The conventional entanglement routing algorithms favor the shorter path to maximize the throughput, which leads to the starvation problem in Quantum Key Distribution (QKD). In this paper, we focus on solving the starvation problem in QKD. We design a starvation-aware entanglement routing algorithm, which gives higher priority to the QKD task facing the starvation problem. Specifically, we prioritize the QKD task with a longer Key Distribution Time (KDT). The entangled qubit pairs will first be assigned to the path with the highest priority. Simulation results show that our proposed starvation-aware entanglement routing algorithm can reduce the maximum KDT by up to ten times.
The conventional entanglement routing algorithms favor the shorter path to maximize the throughput, which leads to the starvation problem in Quantum Key Distribution (QKD). In this paper, we focus on solving the starvation problem in QKD. We design a starvation-aware entanglement routing algorithm, which gives higher priority to the QKD task facing the starvation problem. Specifically, we prioritize the QKD task with a longer Key Distribution Time (KDT). The entangled qubit pairs will first be assigned to the path with the highest priority. Simulation results show that our proposed starvation-aware entanglement routing algorithm can reduce the maximum KDT by up to ten times.
Quantum networks, with applications like Quantum Key Distribution (QKD), are gaining significant attention. However, their implementation faces challenges due to low entanglement generation success rates and quantum decoherence. Recent quantum technology advancements have extended entanglement memory lifetimes to one minute, termed cutoff, opening new opportunities for entanglement routing. We propose the Adaptive Entanglement Routing (AER) algorithm, which optimizes resource utilization to improve the success probability of serving entanglement and ultimately reduce the time needed for entanglement establishment. AER includes two phases: 1) determine redundant paths based on load and 2) utilize shared entanglements for entanglement swapping. Moreover, we design the highest-success-path (HSP) algorithm to maximize the success probability of entanglement routing with limited quantum memory. These innovative routing algorithms significantly reduce entanglement request failures, resulting in up to 70% reduction in average waiting times.
New capabilities enabled by quantum networks, e.g. quantum key distribution, rely on the concurrent quantum entanglements via multiple untrusted repeaters between the source-destination pairs, which is known as quantum routing. Existing quantum routing algorithms pre-assign the free qubits to specific paths, in order to avoid resource contention in the process of entanglement swapping. Since not all pre-assigned qubits can be utilized, the resource fragmentation problem caused can limit the network throughout. In this paper, a new fragmentation-aware entanglement routing (FER) algorithm is proposed. With FER, the entangled qubits are shared by all the paths, and the resource contention problem is solved by a priority connection mechanism. Simulation results show that FER consistently outperforms existing algorithms under different network settings.
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.
Entanglement routing establishes remote entanglement connection between two arbitrary nodes, which is one of the most important functions in quantum networks. The existing routing mechanisms mainly improve the robustness and throughput facing the failure of entanglement generations, which, however, rarely include the considerations on the most important metric to evaluate the quality of connection, entanglement fidelity. To solve this problem, we propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (SD) pairs in quantum networks. In our proposal, we first consider the single S-D pair scenario and design an iterative routing algorithm, Q-PATH, to find the optimal purification decisions along the routing path with minimum entangled pair cost. Further, a low-complexity routing algorithm using an extended Dijkstra algorithm, Q-LEAP, is designed to reduce the computational complexity by using a simple but effective purification decision method. Then we consider the common scenario with multiple S-D pairs and design a greedy-based algorithm considering resource allocation and rerouting process for multiple routing requests. To verify the effectiveness and superiority of the proposed algorithms, extensive simulations are conducted, and the simulation results show that the proposed algorithms not only can provide fidelity-guarantee routing solutions, but also has superior performance in terms of throughput, fidelity of end-to-end entanglement connection, and resource utilization ratio, compared with the existing routing scheme.
Entanglement routing establishes remote entanglement connection between two arbitrary nodes, which is one of the most important functions in quantum networks. The existing routing mechanisms mainly improve the robustness and throughput facing the failure of entanglement generations, which, however, rarely include the considerations on the most important metric to evaluate the quality of connection, entanglement fidelity. To solve this problem, we propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (S-D) pairs in quantum networks. In our proposal, we first consider the single S-D pair scenario and design an iterative routing algorithm, Q-PATH, to find the optimal purification decisions along the routing path with minimum entangled pair cost. Further, a low-complexity routing algorithm using an extended Dijkstra algorithm, Q-LEAP, is designed to reduce the computational complexity by using a simple but effective purification decision method. Finally, we consider the common scenario with multiple S-D pairs and design a greedy-based algorithm considering resource allocation and re-routing process for multiple routing requests. Simulation results show that the proposed algorithms not only can provide fidelity-guaranteed routing solutions, but also has superior performance in terms of throughput, fidelity of end-to-end entanglement connection, and resource utilization ratio, compared with the existing routing scheme.
Quantum teleportation via quantum entanglement enables high-security communications in networks. However, if two quantum nodes are far away, it may be difficult to create an entangled path due to the low success probability. Besides, existing approaches neglect social relations among nodes' owners. In this paper, we propose a new framework SEER to minimize the waiting time of all source-destination (SD) pairs' requests. SEER has two promising features: 1) Social-relation Consideration. SEER makes the first attempt to select trusted owners' nodes via social networks as intermediate nodes for requests to temporarily store data qubits to increase the success probability. 2) Starvation Mitigation. SEER divides long SD pairs and slices resources reasonably to remedy starvation due to their low success probability. To this end, we design RATE and PLAN to find the proper intermediate nodes and reduce the average waiting time. Simulation results manifest SEER outperforms others by 37%.