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REVIEW 4 major objections 5 minor 46 references

A Comprehensive Protocol Stack for Quantum Networks with a Global Entanglement Module

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read The paper proposes the first quantum-network protocol stack that separates offline planning from real-time execution, using a distributed Global Entanglement Module (GEM) to coordinate adaptive entanglement swapping, and reports a roughly 2

desk verdict A genuinely integrative quantum network stack that deserves a real referee, but the headline ~20% gain is not yet established because the fixed-tree baseline may not use the same early-discard mechanism and the scoring parameters are unpublished. read the letter →

arxiv 2509.16817 v2 pith:SHPX27MP submitted 2025-09-20 quant-ph cs.NI

Xiaojie Fan , C.R. Ramakrishnan , Himanshu Gupta This is my paper · ORCID
classification quant-phcs.NI
keywords quantumnetworkprotocolstackglobalentanglementmoduleadaptivedistributionswappingtreesfidelitypre-distributedmultipartitesimulation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that a quantum network needs a full protocol stack analogous to the classical Internet, and that the missing piece is a module that maintains a consistent network-wide view of entangled pairs. It proposes a six-layer stack with a Global Entanglement Module (GEM) that synchronizes metadata about every active entanglement across nodes via broadcast updates, separating offline planning (swapping trees) from real-time adaptive execution. The central claim is that this separation, guided by GEM, lets lightweight local policies react to stochastic generation outcomes and decoherence, and that a scoring-based policy improves entanglement generation rates by about 20% over a globally optimal but non-adaptive fixed-tree plan, and more than doubles the rate of connectionless hop-by-hop routing. If correct, this gives a practical architecture for scalable quantum networks that naturally supports pre-distributed entanglement, purification, and multipartite state generation.

What carries the argument

Global Entanglement Module (GEM) — a cross-layer distributed data structure that keeps a best-effort, network-wide, near-real-time record of all active entanglements (endpoints, fidelity, age, usage) via timestamped broadcast updates. It is the carrier of the argument: by giving every node a consistent view of entanglement availability, it turns adaptive swapping from a local heuristic into a coordinated network-wide decision process, enabling the scoring-based strategy to balance immediate benefit, opportunity loss, and memory relief in real time.

What would settle it

Simulate the same stack on a network an order of magnitude larger (e.g., 500-1000 nodes) or with a classical-message loss rate above zero; the scoring strategy's ~20% rate advantage over the fixed-tree baseline should shrink or reverse if the GEM's global view is the load-bearing mechanism.

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Extended reading notes

Core claim

The paper's central discovery is that the bottleneck in quantum network protocol design is not a single layer but the absence of a cross-layer, network-wide view of entanglement resources. GEM is a distributed data structure—each node holds a replica tracked with timestamps and synchronized by broadcast—that records for each active entangled pair its endpoints, fidelity, age, and intended use. With this view, the Swapping/Fusion Layer can replace fixed swapping orders with real-time choices: a scoring function weighs immediate benefit (route-completion progress), opportunity loss (foregoing alternative swaps that the offline plan predicted), and memory pressure, then picks the swap with the

Load-bearing premise

The distributed GEM relies on broadcast updates over reliable classical channels; if broadcast overhead, staleness, or classical-channel unreliability grows with network size, the global view on which adaptive execution depends degrades, and the paper's evaluation only covers modest random networks, deferring hierarchical synchronization to future work.

