Unlike classical routing algorithms, quantum routing algorithms make use of entangled states - a type of resources that have a limited lifetime and need to be regenerated after consumption. In a nutshell, quantum routing algorithms have to use these resources efficiently, while optimizing some objectives such as the total waiting time. Current routing algorithms tend to keep a routing request waiting until all of the resources on its path are available. In this paper, we introduce a new way of managing entanglement resources in an opportunistic fashion: a request can move forward along its path as soon as possible (even if some resources on its path are not ready). We show that this opportunistic approach is fundamentally better than conventional approaches. In particular, our results indicate that this new approach achieves a 30-50% improvement in the average total waiting time and average link waiting time compared with several state-of-the-art routing algorithms. As a by-product of this work, we develop a new simulator for quantum routing, which can be used to evaluate various design choices under different scenarios.
Unlike classical routing algorithms, quantum routing algorithms make use of entangled states - a type of resources that have a limited lifetime and need to be regenerated after consumption. In a nutshell, quantum routing algorithms have to use these resources efficiently, while optimizing some objectives such as the total waiting time. Current routing algorithms tend to keep a routing request waiting until all of the resources on its path are available. In this paper, we introduce a new way of managing entanglement resources in an opportunistic fashion: a request can move forward along its path as soon as possible (even if some resources on its path are not ready). We show that this opportunistic approach is fundamentally better than conventional approaches. In particular, our results indicate that this new approach achieves a 30-50% improvement in the average total waiting time and average link waiting time compared with several state-of-the-art routing algorithms. As a by-product of this work, we develop a new simulator for quantum routing, which can be used to evaluate various design choices under different scenarios.
Unlike classical routing algorithms, quantum routing algorithms make use of entangled states—a type of resources that have a limited lifetime and need to be regenerated after consumption. In a nutshell, quantum routing algorithms have to use these resources efficiently, while optimizing some objectives such as the total waiting time. Current routing algorithms tend to keep a routing request waiting until all of the resources on its path are available. In this paper, we introduce a new way of managing entanglement resources in an opportunistic fashion: a request can move forward along its path as soon as possible (even if some resources on its path are not ready). We show that this opportunistic approach is fundamentally better than conventional approaches. In particular, our results indicate that this new approach achieves a 30-50% improvement in the average total waiting time and average link waiting time compared with several state-of-the-art routing algorithms. As a by-product of this work, we develop a new simulator for quantum routing, which can be used to evaluate various design choices under different scenarios.
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.
Scheduling in quantum networks involves strategically allocating quantum resources to maximize entanglement distribution efficiency and overall performance. Unlike classical networks, quantum systems lack conventional buffering mechanisms, making traditional scheduling paradigms less effective. Existing quantum network algorithms predominantly focus on routing algorithms to identify optimal entanglement paths, often neglecting timeslot scheduling for mitigating resource contention and transmission latency, particularly under multi-user environments. To address these challenges, we propose a routing algorithm tightly integrated with priority-based dynamic scheduling. The algorithm not only discovers efficient entanglement paths but also optimizes resource utilization for concurrent requests. Simulation results confirm that this approach conserves quantum resources, reduces latency, and substantially boosts network performance, providing a robust solution for multi-user quantum environments.
Entanglement generation in long-distance quantum networks is a difficult process due to resource limitations and the probabilistic nature of entanglement swapping. To maximize success probability, existing quantum routing algorithms employ computationally expensive solutions (e.g., linear programming) to determine which links to entangle and use for end-to-end entanglement generation. Such optimization methods, however, cannot meet the delay requirements of real-world quantum networks, necessitating swift yet efficient real-time optimization models. In this paper, we propose reinforcement learning (RL)-based models to determine which links to entangle and proactively swap to meet connection requests. We show that the proposed RL-based approach is 20x faster compared to linear programming. Moreover, we show that one can take advantage of the longevity of entanglements to (i) cache entangled links for future use and (ii) proactively swap entanglement on high-demand path segments, thereby increasing the likelihood of request success. Through comprehensive simulations, we demonstrate that caching unused entanglements leads to a 10-15% improvement in the performance of state-of-the-art quantum routing algorithms. Complementing caching with proactive entanglement swapping further enhances the request success rate by up to 52.55%.
We develop new routing algorithms for a quantum network with noisy quantum devices such that each can store a small number of qubits. We thereby consider two models for the operation of such a network. The first is a continuous model, in which entanglement between a subset of the nodes is produced continuously in the background. This can in principle allows the rapid creation of entanglement between more distant nodes using the already pre-generated entanglement pairs in the network. The second is an on-demand model, where entanglement production does not commence before a request is made. Our objective is to find protocols, that minimise the latency of the network to serve a request to create entanglement between two distant nodes in the network. We propose three routing algorithms and analytically show that as expected when there is only a single request in the network, then employing them on the continuous model yields a lower latency than on the on-demand one. We study the performance of the routing algorithms in a ring, grid, and recursively generated network topologies. We also give an analytical upper bound on the number of entanglement swap operations the nodes need to perform for routing entangled links between a source and a destination yielding a lower bound on the end to end fidelity of the shared entangled state. We proceed to study the case of multiple concurrent requests and show that in some of the scenarios the on-demand model can outperform the continuous one. Using numerical simulations on ring and grid networks we also study the behaviour of the latency of all the routing algorithms. We observe that the proposed routing algorithms behave far better than the existing classical greedy routing algorithm. The simulations also help to understand the advantages and disadvantages of different types of continuous models for different types of demands.
Quantum Data Networks (QDNs) typically leverage teleportation to reliably send data quantum bits (called qubits) to their destinations. To teleport a data qubit from Alice to Bob, one entanglement connection between Alice and Bob needs to be established. Accordingly, we have to establish as many entanglement connections as possible with limited quantum resources in order to maximize the network throughput. Conventional methods assume a known traffic matrix and calculate the paths to establish entanglement connections in one batch using a centralized algorithm. However, these methods are not scalable in large scale QDNs since it is time consuming to optimize the entanglement paths for a batch of requests, which may result in a long time slot duration and significantly reduce the network throughput. To address this issue, we propose an Online Entanglement Routing (OER) scheme which determines the entanglement paths for each request when it arrives. In addition, OER pursues work conservation and fairness among all requests in the QDNs. Through extensive simulations, we demonstrate that OER not only outperforms two representative heuristics by up to 61.27% and 52.79%, respectively, in terms of average request completion time, but also achieves a better fairness performance than these two counterparts.