Jul 2026· International Conference on Computer Communications and Networks· pp. 1-6· 0 citations· 24 references
Abstract
Quantum networks are expected to support a wide range of emerging applications by enabling entanglement routing among remote nodes. Existing studies on quantum entanglement routing primarily focus on either ground-based networks or satellite-assisted networks. Moreover, bipartite and multipartite entanglement demands are typically considered separately, whereas practical quantum networks must simultaneously support heterogeneous entanglement demands. To fill this gap, this paper investigates an integrated ground–satellite quantum network that jointly considers both network architectures and demand types, serving both bipartite (Bell) and multipartite (GHZ) entanglement demands. We propose a unified optimization framework that jointly determines entanglement routing, resource allocation, and fusion-node selection for GHZ demands, with the objective of maximizing the expected entanglement delivery rate. To prevent multipartite entanglement requests from being consistently dominated by Bell demands, we incorporate an ϵ-constraint that enforces a minimum level of GHZ demand admission. Extensive simulation results demonstrate that the proposed approach significantly improves the total expected throughput while ensuring a higher admission rate for GHZ entanglement demands than existing routing schemes.
As practical quantum networks approach large-scale deployment, the need for efficient user-to-user frequency allocation is increasing, yet current approaches only provide partial solutions to the routing and spectrum allocation problem for an arbitrary quantum network. We address this challenge for repeater-less flex-grid quantum networks based on hyperentangled photons using an efficient three-stage pipeline combining leading tools in classical networking with recent advances in numerical optimization. First, double instantiations of Yen's algorithm obtain low-loss route candidates between each pair of users and the entanglement sources. Second, the advanced process optimizer (APOPT) obtains frequency channel allocations that maximize distribution rates under fidelity constraints. Finally, the constraint programming solver using satisfiability methods (CP-SAT) assigns specific frequency bins to each link, ensuring that there is no contention between frequencies from different sources. We numerically demonstrate this approach on a representative ring network and a Manhattan incumbent local exchange carrier topology, realizing significant improvements over prior genetic algorithm approaches in speed, accuracy, and scalability. Overall, this pipeline provides an efficient heuristic workflow for optimizing broadband entanglement distribution, applicable to arbitrarily connected quantum networks integrated within the existing lightwave infrastructure.
Zachary Goisman, M. L. Stevens, Maxwell Goisman et al.· 0 citations
In this study, for the first time, a novel routing and purification approach for quantum networks is presented, using the end-to-end (E2E) relative entropy of coherence (REC) together with E2E fidelity to determine the purification level and the feasibility of candidate paths.
H. S. D. Tunç, Joy Halder, Azita Hajizade et al.· Scientific Reports· 0 citations
A metropolitan-scale entanglement-based quantum communication network enabled by a quantum reconfigurable optical add-drop multiplexer (q-ROADM), which dynamically distributes polarisation-entangled photon pairs from a broadband source to six users over deployed campus and metropolitan fibre, is demonstrated.
Rui Wang, Marcus J. Clark, O. Alia et al.· 1 citation
Entanglement-based networks provide a scalable framework for multiuser quantum communication by passively routing spectrally correlated photon pairs across interconnected nodes. Several wavelength-allocation schemes have been demonstrated experimentally, but these designs do not yet give a general way to determine how spectral use, receiver load, repeated connections, and fan-out constrain one another. We address this problem through the network's connectivity graph, where the wavelength assignment becomes a resource-optimization problem. For one-sided fan-out, assigning each link to a center and grouping links with the same center gives an exact optimization for arbitrary networks and fan-out limits. We solve this for complete networks and for complete networks in which every user has one excluded partner. Allowing both conjugate wavelengths to fan out changes the resource landscape: a balanced binary hierarchy attains the minimum spectral-layer count for a complete network while reducing the maximum receiver load to logarithmic in the number of users. An eight-user complete network then makes explicit the competing roles of spectral efficiency, receiver load, redundancy, and fan-out. We include the passive-splitter loss and the dependence of the key rate on the delivered pair flux to determine the minimum total pair-generation rate required to meet the prescribed targets. Finally, we formulate the corresponding BBM92 quantum key distribution (QKD) secret-key-rate analysis for a continuous-wave-pumped broadband source, with true and accidental coincidences evaluated between detector channels at the two endpoint users and relative layer pair-generation rates fixed by the source spectrum. This framework, therefore, provides a direct route from exact network resource laws to the design and comparison of passive entanglement architectures under experimentally specified hardware constraints.
Ekta Panwar, Gilberto Borges, Saeide Salari et al.· 0 citations
A comparative study of three entanglement management paradigms for multi-core quantum processors shows that adaptive entanglement managements can substantially improve communication efficiency in scalable quantum multi-core systems.
Rajeswari Suance, Anubhab Dutta, Ruchika Gupta et al.· 0 citations
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.
Tasdiqul Islam, Rasman Mubtasim Swargo, Md Arifuzzaman· 0 citations
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