Editorial extensions

If this is right

  • If the stack is right, quantum networks can be built with a modular, Internet-like architecture where new adaptive execution policies are drop-in replacements.
  • The ~20% rate gain over a globally optimal fixed tree shows that real-time adaptation is not a compromise but an improvement even over optimal static plans.
  • The two-fold gain over connectionless approaches suggests that a shared global view is more valuable than purely local routing, at least in the simulated regime.
  • Support for predistribution, purification, and multipartite generation in one stack means applications (distributed quantum computing, GHZ distribution) can be built on a single unified service interface.
  • The failure of the centralized GEM (worse than fixed-tree) implies that distributing the module is architecturally necessary, not just an optimization.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The broadcast-based eventually-consistent GEM is the scaling bottleneck; a hierarchical synchronization mechanism (which the paper defers to future work) or subscription-based updates would be the natural next step, and its absence likely caps the stack at networks of a few hundred nodes.
  • The scoring function's weighting parameters and the depth-dependent age cutoff embody implicit design choices; one could test whether adaptively tuning those weights online further widens the reported 20% gap.
  • The same GEM abstraction could extend to multipartite adaptive execution, which the paper leaves open, potentially carrying the rate gains to GHZ and graph-state distribution.
  • The paper's 'globally optimal' comparison is an offline optimum computed under the same expected parameters; a stronger test would compare against an online policy that also adapts the route, not just the swapping order.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a six-layer quantum network protocol stack with a cross-layer Global Entanglement Module (GEM) that maintains a distributed, eventually consistent view of active entanglement metadata. The stack separates offline planning from real-time execution, and the Swapping/Fusion Layer adapts to the stochastic availability of EPs using a set of lightweight policies (youngest, oldest, longest-hop, shortest-hop, and score-based), together with a proactive early-discarding rule. The main quantitative claim is that the score-based strategy improves entanglement generation rates by about 20% over a globally optimal but non-adaptive fixed-tree baseline, and more than doubles the rate of a connectionless hop-by-hop baseline. The paper also describes support for predistributed entanglement, purification, and multipartite states, and evaluates the framework in NetSquid on Waxman random networks of 50–200 nodes.

Significance. If the main quantitative claim is substantiated, the architectural contribution is meaningful: separating planning from execution and maintaining a network-wide entanglement view through GEM is a useful step toward practical quantum network stacks, and the modular support for predistribution, purification, and multipartite states goes beyond many prior proposals. The paper is also explicit about the near-term scope, citing modest network sizes and eventual consistency via broadcast. The NetSquid evaluation is a genuine attempt to compare multiple adaptive policies against non-adaptive and connectionless baselines. However, the load-bearing quantitative comparison currently has a confounding baseline, undisclosed algorithm parameters, and no statistical support, so the central 20% claim is not yet established as reported.

major comments (4)
  1. [§VI, Our Algorithms and Prior Algorithms Compared] The text states that all five adaptive strategies are 'each applied in conjunction with the Early Discarding Strategy' (§VI, Our Algorithms), while the Fixed-Swapping-Tree baseline is described only as the DP-optimal tree from [11] with no indication that it also uses the depth-dependent cutoff T_cutoff(d)=f(d)·L_target from §IV.E. Early discarding can independently increase throughput by freeing memory and avoiding swaps with aged EPs. The reported ~20% gain of Scoring over Fixed-tree therefore conflates the adaptive swapping policy with the early-discarding heuristic. The fixed-tree baseline must be run with the same early-discard policy, or an ablation isolating early discarding from scoring must be provided.
  2. [Algorithm 2 and §VI Parameter Values] The scoring strategy depends on five weighting parameters α, γ, δ, β1, β2 (Algorithm 2) and on the depth-dependent scaling function f(d) used in the early-discarding cutoff. None of these values are reported anywhere in §VI or the appendix. Without them, the experiments are not reproducible, and the comparison could depend on parameter choices that favor Scoring over the baselines. Please report the exact parameter values and, ideally, a sensitivity analysis showing that the ~20% margin is stable across reasonable parameter ranges.
  3. [§VI, Simulation Setting] Each data point is described as one 100-second NetSquid simulation, but the number of independent runs, seeds, confidence intervals, or statistical tests is never stated. Entanglement generation and swapping are stochastic, and the reported performance differences—especially the ~20% improvement over fixed-tree—need error bars or a seed analysis to be credible. Without this, it is impossible to tell whether the observed ordering of strategies is robust or within simulation noise.
  4. [§VI, Centralized Module scheme] The conclusion that a distributed GEM is architecturally necessary is based on the 'centralized module scheme', which is implemented with the oldest adaptive strategy rather than the scoring strategy or a centralized variant of the same policy. The poor performance of this centralized scheme could be due to the strategy choice, the lack of the same early-discard rule, or an unfavorable message-query rate, rather than to centralization per se. A fair comparison should use the same adaptive policy and same early-discard policy in both centralized and distributed settings.
minor comments (5)
  1. [Title/Abstract and §III-C] Typesetting and spacing issues: 'aGlobal Entanglement Module' appears in the abstract and elsewhere, and 'entanglementsynchronization' appears in §III-C. These should be corrected.
  2. [§IV.E] The term 'Early Discarding Strategy' is used in §VI but the formal subsection is titled 'Proactive Strategy for Discarding Decohered EPs'. Please use consistent terminology so the reader can identify the mechanism.
  3. [§IV.E, Figure 4] The text says f(d) produces an 'exponentially decaying function' but no explicit family or parameterization is given. Even if the exact values are deferred, please define the functional form (e.g., f(d)=a·b^d with specified constants) or cite the source of the curve.
  4. [Algorithm 1 and §VI Parameter Values] The execution-monitor thresholds (N_min, T_min, ΔT, T_susp, δr, δf, ηr, ηf) are listed as inputs in Algorithm 1 but no numerical values are provided in the evaluation section. If any of these were used in the NetSquid experiments, they should be reported.
  5. [Figures 6–9] The figure captions do not identify which curves correspond to each strategy. Adding a legend or clarifying the line styles would make the results much easier to interpret.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; the architecture and simulations are self-contained, though the evaluation has a confound and unreported tunable parameters.

