, or rely on sequential resource allocation, which results in inefficient resource utilisation, unfairness and degraded performance when multiple applications compete for shared network resources. In this paper, we propose AMARQ, a multi-objective deep Q-network based routing framework to jointly serve heterogeneous QKD and DQC traffic on shared quantum infrastructure. The agent makes adaptive and application-aware routing decisions to dynamically balance the rigid fidelity and latency requirements of QKD and the high-throughput and elastic rate requirements of DQC. The reward function combines a successful-routing throughput term with a per-edge fidelity penalty and an end-of-episode failure penalty; per-flow latency is enforced as a QoS constraint, and inter-application fairness is enforced by a dedicated admission-control loop that reserves a dynamic QKD qubit pool. We assess the performance of AMARQ on grid topologies with 25 to 625 nodes. AMARQ attains a request success ratio above 85%, which is up to 32 percentage points higher than ESDI and 39 percentage points higher than Cicconetti, while keeping the end-to-end fidelity above 0.80 and the inference latency below 90 ms for all configurations.
The realization of a large-scale Quantum Internet is a long-term objective of quantum information science, aiming to interconnect quantum devices through the reliable distribution of entanglement across long distances. Unlike classical networks, quantum networks are constrained by decoherence, probabilistic operations, and the no-cloning theorem, which fundamentally alter network design principles. This paper presents a new simulation-driven study of a scalable and resilient Quantum Internet architecture based on adaptive, entanglement-centric networking. A detailed system model is developed, incorporating realistic quantum channel noise, memory decoherence, and repeater-assisted communication. Novel fidelity-aware routing and resource-adaptive entanglement management strategies are evaluated through extensive simulations. Mathematical models, algorithmic pseudocode, and performance analysis demonstrate how adaptive control significantly improves end-to-end fidelity and network throughput under non-ideal conditions. The results provide actionable design insights for future experimental and large-scale quantum networking deployments.
As a novel form of network structure, quantum networks can be considered nodes equipped with limited quantum memory units connected via lossy optical links. To fully unleash the potentials of quantum networks in applications such as distributed quantum computing, quantum key distribution, etc., an efficient quantum routing mechanism is vital for discovering multiple path solutions that deliver overall higher communication opportunities. Existing quantum routing algorithms focus on the “contention-free” strategy, which may result in restricted throughput gains. In this paper, we propose a novel metric called reusable factor to evaluate the probability that the reserved quantum resources for a candidate path can still offer available channels for other potential available paths even after path assignments. Further, a new Entanglement-Reusable Dynamic Routing Algorithm (ERDRA) is proposed to enable the multiple assignment of quantum entanglement. Simulation results demonstrate that Erdra achieves solid performance gains over state-of-the-art algorithms under various network settings.
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.