Entanglement-based quantum networks (EBNs) distribute entanglement between distant end nodes via intermediate quantum switches that perform entanglement swapping. A 2025/2026 NetSquid simulation study by Pérez Castro et al. characterized how memory technology (NV vs. SiV color centers), gate durations, decoherence, number of switches, distances, DEJMPS purification, and Shor-code error correction jointly determine end-to-end capacity, fidelity, and processing overhead, but it explicitly states that the distribution of switches and resource allocation is still an open problem and lists alternative purification techniques (e.g., optimistic entanglement pumping) and further memory technologies (e.g., superconducting) as unresolved directions. This task asks: given an arbitrary physical topology, a request set, and a catalog of hardware and protocol options, construct a general method — with optimality or approximation guarantees, or an analytical characterization validated against simulation — for placing quantum switches and allocating resources so that end-to-end capacity, fidelity, and processing overhead are jointly optimized (the three metrics combined as a Pareto frontier, or under an explicitly declared scalarization; a heuristic validated only on particular instances does not by itself resolve the task). Existing analytical results cover single switches and repeater chains, routing heuristics assume given topologies, and network-design studies handle small instances or chain topologies only.

Background

An entanglement-based network (EBN) connects quantum end nodes through quantum switches — intermediate nodes holding quantum memories that establish entanglement with neighbors and perform entanglement swapping (a joint Bell-state measurement that consumes two short entangled pairs to create one longer pair). NV and SiV color centers are point defects in diamond used as quantum memories, differing in gate duration, coherence time, and fiber-coupling efficiency; these physical parameters, together with channel attenuation and depolarization, determine the three standard performance metrics: capacity (rate of delivered end-to-end entangled pairs), fidelity (overlap of the delivered state with the ideal Bell state), and processing overhead (time and quantum resources spent on auxiliary operations). Because raw links are noisy, protocols must improve fidelity: DEJMPS is a standard two-copy entanglement purification protocol that probabilistically distills a higher-fidelity pair from two lower-fidelity ones; entanglement pumping is a variant that repeatedly purifies one pair against fresh link pairs; optimistic purification delays the classical heralding communication to reduce memory residence time; quantum error correction (QEC) codes such as the 9-qubit Shor code protect qubits instead. QBER (quantum bit error rate) is measurable at distance and is used as an experimentally accessible proxy for fidelity. NetSquid is a discrete-event simulator of quantum networks operating at the physical layer.

Prior work splits into three strands. Analytical queueing and capacity theory covers a single entanglement-distribution switch (Vardoyan et al., 2021), switches with purification (Panigrahy et al., 2023; Tillman et al., 2024), bipartite entanglement capacity under restricted network settings (Vardoyan et al., 2024), and repeater chains with sequential swaps (de Andrade et al., 2024); extension beyond linear topologies is considered intractable, and recent analytical work still targets chains. Entanglement routing (Pant et al., 2019; Zhao et al., 2022; Shi et al., 2024) selects paths for communication requests, but assumes the physical topology — including switch placement — as given, and mostly relies on heuristics without optimality guarantees. Network design studies are the closest neighbors: Rabbie et al. (2022) designed quantum networks on preexisting fiber infrastructure for small instances, Avis et al. (2023) derived hardware requirements for a processing-node repeater on a real-world fiber grid, Labay Mora et al. (2024) jointly optimized hardware and protocols but only for repeater chains, and Pouryousef et al. (2023) optimized entanglement storage placement on a given physical graph. Against this background, Pérez Castro et al. (2025/2026) built a NetSquid tool for switch-based EBNs of arbitrary topology, compared NV and SiV memories, purification, and Shor-code error correction up to 70 km, and derived configuration guidelines — while stating verbatim that the distribution of switches and the resource allocation 'is still an open problem', and listing superconducting memories and purification alternatives such as optimistic entanglement pumping (studied to date only for a single link by Mobayenjarihani et al., 2024) as future work.

Problem Statement

Given (i) an arbitrary physical topology of end nodes, candidate switch sites, and fiber link lengths; (ii) a set of end-to-end communication requests; and (iii) a catalog of hardware and protocol options — memory technologies (e.g., NV, SiV, or superconducting memories with their gate durations, coherence times, and coupling efficiencies) and entanglement-improvement strategies (DEJMPS purification of chosen depth, optimistic entanglement pumping, QEC codes such as the Shor code) — determine the number and placement of quantum switches and the allocation of quantum and classical resources (memories per node, channel configurations, purification/QEC redundancy) so that end-to-end capacity, fidelity, and processing overhead are jointly optimized for arbitrary topologies. The question replaces the current state of practice — case-by-case discrete-event simulation guidance (as in the source study, which explored linear switch chains up to tens of km) — with a general, principled placement and resource-allocation method; it subsumes, as co-design dimensions, the source's two named open directions: the network-level impact of further memory technologies such as superconducting memories, and the network-level trade-offs of purification alternatives such as optimistic entanglement pumping.