full rationale

The paper's central claims are empirical: the ~20% improvement of the Scoring strategy over a fixed-tree baseline and the >2x improvement over connectionless approaches are NetSquid simulation outcomes, not consequences of a definition. The GEM architecture is described independently of these results, and the distributed-vs-centralized GEM comparison uses the same oldest policy, isolating the communication overhead of centralization. No equation in the paper is shown to be equivalent by construction to its inputs; Algorithm 2's scoring formula is a heuristic definition, not a derived prediction. The scoring weights (α, γ, δ, β1, β2) and the depth-dependent cutoff function f(d) are not reported, which is a reproducibility and possible parameter-tuning concern, but there is no textual evidence that they were fitted to the reported outcomes. A more serious issue is experimental confounding: Sec. VI states the adaptive strategies are "each applied in conjunction with the Early Discarding Strategy," while the Fixed-Swapping-Tree baseline from [11] is described only as a DP-optimal tree with no mention of the same cutoff; thus the ~20% gain may not be solely attributable to GEM-based adaptive execution. This is a validity issue, not circularity. Self-citations to [11], [12], [20], [21], [22], and [24] are used as baselines or prior building blocks; they are not load-bearing for the paper's novelty claims or for the simulation results. Overall, the paper does not derive its conclusions from its inputs by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 1 invented entities

The central design is an architecture, so most axioms are simulation/modeling assumptions. No deep mathematical derivation is offered; the main numerical claims rest on NetSquid parameters and unspecified heuristic weights.

