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International Conference on Quantum Communications, Networking, and Computing (QCNC)

Reinforcement Learning Based Proactive Entanglement Swapping for Quantum Networks

Tasdiqul Islam, Md Arifuzzaman, Engin Arslan

Published Jul 1, 2024• 11 citations
Publisher

Abstract

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.

DOI
10.1109/qcnc62729.2024.00030

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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.
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%.
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.
Quantum routing deals with identifying a set of quantum repeaters to use to create entanglement between distant endpoints. Previous approaches proposed shortest-path and linear programming methods to find a solution to this problem. While the shortest path approach results in suboptimal performance, linear programming takes too long to find a solution as the network size and constraints increase. In this paper, we apply Deep Q-Reinforcement Learning (DQRL) to optimize routing in quantum networks both in terms of execution time and performance. The proposed Quantum Routing Algorithm (QuRA) first chooses which request to schedule among all requests. It then determines which route to take for the selected request. Since the number of all possible routes can be very high, we developed a hop-by-hop decision making model to lower the complexity while still attaining high performance. Experiments show that the proposed QuRA outperforms existing solutions by up to 90% in success rate and up to 79% in execution time. These results highlight QuRA as a scalable and effective solution for intelligent routing in quantum networks.
We consider the problem of entanglement routing in heterogeneous quantum networks where each link and node may offer a different entanglement generation quality. We consider that each link offers a base rate and fidelity of entanglement generation. These base values can be altered by performing entanglement purification on each link. Furthermore, we consider that each repeater node offers a different probability of a successful entanglement swapping. With this setting, we design a reinforcement learning-based approach for flexible entanglement routing on future quantum networks. Specifically, we formulate the problem of entanglement routing in the Q-learning framework where the main objective of the agent is to deliver end-to-end entanglement while maximizing a user-specified objective function in the form of a weighted sum of achieve fidelity and rate. We demonstrate the efficacy and flexibility of developed framework by simulating random quantum networks where the random requests in fidelity maximizing, rate maximizing, or balanced mode are generated. We also numerically assess the effects of infrastructure developments and technology enhancements in future quantum networks.
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.
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.