Quality-of-Transmission Estimation in Quantum Networks: Compute, Simulate, or Learn?

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Quality-of-Transmission Estimation in Quantum Networks: Compute, Simulate, or Learn?

arXiv:2610.23827v1 [quant-ph] 16 Oct 2026

Anis Bekri1,2 , Amar Abane1 , Van Sy Mai1 , Abdella Battou1 , and Saddek Bensalem2

Abstract—Future quantum networks will rely on control-plane decisions such as routing, scheduling, entanglement provisioning, purification, and memory allocation. Each decision depends on a timely and reliable estimate of the quality of the entanglement that a candidate path can deliver, with end-to-end fidelity as a central metric. Inspired by the optical-networking experience with quality-of-transmission (QoT) estimation, this article asks when a quantum controller should compute fidelity analytically, simulate it, or learn a surrogate model. The optical analogy is useful but incomplete: optical QoT estimation often predicts a scalar transmission margin, whereas quantum QoT estimation must account for probabilistic entanglement generation, random memory waiting times, decoherence, state consumption, and measurement constraints that can make fidelity a stochastic target. We propose a compute/simulate/learn framework for quantum QoT estimation and illustrate it with a simulation-based proof of concept. Our study shows that analytical composition can be sufficient in simple regimes, that simulation becomes necessary when stochastic memory effects shape the delivered fidelity distribution, and that lightweight learned surrogates can approximate simulator labels at much lower runtime cost for online control. We close with open challenges for deployment, including stochastic targets, sim-to-real transfer, datageneration cost, physics-informed learning, and generalization beyond individual paths to full network topologies. Index Terms—Quantum networks, entanglement distribution, fidelity estimation, quality of transmission, machine learning, network simulation.

Introduction Quantum networks are expected to support applications that rely on distributed entanglement, from secure communication to distributed sensing and networked quantum computing [1]. Delivering such services is not only a physical-layer problem. A quantum network also needs a control plane able to decide which path should be used for an entanglement request, which elementary links should be generated first, when entanglement swapping should be attempted, whether purification is worth the cost, and how scarce quantum memories should be allocated [2]. Each of these decisions depends on a prior estimate of the quality that a candidate path can deliver. 1 National Institute of Standards and Technology, Gaithersburg, Maryland, USA. 2 Université Grenoble Alpes, Grenoble, France. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The central metric in this article is the end-to-end fidelity of the delivered entangled state, considered together with rate and waiting time. These are the quantities in which quantum applications are expected to express their quality-of-service (QoS) requirements [3]. This makes fidelity estimation the quantum counterpart of qualityof-transmission (QoT) estimation for optical lightpaths, where QoT estimators help decide whether a candidate connection will meet the minimum signal quality required for reliable reception. These estimators may rely on analytical models, physical-layer tools such as GNPy [4], monitoring data, or learned predictors for QoT estimation of established and unestablished lightpaths [5]–[7]. A similar operational question now appears in quantum networking: before committing resources to a path, the controller needs to estimate whether the resulting entanglement will satisfy certain QoS requirements. The analogy with optical QoT estimation is useful, but incomplete. In optical networks, QoT is often summarized by a scalar margin, such as the optical signal-to-noise ratio (OSNR), the generalized signal-to-noise ratio (GSNR), or a predicted bit error rate (BER). For a fixed lightpath, this margin is usually treated as a deterministic quantity to estimate conservatively. In quantum networks, the same path can deliver a distribution of fidelities across requests because entanglement generation is probabilistic, memories decohere while waiting, and swapping or purification may succeed or fail [8], [9]. The mean of this distribution is a useful first target for control-plane decisions, and it is the target studied in our proof of concept. More generally, treating fidelity as a distribution opens the door to riskaware QoT estimation, where a controller may also use lower quantiles or threshold-violation probabilities when allowed. This is, however, outside the scope of this paper. There are three natural ways to obtain such an estimate. An analytical formula is fast and interpretable, but it is only reliable when its assumptions match the operating regime and when its parameters are fresh and observable. A simulator can model richer dynamics, including waiting times and memory decoherence, but it is too expensive to run inside every online routing or scheduling decision [8], [9]. A learned model can provide fast predictions after training and can therefore serve as a surrogate for expensive simulation, an idea now emerging in quantum-network optimization [10]. This article organizes quantum fidelity estimation around a simple operational principle: the estimator should adapt to the regime in which the controller op-