free parameters (3)
  • Scoring weights α, γ, δ, β1, β2 (Algorithm 2) = not reported
    These five weights determine the score in the scoring-based adaptive strategy that is the paper's main performance claim. Without their values or a tuning procedure, the reported ~20% improvement could be partly an artifact of fitted constants.
  • Depth-dependent cutoff scaling f(d) = not reported (Figure 4 caption says 'exponentially decaying')
    Used in T_cutoff(d) = f(d) * L_target for the proactive discarding strategy applied in all adaptive policies. The function shape and parameters are not given, so the discarding policy is under-specified.
  • Execution monitor thresholds (N_min, T_min, ΔT, T_susp, δr, δf, ηr, ηf) = not reported
    Algorithm 1 inputs for transport-layer congestion control. Not part of the main rate comparison, but they are free design choices without specified defaults.
assumptions (4)
  • domain assumption Swapping tree latency satisfies l_t = (3/2 max(l_l, l_r) + τ_f + τ_c) / p_f (Eq. 1), where the 3/2 factor approximates the mean of two similar exponential distributions.
    Taken from prior work [11]; the approximation may fail when child latencies are not similar exponentials, affecting plan optimality and the fixed-tree baseline.
  • domain assumption Nodes have limited quantum memories (5 memory positions in simulation) and EPs are subject to depolarization (0.01) and dephasing (1000) in NetSquid.
    Simulation model in §VI; these choices strongly affect decoherence-driven adaptive policies. Not validated against hardware.
  • domain assumption Classical communication is reliable and GEM broadcast overhead is tractable; local GEM replicas achieve eventual consistency.
    Stated in §III-C and §IV-D; the architecture's adaptive execution depends on this assumption. Scaling beyond modest networks is acknowledged as open.
  • domain assumption Link-level EP generation and swapping/fusion probabilities (p_f=0.4, p_g=0.33, photon transmission e^{-d/(2L)}) reflect realistic quantum hardware.
    Parameter values from [11,23]; no hardware validation in this paper. These affect all baselines equally.
invented entities (1)
  • Global Entanglement Module (GEM)
    purpose: Maintains a near-real-time network-wide view of entanglement metadata (state, fidelity, age, usage) to enable decentralized adaptive swapping/purification decisions.
    Only evidence is the paper's own NetSquid simulations; no external implementation or falsifiable prediction outside the simulator. Acknowledged as needing hierarchical synchronization to scale.

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Cite this review

Pith. "Pith review of A Comprehensive Protocol Stack for Quantum Networks with a Global Entanglement Module." pith.science (2026). https://pith.science/paper/SHPX27MP

@misc{pith2026250916817,
  author       = {Pith},
  title        = {Pith review of: A Comprehensive Protocol Stack for Quantum Networks with a Global Entanglement Module},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SHPX27MP}},
  note         = {Machine review of arXiv:2509.16817}
}
read the original abstract

The development of large-scale quantum networks requires not only advances in physical-layer technologies but also a comprehensive protocol stack that integrates communication, control, and resource management across all layers. We present the first such protocol stack, which introduces a Global Entanglement Module (GEM) that maintains a consistent, network-wide view of entanglement resources through distributed synchronization strategies. By enabling real-time adaptive execution of entanglement distribution plans, GEM bridges the gap between static planning and dynamic operation. The stack naturally supports pre-distributed entanglement, purification, and multi-partite state generation, making it applicable to a broad range of quantum networking applications. We design and evaluate multiple adaptive heuristics for real-time execution and show that a lightweight scoring-based strategy consistently achieves the best performance, improving entanglement generation rates by about 20% over a globally optimal but non-adaptive fixed-tree baseline and achieving more than a two-fold improvement relative to recent connectionless approaches. Across all scenarios-including predistribution and fidelity analysis-GEM consistently enables lower latency and robust operation. These results establish a practical pathway toward scalable, adaptive quantum internet systems.

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Works this paper leans on

46 extracted references · 4 linked inside Pith

  1. [11]

    Efficient quantum network communication using optimized entanglement swapping trees,

    M. Ghaderibanehet al., “Efficient quantum network communication using optimized entanglement swapping trees,”IEEE TQE, 2022

    2022

  2. [1]

    The security of practical quantum key distribution,

    V . Scaraniet al., “The security of practical quantum key distribution,” Reviews of modern physics, 2009

    2009

  3. [2]

    Distributing quantum circuits with minimum circuit execution time over quantum networks,

    R. G. Sundaram, H. Gupta, and C. Ramakrishnan, “Distributing quantum circuits with minimum circuit execution time over quantum networks,” in2024 IEEE International Conference on Quantum Computing and Engineering (QCE), vol. 1. IEEE, 2024, pp. 1938–1948

    2024

  4. [3]

    Distributed quantum computation over noisy channels,

    J. I. Cirac, A. Ekert, S. F. Huelga, and C. Macchiavello, “Distributed quantum computation over noisy channels,”Physical Review A, vol. 59, no. 6, p. 4249, 1999

    1999

  5. [4]

    Fusion-based quantum computation,

    S. Bartolucciet al., “Fusion-based quantum computation,”Nature Com- munications, 2023

    2023

  6. [5]

