Quantum Internet aims to enable quantum communication between any two points, offering applications such as quantum key distribution (QKD), distributed quantum computing, and entanglement networks. However, the current quantum technology presents challenges with low entanglement (EPR pairs) generation rates, limited quantum memory capacity, and decoherence rates that often lead to unusable EPR pairs due to low fidelity. This presents a significant challenge for tasks such as routing. In this paper, we tackle this challenge by introducing qRL, a quantum-aware routing protocol that utilizes reinforcement learning to optimize quantum routing decisions. We show that qRL consistently outperforms traditional methods by maintaining higher fidelity routes and request success rates in different network configuration scenarios.
Quantum Internet aims to enable quantum communication between any two points, offering applications such as quantum key distribution (QKD), distributed quantum computing, and entanglement networks. However, the current quantum technology presents challenges with low entanglement (EPR pairs) generation rates, limited quantum memory capacity, and decoherence rates that often lead to unusable EPR pairs due to low fidelity. This presents a significant challenge for tasks such as routing. In this paper, we tackle this challenge by introducing qRL, a quantum-aware routing protocol that utilizes reinforcement learning to optimize quantum routing decisions. We show that qRL consistently outperforms traditional methods by maintaining higher fidelity routes and request success rates in different network configuration scenarios.
The quantum internet holds transformative potential for global communication by harnessing the principles of quantum information processing. Despite significant advancements in quantum communication technologies, the efficient distribution of critical resources, such as qubits, remains a persistent and unresolved challenge. Conventional approaches often fall short of achieving optimal resource allocation, underscoring the necessity for more effective solutions. This study proposes a novel reinforcement learning-based adaptive entanglement routing framework designed to enable resource allocation tailored to the specific demands of quantum applications. The introduced QuDQN model utilizes reinforcement learning to optimize the management of quantum networks, allocate resources efficiently, and enhance entanglement routing. The model integrates key considerations, including fidelity requirements, network topology, qubit capacity, and request demands.
Abstract Entanglement-based quantum networks require intelligent routing mechanisms that not only maximize fidelity and connectivity but also ensure service differentiation under varying traffic conditions. In this work, we propose a novel QoS-aware traffic modeling and scheduling framework integrated into a reinforcement learning-based quantum routing agent. Unlike prior approaches that assume deterministic or uniform request arrival patterns, our model introduces stochastic and bursty traffic scenarios using Erlang and ON/OFF distributions to emulate realistic quantum workloads. Each entanglement request is assigned a set of QoS attributes, including fidelity requirements, time-to-live constraints, and priority levels. We then extend the reward function of the agent to consider deadline compliance and resource efficiency in addition to fidelity. Through simulation, we compare the proposed method against state-of-the-art baselines, including DQRA and Proactive RL, under both best-effort and QoS-prioritized scheduling modes. Results demonstrate that our approach maintains higher success rates and lower latency for high-priority requests, while preserving entanglement fidelity and efficient resource allocation across varying network loads. This study highlights the importance of integrating QoS mechanisms into quantum routing to support scalable and differentiated quantum services
Quantum communications are gaining momentum in finding applications in a wide range of domains, especially those require high-security data transmissions. On the other hand, machine learning has achieved numerous breakthrough successes in various application domains including networking. However, currently, machine learning is not as much utilized in quantum networking as in other areas. With such motivation, we propose a machine-learning-powered entanglement routing scheme for quantum networks that aims to accommodate maximum numbers of demands (source-destination pairs) within a time window. More specifically, we present a deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA utilizes an empirically designed deep neural network that observes the current network states to schedule the network’s demands which are then routed by a qubit-preserved shortest path algorithm. DQRA is trained towards the goal of maximizing the number of resolved requests in each routing window by using an explicitly designed reward function. Our experiment study shows that, on averse, DQRA is able to maintain a rate of successfully routed requests above 80% in a qubit-limited grid network, and about 60% in extreme conditions i.e. each node can act as a repeater exactly once within a window. Furthermore, we show that the complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum networks.
Efficient routing in entanglement distribution networks is a challenging task that requires the coordinated allocation of quantum memories, repeaters, and quantum operations such as entanglement swapping and distillation across dynamic network conditions. In this paper, we present a Deep Q-Learning (DQN) Two-Stage Routing strategy designed to improve both path and quantum operations selection. Our approach enables the agent to make decisions over a diverse action space, including request scheduling, multipath routing, and the strategic application of quantum operations to maximize entanglement success and resource efficiency. We further extend DQN to integrate Double DQN, Dueling DQN, and Prioritized Experience Replay (PER) architectures. Simulation results indicate that our approach significantly outperforms baseline methods regarding request success rate, especially in large-scale networks under strict fidelity requirements.
Entanglement generation and swapping is a difficult process due to probabilistic nature of quantum mechanics. To overcome this issue, existing quantum routing algorithms try to create entanglement on multiple paths between source and destination. Although it is possible to save entangled qubits on unused links using quantum memories, the quantum routing algorithms discard them and try creating new entanglement in each time slot. In this work, we leverage the longevity of entanglement and introduce two enhancements to improve the performance of existing routing algorithms: (i) The generation and caching of entanglements across multiple time slots, and (ii) the proactively executing entanglement swapping for frequently utilized links, increasing the success rate of future requests. Since entanglement caching reduces available quantum memory on quantum repeaters, we apply reinforcement learning to identify the optimal set of segments that are likely to be used in the future. Through comprehensive simulations, we demonstrate that caching unused entanglements lead to 10 - 15% improvement on the performance of quantum routing algorithms REPS and SEER. Complementing the caching with reinforcement learning based proactively entanglement swapping leads to further performance improvements. Specifically, using Q-Learning to decide on which segments to proactively swap leads to 52.55% and 41.79% improvement for REPS and SEER algorithms, respectively. Deep Q-Learning outperforms Q-Learning with 61.43% and 48.27% improvements rates.
Efficient routing in entanglement distribution networks is a challenging task that requires the coordinated allocation of quantum memories, repeaters, and quantum operations such as entanglement swapping and distillation across dynamic network conditions. In this paper, we present a Deep Q-Learning (DQN) Two-Stage Routing strategy designed to improve bothpath and quantum operations selection. Our approach enables the agent to make decisions over a diverse action space, including request scheduling, multipath routing, and the strategic application of quantum operations to maximize entanglementsuccess and resource efficiency. We further extend DQN to integrate Double DQN, Dueling DQN, and Prioritized ExperienceReplay (PER) architectures. Simulation results indicate that our approach significantly outperforms baseline methods regardingrequest success rate, especially in large-scale networks under strict fidelity requirements.