Quantumnetworking is emerging as a new research area to explore the opportunities of interconnecting quantum systems through end-to-end entanglement of qubits at geographical distance via quantum repeaters. A promising architecture has been proposed in the literature that decouples entanglement between adjacent quantum nodes/repeaters from establishing end-to-end paths by adopting a time slotted approach. Within this model, we destructure further end-to-end path establishment into two subproblems: path selection and scheduling. The former is set to determine the best repeaters to connect two end nodes, provided that all their local entanglements have succeeded. On the other hand, scheduling is concerned with deciding, which pairs of end nodes are served in the current time slot, while the others remain queued for later time slots. Unlike path selection, scheduling has not been investigated so far in the literature, particularly in presence of quantum noise, which makes both problems even more challenging. In this article, we propose to address it via a general framework of heuristic algorithms, for which we propose three illustrative instances with the objective of keeping the application delay small while achieving a good system utilization, in terms of high entanglement rate and fidelity of remotely entangled qubits. The system proposed is evaluated extensively via event-driven quantum network simulations, with noisy repeaters, in different node topologies under a Poisson arrival of requests from quantum applications. The results show the existence of a fundamental tradeoff between system- and application-level metrics, such as fairness versus entanglement and fidelity, which lays the foundations for further studies in this thriving research area.
Quantumnetworking is emerging as a new research area to explore the opportunities of interconnecting quantum systems through end-to-end entanglement of qubits at geographical distance via quantum repeaters. A promising architecture has been proposed in the literature that decouples entanglement between adjacent quantum nodes/repeaters from establishing end-to-end paths by adopting a time slotted approach. Within this model, we destructure further end-to-end path establishment into two subproblems: path selection and scheduling. The former is set to determine the best repeaters to connect two end nodes, provided that all their local entanglements have succeeded. On the other hand, scheduling is concerned with deciding, which pairs of end nodes are served in the current time slot, while the others remain queued for later time slots. Unlike path selection, scheduling has not been investigated so far in the literature, particularly in presence of quantum noise, which makes both problems even more challenging. In this article, we propose to address it via a general framework of heuristic algorithms, for which we propose three illustrative instances with the objective of keeping the application delay small while achieving a good system utilization, in terms of high entanglement rate and fidelity of remotely entangled qubits. The system proposed is evaluated extensively via event-driven quantum network simulations, with noisy repeaters, in different node topologies under a Poisson arrival of requests from quantum applications. The results show the existence of a fundamental tradeoff between system- and application-level metrics, such as fairness versus entanglement and fidelity, which lays the foundations for further studies in this thriving research area.
In this paper, a novel Deep Q-Network (DQN) based scheduling method to optimize delay time and fairness among entanglement requests in quantum repeater networks is proposed. The scheduling of requests determines which pairs of end nodes should be entangled during the current time slot, while other pairs are placed in a queue for future slots. However, existing research on quantum networking often relies on simple statistical models to capture the behavior of quantum hardware, such as the failure rate of establishing entanglement. Moreover, current quantum simulators do not support network behaviors, including handling, pending, and dropping requests. To bridge the gap between quantum deployments and network behaviors, in this paper a dynamic network model is presented, encompassing quantum simulations, random topologies, and user modeling. The DQN based scheduling scheme allows us to balance the conflicting objectives of minimizing delay time and maximizing fairness among these entanglement requests. The proposed technique was evaluated using simulations, with results showing that the proposed DQN achieves higher performance compared to Greedy, Proportional fair and FIFO scheduling schemes.
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.
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 are emerging as a rapidly developing field with diverse applications. Current research mainly focuses on maximizing the number of entangled pairs between remote nodes or optimizing the success probability of entanglement requests within a given time slot. However, practical applications often necessitate consideration of request deadlines and tolerable delays. This paper addresses the problem of scheduling quantum links to fulfill entanglement requests under the deadline constraints, aiming to maximize the number of satisfied requests. We formulate the problem using an integer linear program based on the principles of flow networks, whose solution is optimal. In addition, a greedy algorithm is proposed, inspired by the idea that the request with the earliest deadline need to be fulfilled first. We then carry out simulation to evaluate the performance of the proposed methods. The results demonstrate that the solution yielded by the greedy algorithm can approach the optimal solution.
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.
Quantum networks, as a pivotal platform to support numerous quantum applications, have the potential to far exceed traditional communication networks. Establishing end-to-end entanglement connections with guaranteed fidelity is a key prerequisite for realizing the functionality of quantum networks. Entanglement purification techniques are commonly used in the entanglement distribution process to provide end-to-end entanglement connections that meet the fidelity requirements. Since the purification operation sacrifices a certain amount of entanglement resources, it is critical and challenging to efficiently utilize the scarce entanglement resources in quantum networks with concurrent competing requests. To address this problem, we propose a novel demand-oriented resource scheduling (DRS) algorithm. Considering the overall network demand, DRS introduces a congestion factor to evaluate the resource demand of each link, and performs purification operations sequentially based on the congestion level of the links, thus avoiding the excessive consumption of entanglement resources of bottleneck links. Extensive simulation results show that the DRS algorithm can achieve higher network throughput with similar resource conversion rates compared to traditional resource allocation schemes. Our work provides a new scheme for the resource scheduling problem under concurrent competing requests, which can promote the further development of existing entanglement routing techniques.