    Quantum-driven collaborative uas beam-reforming: A novel approach for congested spectrum environment in nextg communications,

    R. Tejaswini, D. Vemuri, X. Li, S. Arya, M. S. Mahalingam, and Y . Peng, “Quantum-driven collaborative uas beam-reforming: A novel approach for congested spectrum environment in nextg communications,” in2024 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON). IEEE, 2024, pp. 1–4

    2024

  7. [6]

    Designing a quantum network protocol,

    W. Kozlowskiet al., “Designing a quantum network protocol,” in CoNEXT, 2020

    2020

  8. [7]

    A link layer protocol for quantum networks,

    A. Dahlberget al., “A link layer protocol for quantum networks,” in SIGCOMM, 2019

    2019

Show all 46 references
  1. [8]

    Experimental demonstration of entanglement delivery using a quantum network stack,

    M. Pompili, C. Delle Donne, I. te Raa, B. van der Vecht, M. Skrzypczyk, G. Ferreira, L. de Kluijver, A. J. Stolk, S. L. Hermans, P. Pawełczak et al., “Experimental demonstration of entanglement delivery using a quantum network stack,”npj Quantum Information, vol. 8, no. 1, p. ...

    2022

  2. [9]

    A single quantum cannot be cloned,

    W. K. Wootters and W. H. Zurek, “A single quantum cannot be cloned,” Nature, vol. 299, no. 5886, pp. 802–803, 1982

    1982

  3. [10]

    Communication by EPR devices,

    D. Dieks, “Communication by EPR devices,”Physics Letters A, vol. 92, no. 6, pp. 271–272, Nov. 1982

    1982

  4. [12]

    Optimized generation of entanglement by real-time ordering of swapping operations,

    R. G. Sundaram and H. Gupta, “Optimized generation of entanglement by real-time ordering of swapping operations,” in2024 IEEE Inter- national Conference on Quantum Computing and Engineering (QCE), vol. 1. IEEE, 2024, pp. 1973–1979

    2024

  5. [13]

    Optimal entanglement distribution policies in homogeneous repeater chains with cutoffs,

    Á. G. Iñesta, G. Vardoyan, L. Scavuzzo, and S. Wehner, “Optimal entanglement distribution policies in homogeneous repeater chains with cutoffs,”npj Quantum Information, vol. 9, no. 1, p. 46, 2023

    2023

  6. [14]

    Fendi: Toward high-fidelity entanglement distribution in the quantum internet,

    H. Guet al., “Fendi: Toward high-fidelity entanglement distribution in the quantum internet,”IEEE/ACM Transactions on Networking, 2024

    2024

  7. [15]

    System design for a long-line quantum repeater,

    R. Van Meter, T. D. Ladd, W. J. Munro, and K. Nemoto, “System design for a long-line quantum repeater,”IEEE/ACM Transactions On Networking, vol. 17, no. 3, pp. 1002–1013, 2008

    2008

  8. [16]

    Recursive quantum repeater networks,

    R. Van Meter, J. Touch, and D. Horsman, “Recursive quantum repeater networks,”arXiv preprint arXiv:1105.1238, 2011

    2011 arXiv

  9. [17]

    Quantum link bootstrapping using a ruleset-based communication protocol,

    T. Matsuo, C. Durand, and R. Van Meter, “Quantum link bootstrapping using a ruleset-based communication protocol,”Physical Review A, vol. 100, no. 5, p. 052320, 2019

    2019

  10. [18]

    A quantum inter- net architecture,

    R. Van Meter, R. Satoh, N. Benchasattabuse, K. Teramoto, T. Matsuo, M. Hajdušek, T. Satoh, S. Nagayama, and S. Suzuki, “A quantum inter- net architecture,” in2022 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2022, pp. 341–352

    2022

  11. [19]

    Leveraging internet principles to build a quantum network,

    L. Bacciottini, A. Chandra, M. G. De Andrade, N. K. Panigrahy, S. Pouryousef, N. S. Rao, E. Van Milligen, G. Vardoyan, and D. Towsley, “Leveraging internet principles to build a quantum network,”arXiv preprint arXiv:2410.08980, 2024

