Quantum networks (QNs) gradually gain significant attention due to their higher security compared with classical networks. Conventional approaches in QN routing often aggregate multiple requests into a batch before determining their routing paths. However, this approach may overlook the limited lifetime of qubits, resulting in critical decoherence. In this paper, we present a new online entanglement routing architecture with an online optimization problem and propose a novel [1, O(log |V|)]-competitive algorithm supporting online requests with admission control, aiming to maximize the number of admitted requests. Finally, extensive simulation results show that our algorithm can outperform the existing approaches by up to 98%.
Quantum networks (QNs) gradually gain significant attention due to their higher security compared with classical networks. Conventional approaches in QN routing often aggregate multiple requests into a batch before determining their routing paths. However, this approach may overlook the limited lifetime of qubits, resulting in critical decoherence. In this paper, we present a new online entanglement routing architecture with an online optimization problem and propose a novel [1, O(log |V|)]-competitive algorithm supporting online requests with admission control, aiming to maximize the number of admitted requests. Finally, extensive simulation results show that our algorithm can outperform the existing approaches by up to 98%.
Quantum nodes exchange qubits to achieve largescale quantum computing applications in a quantum network (QN). However, due to environmental influences, entangled links used for data transmission would decohere over time. Besides, the scarcity of quantum memory in each quantum node necessitates frequent communication between nodes, leading to significant variations in the traffic matrix. Thus, providing real-time services in QNs becomes particularly important. To this end, this paper presents a new optimization problem and proposes a novel online algorithm, which combines the game-theoretic analysis results with the linear programming (LP) by the primal-dual technique. Finally, the simulation results demonstrate that the proposed algorithm outperforms the other baselines by up to 90 %.
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.
With the enhanced security offered by quantum teleportation, quantum networks (QNs) are gradually gaining significant attention. However, previous research on QNs often exhausts all network resources to serve requests, hence overlooking the scarce resources in QN. This oversight easily leads to resource wastage and requests competing for limited resources. To address the above issues, this paper introduces a new optimization problem named EINS, which minimizes the maximum resource utilization and finds routing paths for each request through certain intermediate nodes. This design cleverly divides the routing path into segments to enhance routing path diversity. To solve the EINS, we design a novel algorithm named GOAL, which provides anO(log∣V∣)bound for constraint deviation. It efficiently and equitably distributes requests, ensuring optimal network utilization. Finally, the simulation results manifest that GOAL can outperform the existing methods by up to 99%.
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.
There will be a fast-paced shift from conventional network systems to novel quantum networks that are supported by the quantum entanglement and teleportation, key technologies of the quantum era, to enable secured data transmissions in the next-generation of the Internet. Despite this prospect, migration to quantum networks cannot happen all at once, especially when it comes to quantum routing. In this paper, we focus on the maximizing entanglement routing rate (MERR) problem, which aims to determine entangled routing paths for the maximum number of demands in the quantum network while meeting the network's fidelity. To tackle this problem, we first formulate the MERR problem using an integer linear programming (ILP) model. We then leverage the method of linear programming relaxation to devise two efficient algorithms, including the half-based rounding algorithm (HBRA) and the randomized rounding algorithm (RRA) with a provable approximation ratio for the objective function. Furthermore, to address the challenge of the combinatorial optimization problem in big scale networks, we also propose the path-length-based approach (PLBA) to solve the MERR problem. Finally, we evaluate the performance of our algorithms and show up the success of maximizing the entanglement routing rate.
There will be a fast-paced shift from conventional network systems to novel quantum networks that are supported by the quantum entanglement and teleportation, key technologies of the quantum era, to enable secured data transmissions in the next-generation of the Internet. Despite this prospect, migration to quantum networks cannot be done at once, especially on the aspect of quantum routing. In this paper, we study the maximizing entangled routing rate (MERR) problem. In particular, given a set of demands, we try to determine entangled routing paths for the maximum number of demands in the quantum network while meeting the network's fidelity. We first formulate the MERR problem using an integer linear programming (ILP) model to capture the traffic patent for all demands in the network. We then leverage the theory of relaxation of ILP to devise two efficient algorithms including HBRA and RRA with provable approximation ratios for the objective function. To deal with the challenge of the combinatorial optimization problem in big scale networks, we also propose the path-length-based approach (PLBA) to solve the MERR problem. Using both simulations and an open quantum network simulator platform to conduct experiments with real-world topologies and traffic matrices, we evaluate the performance of our algorithms and show up the success of maximizing entangled routing rate.