In this paper, we consider the problem of concurrent multipath routing and end-to-end entanglement distribution for online resource allocation in quantum networks. We propose a heuristic algorithm to solve this problem, considering quantum memory, decoherence time, entanglement distribution probability, and fidelity. A time-slotted quantum network operation model is considered based on the cut-off decoherence time of quantum memories. The proposed heuristic is designed for a quantum network with noisy intermediate scale quantum (NISQ) constraints, including fixed quantum memory decoherence time, and probabilistic entanglement generation and swapping. It considers integer linear programming (ILP)-based and heuristic approaches to select multiple paths and resource allocation in the network. Simulations are performed to evaluate the performance of ILP and heuristic based approaches in terms of requests using multipath approach, blocking ratio, and computation time in a small sized quantum network with few requests. Next, the performance of heuristic based approaches are evaluated for a large problem size. The obtained results ensure that performance of ILP and heuristic based approaches are comparable, and multipath routing outperforms single path routing in terms of blocking ratio.
In this paper, we consider the problem of concurrent multipath routing and end-to-end entanglement distribution for online resource allocation in quantum networks. We propose a heuristic algorithm to solve this problem, considering quantum memory, decoherence time, entanglement distribution probability, and fidelity. A time-slotted quantum network operation model is considered based on the cut-off decoherence time of quantum memories. The proposed heuristic is designed for a quantum network with noisy intermediate scale quantum (NISQ) constraints, including fixed quantum memory decoherence time, and probabilistic entanglement generation and swapping. It considers integer linear programming (ILP)-based and heuristic approaches to select multiple paths and resource allocation in the network. Simulations are performed to evaluate the performance of ILP and heuristic based approaches in terms of requests using multipath approach, blocking ratio, and computation time in a small sized quantum network with few requests. Next, the performance of heuristic based approaches are evaluated for a large problem size. The obtained results ensure that performance of ILP and heuristic based approaches are comparable, and multipath routing outperforms single path routing in terms of blocking ratio.
In this study, we propose a resource allocation model for end-to-end (E2E) fidelity-guaranteed entanglement distribution considering novel path-based entanglement purification (PEP) model for online quantum networks. The proposed PEP model is developed by extending the existing link-based entanglement purification (LEP) model. A heuristic framework is designed for both the PEP and LEP models, consisting of two stages: (i) an E2E entanglement purification stage based on the PEP (or LEP) model, and (ii) a resource allocation stage. The resource allocation stage employs an integer linear programming (ILP) formulation for small-scale quantum networks with a limited number of requests, and a greedy-based heuristic for large-scale networks with higher request volumes. This heuristic algorithm incorporates entanglement distribution probability (EDP), entanglement swapping probability (ESP), and fidelity metrics to ensure feasibility under noisy intermediate-scale quantum (NISQ) constraints. An online traffic model is considered, where requests arrive according to a Poisson process, each with a fixed execution time and a variable deadline. A time-slotted operation framework is adopted for the quantum network. In each Bell-pair generation window, Bell pairs are generated and distributed between adjacent nodes. The duration of these windows is constrained by the decoherence time of the Bell pairs. Extensive simulations are conducted on the square-grid quantum networks of varying dimensions and parameter ranges, including different ranges of EDP, ESP, and fidelity values. The number of requests and capacity of edges (in terms of quantum channels) are also varied. Across all simulation setups, the proposed PEP model consistently outperforms the LEP model in terms of the blocking ratio (BR).
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.
This paper investigates resource allocation for entanglement distribution in multi-node, multi-channel quantum networks at the metropolitan scale. A multi-slot quantum network simulation framework is developed across physical and network layers, incorporating heterogeneous link characteristics, limited quantum memories, request queuing, retry mechanisms, and concurrent entanglement distribution. Based on this framework, a centralized scheduling architecture is proposed to coordinate quantum memories, communication channels, and routing paths for multiple simultaneous entanglement requests. Three classes of resource allocation strategies are evaluated: heuristic approaches, a mixed-integer linear programming (MILP) optimization method, and a Proximal Policy Optimization (PPO)-based reinforcement learning approach. The heuristic schemes reveal fundamental trade-offs between request delay and entanglement success rate: Dynamic Efficient minimizes delay, Success Enhancement improves success probability through adaptive multi-path allocation, and Static Efficient provides a balance between these objectives. The MILP approach achieves optimized resource allocation by jointly considering request handling and multi-path assignment, while the PPO-based method learns adaptive scheduling policies to improve overall performance. Simulation results demonstrate that these approaches provide different performance advantages in terms of request delay, entanglement success rate, capacity utilization, and request handling rate, highlighting the trade-offs between efficiency and reliability in metropolitan-scale quantum network scheduling.
Quantum networks facilitate numerous applications including secure communication and distributed quantum computation by performing entanglement distribution. For some multi-user quantum applications access to a shared multipartite state is required. We consider the problem of designing protocols for distributing such states, at an increased rate. For this, we propose three protocols that leverage multipath routing to increase the distribution rate for multi-user applications. The protocols are evaluated on quantum networks with NISQ constraints, including limited quantum memories and probabilistic entanglement generation. Simulation results show that the developed protocols achieve an exponential increase in the distribution rate of multipartite states compared to single path routing techniques, with a maximum increase of four orders of magnitude for the cases studied. Further, the relative increase in distribution rate was also found to improve for larger sets of users. When the protocols were tested in scaled-down real-world topologies, it was found that topology had a significant effect on the multipartite state distribution rates achieved by the protocols. Finally, we found that the benefits of multipath routing are maximum for short quantum memory decoherence times and intermediate values of entanglement generation probability. Hence, the protocols developed can benefit NISQ quantum network control and design.
Abstract Quantum network is a promising platform for many ground-breaking applications that lie beyond the capability of its classical counterparts. Efficient entanglement generation on quantum networks with relatively limited resources such as quantum memories is essential to fully realize the network’s capabilities, the solution to which calls for delicate network design and is currently at the primitive stage. In this study we propose an effective routing scheme to enable automatic responses for multiple requests of entanglement generation between source-terminal stations on a quantum lattice network with finite edge capacities. Multiple connection paths are exploited for each connection request while entanglement fidelity is ensured for each path by performing entanglement purification. The routing scheme is highly modularized with a flexible nature, embedding quantum operations within the algorithmic workflow, whose performance is evaluated from multiple perspectives. In particular, three algorithms are proposed and compared for the scheduling of capacity allocation on the edges of quantum network. Embodying the ideas of proportional share and progressive filling that have been well-studied in classical routing problems, we design another scheduling algorithm, the propagatory update method, which in certain aspects overrides the two algorithms based on classical heuristics in scheduling performances. The general solution scheme paves the road for effective design of efficient routing and flow control protocols on applicational quantum networks.
We consider the problem of distributing entangled multipartite states across a quantum network with improved distribution rate and fidelity. For this, we propose fidelity-aware multi-path routing protocols, assess their performance in terms of the rate and fidelity of the distributed Greenberger-Horne-Zeilinger (GHZ) states, and compare such performance against that of single-path routing. Simulation results show that the proposed multi-path routing protocols select routes that require more Bell states compared to single-path routing, but also require fewer rounds of Bell state generation. We also optimised the trade-off between distribution rate and fidelity by selecting an appropriate cutoff to the quantum memory storage time. Using such a cutoff technique, the proposed multi-path protocols can achieve up to an 8.3 times higher distribution rate and up to a 28% improvement in GHZ state fidelity compared to single-path routing. These results show that multi-path routing both improves the distribution rates and enhances fidelity for multipartite state distribution.