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Fig. 1. Entanglement distribution along a repeater path. Elementary links are generated probabilistically and then fused by Bell-state measurements at intermediate repeaters. The final end-to-end fidelity is affected not only by the elementary-link qualities, but also by swap imperfections and memory decoherence accumulated while neighboring links wait.

erates. For simple, well-characterized paths, analytical composition provides a fast and transparent estimate. However, when probabilistic generation, memory waiting times, and complex decoherence patterns shape the delivered fidelity distribution, simulation provides the right level of detail. Finally, when many candidate paths or resource-allocation decisions must be evaluated online, a learned model can turn simulator-generated data into a fast estimator. The practical question is therefore the following: when should a controller compute fidelity analytically, when should it simulate, and when should it rely on a learned surrogate model? We present a simple framework that helps make that decision, and we illustrate it with a quantum-network simulator-based study. Quantum QoT and the Optical Precedent What “QoT” Means in a Quantum Network The QoT of a quantum path is carried chiefly by the end-to-end fidelity. It is hard to predict for reasons with no optical analogue: it is inherently stochastic, decoheres in memory, is consumed on use, and the no-cloning theorem forbids measuring it without destroying the state. Entanglement is distributed by generating short elementary links and fusing them through entanglement swapping at intermediate repeaters (Fig. 1); each swap extends reach but compounds imperfection and lets decoherence act over the accumulated waiting time. For a path Ps,d of Werner links1 with fidelities Fℓ , 1 A Werner link is an elementary entangled channel whose shared state is modeled as a mixture of a maximally entangled Bell state and uniform noise.

ideal swapping composes the end-to-end fidelity multiplicatively:   Y 1 4Fℓ − 1 Fs,d = 1 + 3 Wℓ  , Wℓ = . (1) 4 3 ℓ∈Ps,d

Equation (1) is the quantum counterpart of the optical GSNR composition: a highly efficient and common formula that the network control plane can easily utilize. Three Ways to Get the Metric Table I contrasts the three sources along cost, accuracy, and the parameter knowledge they require. Analytical formulas are fast but their accuracy is regime-dependent: exact when little decoherence occurs, biased once memories wait. Simulators are accurate across regimes because they reproduce the underlying stochastic dynamics, but that accuracy comes at a real runtime cost, since estimating an expected fidelity means averaging many simulation runs. Learned surrogates pay a one-time data-generation and training cost and then provide rapid predictions from features, implicitly absorbing whatever physics the formula leaves out. The Optical QoT Estimation Story Optical networks faced this same estimation problem a decade ago. A physics engine such as GNPy [4] computes the generalized signal-to-noise ratio of a lightpath from fiber and amplifier parameters, and machine-learned QoT estimators were layered on top to predict margins for lightpaths that had never been provisioned, tightening design margins and recovering capacity [5]–[7]. The lesson

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TABLE I Sources of a Quantum QoT Estimate

Source Analytical Simulator Learned surrogate

Offline cost None None High

Online cost Low High Low

Accuracy Regime-dependent High Training-dependent

Parameters needed Exact, fresh Exact, known Implicit, from data

Offline cost for the surrogate covers data generation and training.