    2024 arXiv

  12. [20]

    Distribution and purification of entanglement states in quantum networks,

    X. Fan, Y . Yang, H. Gupta, and C. Ramakrishnan, “Distribution and purification of entanglement states in quantum networks,” in2025 International Conference on Quantum Communications, Networking, and Computing (QCNC). IEEE, 2025, pp. 74–82

    2025

  13. [21]

    Pre-distribution of entanglements in quantum networks,

    M. Ghaderibanehet al., “Pre-distribution of entanglements in quantum networks,” inIEEE QCE, 2022, pp. 426–436

    2022

  14. [22]

    Optimized distribution of entanglement graph states in quantum networks,

    X. Fanet al., “Optimized distribution of entanglement graph states in quantum networks,”arXiv preprint arXiv:2405.00222, 2024

    2024 arXiv

  15. [23]

    Optimal routing for quantum networks,

    M. Caleffi, “Optimal routing for quantum networks,”IEEE Access, 2017

    2017

  16. [24]

    Generation and distribution of GHZ states in quantum networks,

    M. Ghaderibanehet al., “Generation and distribution of GHZ states in quantum networks,” inIEEE QCE, 2023

    2023

  17. [25]

    A quantum network stack and protocols for reliable entanglement-based networks,

    A. Pirker and W. Dür, “A quantum network stack and protocols for reliable entanglement-based networks,”New Journal of Physics, vol. 21, no. 3, p. 033003, 2019

    2019

  18. [26]

    Co-design of network topology and qubit allocation for distributed quantum computing,

    J. Liu, L. Fan, Y . Guo, Z. Han, and Y . Wang, “Co-design of network topology and qubit allocation for distributed quantum computing,” in 2025 International Conference on Quantum Communications, Network- ing, and Computing (QCNC). IEEE, 2025, pp. 355–362

    2025

  19. [27]

    Qsco: A quantum scoring module for open-set supervised anomaly detection,

    Y . Peng, X. Li, Z. Liang, and Y . Wang, “Qsco: A quantum scoring module for open-set supervised anomaly detection,” inProceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 19, 2025, pp. 19 884–19 894

    2025

  20. [28]

    Hyq2: A hybrid quantum neural network for nextg vulnerability detection,

    Y . Peng, X. Li, Z. Liang, and Y . Wang, “Hyq2: A hybrid quantum neural network for nextg vulnerability detection,”IEEE Transactions on Quantum Engineering, 2024

    2024

  21. [29]

    Ecdqc: Efficient compilation for distributed 15 quantum computing with linear layout,

    K. Liu, Y . Zhou, H. Luo, L. Xiong, Y . Zhu, E. Casey, J. Cheng, S. Y .- C. Chen, and Z. Liang, “Ecdqc: Efficient compilation for distributed 15 quantum computing with linear layout,” in2025 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2025, pp. 1–5

    2025

  22. [30]

    Quantum internet: A vision for the road ahead,

    S. Wehner, D. Elkouss, and R. Hanson, “Quantum internet: A vision for the road ahead,”Science, vol. 362, no. 6412, p. eaam9288, 2018

    2018

  23. [31]

    Concurrent entanglement routing for quantum networks: Model and designs,

    S. Shi and C. Qian, “Concurrent entanglement routing for quantum networks: Model and designs,” inSIGCOMM, 2020

    2020

  24. [32]

    Long-distance quantum communication with atomic ensembles and linear optics,

    L.-M. Duanet al., “Long-distance quantum communication with atomic ensembles and linear optics,”Nature, 2001

    2001

  25. [33]

    Topology design with resource allocation and entanglement distribution for quantum networks,

    J. Liu, X. Liu, X. Wei, and Y . Wang, “Topology design with resource allocation and entanglement distribution for quantum networks,” in2024 21st Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE, 2024, pp. 1–9

    2024

  26. [34]