References

  1. David Pérez-Castro, Juan Fernández-Herrerín, Ana Fernández-Vilas, Manuel Fernández-Veiga, Rebeca P. Díaz-Redondo, 'Simulation of entanglement based quantum networks for performance characterization', Computer Networks 282:112249 (2026); arXiv:2501.03210; DOI 10.1016/j.comnet.2026.112249; https://arxiv.org/abs/2501.03210
  2. Gayane Vardoyan, Saikat Guha, Philippe Nain, Don Towsley, 'On the Stochastic Analysis of a Quantum Entanglement Distribution Switch', IEEE Transactions on Quantum Engineering 2 (2021); DOI 10.1109/tqe.2021.3058058; https://doi.org/10.1109/tqe.2021.3058058
  3. Nitish K. Panigrahy, Thirupathaiah Vasantam, Don Towsley, Leandros Tassiulas, 'On the Capacity Region of a Quantum Switch with Entanglement Purification', IEEE INFOCOM 2023; DOI 10.1109/infocom53939.2023.10229003; https://doi.org/10.1109/infocom53939.2023.10229003
  4. Ian Tillman, Thirupathaiah Vasantam, Don Towsley, Kaushik P. Seshadreesan, 'Calculating the Capacity Region of a Quantum Switch', 2024 IEEE International Conference on Quantum Computing and Engineering (QCE); DOI 10.1109/qce60285.2024.00216; https://doi.org/10.1109/qce60285.2024.00216
  5. Gayane Vardoyan, Emily van Milligen, Saikat Guha, Stephanie Wehner, Don Towsley, 'On the Bipartite Entanglement Capacity of Quantum Networks', IEEE Transactions on Quantum Engineering 5 (2024); DOI 10.1109/tqe.2024.3366696; https://doi.org/10.1109/tqe.2024.3366696
  6. Matheus Guedes de Andrade, Emily A. Van Milligen, Leonardo Bacciottini, Aparimit Chandra, Shahrooz Pouryousef, Nitish K. Panigrahy, Gayane Vardoyan, Don Towsley, 'On the Analysis of Quantum Repeater Chains with Sequential Swaps' (2024); arXiv:2405.18252; DOI 10.48550/arXiv.2405.18252; https://arxiv.org/abs/2405.18252
  7. Nengkun Yu, 'The Quantum Repeater Network Saturates the Entanglement Distribution Asymptotically', IEEE Transactions on Information Theory (2025); DOI 10.1109/tit.2025.3584199; https://doi.org/10.1109/tit.2025.3584199
  8. Luca Tavanti, 'Analytical computation of entanglement distribution time in first-generation quantum repeater chains', npj Quantum Information (2026); DOI 10.1038/s41534-026-01299-8; https://doi.org/10.1038/s41534-026-01299-8
  9. Mihir Pant, Hari Krovi, Don Towsley, Leandros Tassiulas, Liang Jiang, Prithwish Basu, Dirk Englund, Saikat Guha, 'Routing entanglement in the quantum internet', npj Quantum Information 5:25 (2019); DOI 10.1038/s41534-019-0139-x; https://doi.org/10.1038/s41534-019-0139-x
  10. Yangming Zhao, Gongming Zhao, Chunming Qiao, 'E2E Fidelity Aware Routing and Purification for Throughput Maximization in Quantum Networks', IEEE INFOCOM 2022, pp. 480–489; DOI 10.1109/infocom48880.2022.9796814; https://doi.org/10.1109/infocom48880.2022.9796814
  11. Shouqian Shi, Xiaoxue Zhang, Chen Qian, 'Concurrent Entanglement Routing for Quantum Networks: Model and Designs', IEEE/ACM Transactions on Networking (2024); DOI 10.1109/tnet.2023.3343748; https://doi.org/10.1109/tnet.2023.3343748
  12. Julian Rabbie, Kaushik Chakraborty, Guus Avis, Stephanie Wehner, 'Designing quantum networks using preexisting infrastructure', npj Quantum Information 8 (2022); DOI 10.1038/s41534-021-00501-3; https://doi.org/10.1038/s41534-021-00501-3
  13. Guus Avis, Francisco Ferreira da Silva, Tim Coopmans, Axel Dahlberg, Hana Jirovská, David Maier, Julian Rabbie, Ariana Torres-Knoop, Stephanie Wehner, 'Requirements for a processing-node quantum repeater on a real-world fiber grid', npj Quantum Information 9 (2023); DOI 10.1038/s41534-023-00765-x; https://doi.org/10.1038/s41534-023-00765-x
  14. Adrià Labay Mora, Francisco Ferreira da Silva, Stephanie Wehner, 'Reducing hardware requirements for entanglement distribution via joint hardware-protocol optimization', Quantum Science and Technology 9 (2024); DOI 10.1088/2058-9565/ad57e9; https://doi.org/10.1088/2058-9565/ad57e9
  15. Shahrooz Pouryousef, Nitish K. Panigrahy, Don Towsley, 'A Quantum Overlay Network for Efficient Entanglement Distribution', IEEE INFOCOM 2023; DOI 10.1109/infocom53939.2023.10228944; https://doi.org/10.1109/infocom53939.2023.10228944
  16. Mohammad Mobayenjarihani, Gayane Vardoyan, Don Towsley, 'Optimistic Entanglement Purification in Quantum Networks' (2024); arXiv:2401.08034; https://arxiv.org/abs/2401.08034
  17. Pranav Kulkarni, Leo Sünkel, Michael Kölle, 'An Adaptive Purification Controller for Quantum Networks: Dynamic Protocol Selection and Multipartite Distillation' (2026); arXiv:2601.18351; https://arxiv.org/abs/2601.18351
  18. Chun-Hsiang Wang, Chia-Wei Tsai, 'GHZ-Equivalent State Distribution in Quantum Networks: Reducing Decoherence and Quantum Resource Consumption' (2026); arXiv:2607.17101; https://arxiv.org/abs/2607.17101
  19. Guus Avis, Filip Rozpędek, Stephanie Wehner, 'Analysis of multipartite entanglement distribution using a central quantum-network node', Physical Review A 107, 012609 (2023); DOI 10.1103/physreva.107.012609; https://doi.org/10.1103/PhysRevA.107.012609