we import is the decision discipline itself: compute where the physics is trustworthy and cheap, and learn where estimates must be produced online for configurations never seen at design time. In optics, an analytical model is usually accurate once its parameters are supplied. Learning earns its place because those parameters are impractical to probe exhaustively across a live network. In the quantum case, as the next section argues, the deployable formula’s weakness is different in kind. It is undermined less by unknown parameters than by a dynamical effect, namely decoherence over random waiting times, that its functional form cannot represent at all, together with inputs that nocloning makes unobservable at runtime. Learn, Compute, or Simulate? A Decision Grid When the control plane needs the fidelity of a candidate request, “use machine learning” is often treated as a single decision. It is in fact two decisions and one workflow. The first decision is whether a learned predictor should replace the cheap analytical formula and the second is whether it should replace the accurate but slow simulator. If learning is selected, the question then becomes operational: simulator outputs must be collected offline, converted into a QoT dataset, and used to train a surrogate that can be queried online by the control plane. We separate these choices into two axes and fold them into the decision-aware workflow of Fig. 2. Axis A: Learn vs. Compute: When the Formula Fails The multiplicative composition described in (1) is compelling because it condenses an entire network path into a straightforward product of per-link values. In this context, a learned model becomes necessary only when calculating that product is no longer appropriate, which can happen for two distinct reasons below. First, the formula ignores the dynamics that generate the state. Equation (1) composes fixed link fidelities as if they were delivered instantaneously. In practice, elementary links are generated by probabilistic attempts, so a link’s memory waits a random amount of time for its neighbor to be ready, and it decoheres over that waiting period. The formula has no notion of waiting time by construction, so it is accurate only when little decoherence occurs and biased once it does. This is not because any input is wrong, but because its functional form cannot represent a timedependent effect at all. The illustrative study below shows this gap directly: a calibrated version of (1) is close to exact in a low-decoherence regime and increasingly biased as waiting times grow, which is the structured, learnable

residual a surrogate can absorb. Other simplifications compound the same weakness. With an imperfect Bellstate measurement (BSM) at node k, of quality gk , two segments compose as Wij = gk Wi Wj ,

(2)

and assuming a single global g already fails once BSM quality varies across nodes. Interleaving purification would break the product further, since purification is nonlinear and order-dependent. We do not measure these effects here, but they point in the same direction: relaxing any of the formula’s idealizations turns a clean product into something a fixed formula cannot track, while remaining learnable from the same features. Second, the parameters are unobservable or stale. A perfect formula is still impractical if its inputs, such as the active link fidelities or the residual coherence within quantum memories, cannot be readily obtained, a common obstacle in quantum networking. No-cloning forbids reading the fidelity of a live entangled state without measuring, and thereby destroying, it; fidelity is learned only by consuming pairs in tomography or verification protocols, or inferred through proxies [2]. Worse, the relevant state decoheres on timescales comparable to classical control round-trips, so any measured value is partly out of date by the time a decision consumes it. Continuous characterization of every link and memory is costly and competes with live traffic for the same scarce quantum resources. This is the true analogue of the optical “analytical-is-infeasible” case, where probing every spectral load before deployment was impractical. A learned predictor sidesteps the problem by mapping cheaply observable, stable features, such as topology, distances, nominal hardware specifications, and request type, onto the fidelity that the hidden instantaneous parameters would have produced, having seen ground truth once, at training time. This is where a learned estimator most clearly demonstrates its value, offering a perspective that is highly compelling: rather than “learning beats physics,” it is “learning supplies what measurement cannot.” Axis B: Learn vs. Simulate: The Deployment Case The second axis is often mischaracterized. When a study indicates that a learned model “generalizes to unseen networks,” the baseline it outperforms is not the analytical formula, which generalizes for free, since one simply plugs in new lengths and hop counts as inputs. The baseline is in fact re-running the simulator. A detailed simulator is accurate and, for that reason, slow: a simulation run cannot

