Long-distance quantum link generates entanglement faster than it is lost

Saw Nang Paing, Trung Q. Duong, Hyundong Shin

Long-distance quantum communication holds significant promises for information transmission over extremely large distances. However, ensuring communication security over extensive distances poses considerable challenges. In this article, we propose a new approach to long-distance quantum communication that is carried out in an information-carrying particle-free manner, known as counterfactual quantum communication. Our approach extends the counterfactual transmission signal by leveraging quantum repeaters situated between distant nodes, facilitating counterfactual entanglement swapping. Due to the absence of information-carrying particles in the channel, it enhances the security of information transmission. We demonstrate that our proposed protocol not only establishes the direct communication link between distant nodes, but also generates redundant communication links between neighboring nodes, enhancing resilience against primary link failures. Furthermore, we provide numerical simulations of C-Swap gate and counterfactual long-distance quantum communication scenarios to quantify success probabilities where the photon is not lost during the counterfactual communication.

Sami Ul Haq, Farhan Saif

Tasdiqul Islam, Arifuzzaman, Md, Engin Arslan

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%.

Tasdiqul Islam, Rasman Mubtasim Swargo, Md Arifuzzaman

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.

Guifré Vidal