    Optimal entanglement distribution problem in satellite-based quantum networks,

    X. Wei, J. Liu, L. Fan, Y . Guo, Z. Han, and Y . Wang, “Optimal entanglement distribution problem in satellite-based quantum networks,” IEEE Network, vol. 39, no. 1, pp. 97–103, 2024

    2024

  27. [35]

    Quesat: Satellite- assisted quantum internet for global-scale entanglement distribution,

    H. Gu, R. Yu, Z. Li, X. Wang, and G. Xue, “Quesat: Satellite- assisted quantum internet for global-scale entanglement distribution,” in IEEE INFOCOM 2025-IEEE Conference on Computer Communications. IEEE, 2025, pp. 1–10

    2025

  28. [36]

    Learning best paths in quantum networks,

    X. Wang, M. Liu, X. Liu, Z. Li, M. Hajiesmaili, J. C. Lui, and D. Towsley, “Learning best paths in quantum networks,” inIEEE IN- FOCOM 2025-IEEE Conference on Computer Communications. IEEE, 2025, pp. 1–10

    2025

  29. [37]

    Mi-co: Models and algorithms for cost-efficient entanglement distribution in the quantum internet,

    H. Gu, Z. Li, X. Wang, D. Yang, G. Xue, and R. Yu, “Mi-co: Models and algorithms for cost-efficient entanglement distribution in the quantum internet,” in2025 International Conference on Quantum Communications, Networking, and Computing (QCNC). IEEE, 2025, pp. 339–346

    2025

  30. [38]

    Dynamic distribution of quantum circuits with minimum circuit-execution time,

    R. G. Sundaram and H. Gupta, “Dynamic distribution of quantum circuits with minimum circuit-execution time,” in2025 International Conference on Quantum Communications, Networking, and Computing (QCNC). IEEE, 2025, pp. 238–246

    2025

  31. [39]

    NSF national quantum virtual labo- ratory advances with first five projects,

    National Science Foundation, “NSF national quantum virtual labo- ratory advances with first five projects,” https://www.nsf.gov/news/ nsf-national-quantum-virtual-laboratory-advances-first-five, 2023, ac- cessed: 2025-09-01

    2023

  32. [40]

    New high-intensity source of polarization-entangled photon pairs,

    P. G. Kwiat, K. Mattle, H. Weinfurter, A. Zeilinger, A. V . Sergienko, and Y . Shih, “New high-intensity source of polarization-entangled photon pairs,”Physical Review Letters, vol. 75, no. 24, p. 4337, 1995

    1995

  33. [41]

    Vacuum beam guide for large scale quantum networks,

    Y . Huang, F. Salces-Carcoba, R. X. Adhikari, A. H. Safavi-Naeini, and L. Jiang, “Vacuum beam guide for large scale quantum networks,” Physical Review Letters, vol. 133, no. 2, p. 020801, 2024

    2024

  34. [42]

    Optical quantum memory,

    A. I. Lvovsky, B. C. Sanders, and W. Tittel, “Optical quantum memory,” Nature photonics, vol. 3, no. 12, pp. 706–714, 2009

    2009

  35. [43]

    Graph-theoretical optimization of fusion-based graph state generation,

    S.-H. Lee and H. Jeong, “Graph-theoretical optimization of fusion-based graph state generation,”arXiv preprint arXiv:2304.11988, 2023

    2023 arXiv

  36. [44]

    NetSquid, a discrete-event simulation platform for quantum networks,

    T. Coopmanset al., “NetSquid, a discrete-event simulation platform for quantum networks,”Communications Physics, 2021

    2021

  37. [45]

    Routing of multipoint connections,

    B. Waxman, “Routing of multipoint connections,”IEEE Journal on Selected Areas in Communications, 1988

    1988

  38. [46]

    Entanglement distribution in a quantum network: A multicommodity flow-based ap- proach,

    K. Chakraborty, D. Elkouss, B. Rijsman, and S. Wehner, “Entanglement distribution in a quantum network: A multicommodity flow-based ap- proach,”IEEE Transactions on Quantum Engineering, vol. 1, pp. 1–21, 2020. APPENDIX System-Level Details of Each Layer This section details th...

    2020

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