This paper addresses resource allocation for entanglement distribution in multi-channel quantum networks. A system model is proposed that integrates a multi-channel quantum network architecture with heterogeneous link characteristics and user-centric entanglement request handling, including queuing and retry mechanisms. Classical allocation methods for assigning channels and quantum processors to generate entanglement between end nodes are implemented, including the Dynamic Efficient algorithm, Static Efficient algorithm, and the Success Enhancement algorithm. In addition, a Proximal Policy Optimization (PPO)-based allocation approach driven by a reward function is proposed. These methods are evaluated through multi-slot simulations using metrics such as request delay, total number of successful entanglement requests, network capacity utilization, and the entanglement request handling rate. The results show that Dynamic Efficient achieves the lowest delay, while Success Enhancement improves the number of successful requests through multipath allocation. The PPO-based method provides the best overall balance by improving capacity utilization and achieving both low delay and a high number of successful entanglement requests.
This paper addresses resource allocation for entanglement distribution in multi-channel quantum networks. A system model is proposed that integrates a multi-channel quantum network architecture with heterogeneous link characteristics and user-centric entanglement request handling, including queuing and retry mechanisms. Classical allocation methods for assigning channels and quantum processors to generate entanglement between end nodes are implemented, including the Dynamic Efficient algorithm, Static Efficient algorithm, and the Success Enhancement algorithm. In addition, a Proximal Policy Optimization (PPO)-based allocation approach driven by a reward function is proposed. These methods are evaluated through multi-slot simulations using metrics such as request delay, total number of successful entanglement requests, network capacity utilization, and the entanglement request handling rate. The results show that Dynamic Efficient achieves the lowest delay, while Success Enhancement improves the number of successful requests through multipath allocation. The PPO-based method provides the best overall balance by improving capacity utilization and achieving both low delay and a high number of successful entanglement requests.
Quantum networks must deliver end-to-end entanglement whose fidelity meets application requirements, which typically requires entanglement purification along multi-hop paths. Existing scheduling approaches either purify at the link level or optimize end-to-end fidelity for a static set of requests, and do not capture concurrent requests competing for network resources over time. This paper studies concurrent entanglement purification scheduling in a time-slotted quantum network with heterogeneous links, in which requests compete for link capacity and quantum memories, and each request may be served by multiple parallel paths for increased reliability. We formulate a mixed-integer linear programming (MILP) scheduler that maximizes the number of scheduled requests, with max-min path allocation as a fairness criterion, and develop reinforcement learning (RL) schedulers based on DQN, PPO, and SAC, whose rewards balance request success against Bell-pair consumption. Simulations under varying network load, topologies and target fidelity thresholds show that the RL schedulers achieve the highest success rates, improving on MILP by up to 6% and on Progressive Filling and Greedy heuristics by 16.5% and 19.5% respectively, at the cost of higher Bell pair consumption and the deferral of a small fraction of requests. MILP, in contrast, achieves the highest fairness and the lowest Bell pair consumption in most scenarios.
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.
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.
This paper presents a novel algorithm for optimizing the allocation of entangled resources within quantum networks. The core of the approach lies in employing a reinforcement learning (RL) framework to dynamically manage and distribute entanglement, addressing the inherent challenges of maximizing utilization and enhancing quantum communication efficiency. Unlike traditional static allocation methods, the proposed RL agent learns optimal allocation strategies through iterative interaction with a simulated quantum network environment. The algorithm considers factors such as node connectivity, qubit availability, and communication delays to minimize resource wastage and improve overall network performance. The key performance indicator (KPI) is the entanglement fidelity between communicating nodes. The developed algorithm offers a promising pathway towards realizing the full potential of quantum networks by intelligently managing their most valuable asset: entanglement. The primary goal is to achieve higher fidelity entanglement distribution and improved communication rates. The effectiveness of the algorithm is evaluated through simulations, demonstrating its ability to outperform existing allocation schemes in various network topologies and operational scenarios.
Quantum entanglement is the cornerstone of quantum network communication, yet the qubit resources required to establish it are severely limited. Moreover, users within the network impose strict delay constraints on entanglement requests due to various factors, such as the real-time requirements of upper-layer applications or the finite coherence time of userside quantum memories. Consequently, effectively scheduling scarce qubit resources to serve these time-sensitive requests presents a significant challenge. This difficulty is exacerbated by the partial observability of quantum systems and the time-varying nature of qubit generation rates, rendering traditional scheduling algorithms ineffective. Furthermore, while Deep Reinforcement Learning (DRL) offers a promising avenue for quantum network optimization, existing research primarily focuses on network-layer routing, overlooking the finer-grained entanglement scheduling problem subject to strict delay and resource constraints. To bridge this gap, we propose the Deep Quantum Request Scheduler (DQRS), a novel DRL-based algorithm designed to maximize service quality under these dual constraints. Specifically, DQRS integrates a spatio-temporal branching architecture and virtual action slots within an ActorCritic framework. This design enables robust scheduling with continuous actions while adaptively managing fluctuating qubit budgets. Extensive simulation results demonstrate that DQRS achieves up to 44 % performance improvement over traditional methods and a 4 % gain compared to state-of-the-art DRL benchmarks across various resource constraints and observability settings, validating the robustness of the proposed approach.
With the continuous expansion of quantum communication networks and the increasing diversity of their business types, how to achieve efficient configuration under limited quantum resources has become the key to improving system performance. The quantum resources described in this article mainly include entanglement pairs, quantum bits, and channel capacity. Traditional optimization methods typically rely on precise mathematical models, which are difficult to adapt to dynamically evolving network environments and susceptible to noise interference. Therefore, this study proposes a machine learning based framework for optimizing the allocation of quantum communication resources. This framework integrates deep reinforcement learning and graph neural network technology, enabling collaborative optimization of entanglement allocation, routing selection, and time slice scheduling between nodes. Three typical quantum communication scenarios were constructed and tested in a simulation environment, including static topology, dynamic links, and high noise environments. The proposed method was compared and analyzed with integer linear programming, heuristic greedy algorithms, and rule-based strategies. Experimental data shows that this research method achieved an improvement of approximately 12% to 18% in average fidelity retention, a 15% to 22% increase in resource utilization, and a 20% reduction in latency. This study provides useful reference for the intelligent resource management of large-scale quantum networks in the future.