Quantum networks establish end-to-end quantum communication by utilizing probabilistic quantum processes, including entanglement distribution between remote nodes and entanglement swapping at repeater nodes. While previous studies have aimed to maximize the number of entanglement connections under the probabilistic nature, physical link failures provide an additional challenge to ensuring consistent entanglement connections. To address this issue, a path provisioning model should be designed to maintain a high entanglement path success probability even in the presence of link failure. This paper proposes a provisioning model to find link- disjoint entanglement paths in quantum networks, considering both link-level transmission success probabilities and node-level entanglement swapping success probabilities. The objective of the proposed model is to maximize the entanglement path success probability for survivability under link failure. We formulate this model as an integer linear programming problem. The numerical results indicate that the proposed model achieves higher survivability than baseline algorithms.
Quantum networks establish end-to-end quantum communication by utilizing probabilistic quantum processes, including entanglement distribution between remote nodes and entanglement swapping at repeater nodes. While previous studies have aimed to maximize the number of entanglement connections under the probabilistic nature, physical link failures provide an additional challenge to ensuring consistent entanglement connections. To address this issue, a path provisioning model should be designed to maintain a high entanglement path success probability even in the presence of link failure. This paper proposes a provisioning model to find link- disjoint entanglement paths in quantum networks, considering both link-level transmission success probabilities and node-level entanglement swapping success probabilities. The objective of the proposed model is to maximize the entanglement path success probability for survivability under link failure. We formulate this model as an integer linear programming problem. The numerical results indicate that the proposed model achieves higher survivability than baseline algorithms.
Quantum networks establish end-to-end quantum communication by utilizing probabilistic quantum processes, including entanglement distribution between remote nodes and entanglement swapping at repeater nodes. While previous studies have aimed to maximize the number of entanglement connections under the probabilistic nature, physical link failures provide an additional challenge to ensuring consistent entanglement connections. To address this issue, a path provisioning model should be designed to maintain a high entanglement path success probability even in the presence of link failure. This paper proposes a survivable provisioning model to find link-disjoint entanglement paths in quantum networks, considering both link-level transmission success probabilities and node-level entanglement swapping success probabilities. The objective of the proposed model is to maximize the entanglement path success probability for survivability under link failure. We formulate this model as an integer linear programming problem. We prove that the decision version of the entanglement link-disjoint path finding problem in the proposed model is NP-complete. The numerical results indicate that the proposed model achieves up to 11.8% higher survivability than baseline algorithms. While the proposed model incurs longer computation time than the baselines, the required time is still within a feasible range; in our experiments, the maximum computation time is 4.1 seconds. Network operators perform path provisioning in advance of service initiation.
Quantum networks are designed to transmit quantum bits (qubits) among quantum devices to enable new network resources for the applications. Entanglement distribution and entanglement swapping are fundamental procedures that are required in several network operations. However, they are probabilistic operations, which can lead to severe network performance degradation. This article investigates the engineering problem of resource allocation in quantum networks, considering factors like entanglement distribution probability, quantum memory characteristics, and fidelity. We model this as an optimization model to obtain an optimal solution. In particular, we formulate an integer linear programming (ILP) and develop a heuristic algorithm, aiming to minimize the number of required entangled qubit pairs (Bell pairs or EPR pairs) in any adjacent pair in the quantum network. Extensive simulations are performed to compare the performance of proposed ILP and heuristic. In all the cases, the heuristic produces a comparable solution to the optimal one. Simulation results ensure that the value of maximum utilized Bell pairs in a quantum network highly depends on the value of the probability of entangled pairs established, considering the time in the quantum memory besides the number of incoming requests.
Quantum communication using qubits based on the principle of entangled photons is a promising solution to improve network security. However, it is difficult to successfully create an entanglement link or connection between two nodes, especially when they are far apart from each other. In addition, only one qubit can be exchanged over an established entanglement connection, resulting in a low throughput.In this paper, we propose Redundant Entanglement Pro-visioning and Selection (REPS) to maximize the throughput for multiple source-destination (SD) pairs in a circuit-switched, multi-hop quantum network. REPS has two distinct features: (i). It provisions backup resources for extra entanglement links between adjacent nodes for failure-tolerance; and (ii). It provides flexibility in selecting successfully created entanglement links to establish entanglement connections for the SD pairs to achieve network-wide optimization. Extensive analysis and simulations show that REPS can achieve optimal routing with a high probability, and improves the throughput by up to 68.35% over the highest-performing algorithms in existence. In addition, it also improves the fairness among the SD pairs in the networks.
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 consider the problem of multi-path entanglement distribution to a pair of nodes in a quantum network consisting of devices with non-deterministic entanglement swapping capabilities. Multi-path entanglement distribution enables a network to establish end-to-end entangled links across any number of available paths with pre-established elementary (link-level) entanglement. Probabilistic entanglement swapping, on the other hand, limits the amount of entanglement that is shared between the nodes; this is especially the case when, due to architectural and other practical constraints, swaps must be performed in temporal proximity to each other. Limiting our focus to the case where only bipartite entangled states are generated across the network, we cast the problem as an instance of generalized flow maximization between two quantum end nodes wishing to communicate. We propose a mixed-integer quadratically constrained program (MIQCP) to solve this flow problem for networks with arbitrary topology. We then compute the overall network capacity, defined as the maximum number of EPR states distributed to users per time unit, by solving the flow problem for all possible network states generated by probabilistic entangled link presence and absence, and subsequently by averaging over all network state capacities. The MIQCP can also be applied to networks with multiplexed links – ie, links capable of generating multiple EPR pairs per time unit – by converting the original network state's graph into an equivalent one with unit link capacities. While our approach for computing the overall network capacity has the undesirable property that the total number of states grows exponentially with link multiplexing capability, it nevertheless yields an exact solution that serves as an upper bound comparison basis for the throughput performance of more easily-implementable yet non-optimal entanglement routing algorithms. We apply our capacity computation method to several multiplexed and non-multiplexed networks, including a topology based on SURFnet – a backbone network used for research purposes in the Netherlands – for which real fiber length data is available.
Entanglement generation in long-distance quantum networks is challenging because resources are limited and entanglement swapping is probabilistic. To maximize the rate of successful requests, existing quantum routing algorithms often rely on computationally expensive methods such as Integer Linear Programming (ILP) to determine which links to entangle and use for end-to-end entanglement generation. However, these approaches fail to meet the latency requirements of real-world quantum networks. In this study, we propose a Reinforcement Learning (RL)-based model that determines which links to entangle in each time slot, replacing the slow ILP-based link-selection phase used in prior algorithms. The proposed Deep Q-learning model is up to 19.2× faster than linear programming in link-selection while maintaining comparable routing performance. The RL link-selection model alone matches ILP in request success rate; combining RL link selection with entanglement caching and proactive swapping raises throughput by up to 52.55% over ILP. Overall, our approach achieves success rates exceeding state-of-the-art solutions while reducing execution time by more than an order of magnitude. Experiments on a synthetic 50-node Waxman topology and the real-world 54-node SURFnet core topology confirm that these gains generalize across network structures, providing a practical path toward scalable quantum routing.