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sit inside a routing loop that must answer in milliseconds, and it may not even be configured for the topology or scale at hand. A surrogate trained on simulator traces offers simulator-grade accuracy at formula-grade speed. Given the right inductive bias, such as graph structure rather than fixed-length feature vectors, it can also provide predictions for networks it never saw during training. The argument here is therefore not that the formula is wrong, but that accuracy is needed without paying the simulator’s cost at decision time. Establishing the correct baseline is critical, as it dictates what the results must compare against: the study below evaluates the surrogate against re-simulation rather than against the product formula on this axis. Putting It Together The two axes are embedded in the decision flow shown in the lower part of Fig. 2. First: is the multiplicative formula both accurate for this path and feedable with cheap, fresh parameters? If so, use it. This is the homogeneous, wellcharacterized corner where learning would only stack an unvalidated black box on top of a trustworthy answer. If not, a higher-fidelity source is required, which raises the second question: can detailed simulation be run at decision time? If yes, simulate; if the answer is needed online and repeated simulation is too costly, use the trained machinelearning surrogate. The upper part of Fig. 2 shows what this last branch requires before deployment. Candidate path configurations are evaluated offline with a quantum-network simulator. The resulting fidelity labels are stored together with path and hardware features to form a QoT dataset. A machinelearning model is then trained to learn the mapping from configuration to delivered fidelity, and the trained surrogate is deployed as a fast online estimator. Learning, in other words, is not a shortcut around simulation; it is a way to amortize expensive simulation over many future control-plane decisions. To conclude, machine learning should not be treated as a default for quantum QoT estimation. It is the right tool when analytical formulas become significantly inexact or impractical and repeated simulation is too slow for online use. Outside of this intersection, employing it is wasted effort at best. The rest of this article shows, on simulator data, where that regime actually begins. An Illustrative Simulation-Based Study We now locate the boundaries of the grid empirically. The study is built entirely on the public SeQUeNCe simulator [8], using its Bell-diagonal-state pipeline so that each delivered pair carries an analytically tracked fidelity. Elementary links are generated by heralded, probabilistic attempts, so a link succeeds only after a random number of tries; its memory then waits a random time for the neighboring link to be ready, and a depolarizing channel degrades the stored state over that wait. Entanglement swapping fuses links into an end-to-end pair. Hardware

is set to practical values: detector efficiency 0.85 with dark counts, memory efficiency 0.69, and 0.2 dB/km fiber, while configurations sample link lengths of 2–40 km, nominal link fidelities of 0.80–0.99, and coherence times loguniformly from 0.2 to 5 s, and a native memory cutoff discards pairs that waited beyond half the coherence time, rather than delivering a heavily degraded state. This cutoff is what a real controller would apply, and it also keeps the delivered-fidelity distribution unimodal, so that its mean is a meaningful prediction target. The prediction task is deliberately simple. From features a control plane can read cheaply and keep fresh, namely hop count, per-link nominal fidelities, distances, and a coherence-derived decoherence severity, we predict the mean delivered fidelity of a request. Two points frame every number below. The target is the fidelity of the simulator, not of real hardware; and the model is given only these observable, physical features, never the analytical estimate itself, so that outperforming the formula is a genuine comparison rather than a correction applied on top of it. Each label is a mean over three stochastic runs, a stable Monte-Carlo estimate of the expected fidelity.

The Simple Regime: the Formula Suffices We first characterize the low-decoherence corner: twoand three-hop paths with links of 4–6 km, nominal link fidelities of 0.92–0.96, and coherence times of 50–80 s, so that memories wait a negligible fraction of their coherence time before a pair is delivered. The simulator is then effectively deterministic: repeated runs of the same configuration agree to within 10−4 in fidelity. The uncalibrated formula (1) reproduces the simulated fidelity with a mean-squared error of 2 × 10−7 , overestimating it by about 5 × 10−4 on every configuration. This residual overestimation reflects the hardware imperfections and the small amount of decoherence that the ideal formula does not model, but it is three orders of magnitude smaller than any fidelity threshold a controller would act on.

The Complex Regime: Fidelity Becomes a Distribution Lengthen the links and shorten the coherence time and the picture changes qualitatively. The delivered fidelity is no longer a number but a distribution: pairs that assembled quickly arrive fresh, pairs that waited arrive degraded, and across a representative three-hop path the per-pair fidelity spreads with a standard deviation above 0.1. The static formula, blind to waiting time, sits well above the truth, predicting roughly 0.86 where the simulator’s expected fidelity is about 0.64. This gap is not measurement noise; it is the decoherence the formula cannot represent. Recovering the expectation from the simulator is possible but√ costly: the error of an n-run estimate falls only as 1/ n, so halving it demands four times the runs. Accuracy has a price here, and that price is what a learned surrogate amortizes.

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Fig. 2. Decision-aware machine-learning workflow for quantum QoT estimation. The upper part shows the offline training workflow: candidate path configurations are evaluated with a quantum-network simulator to produce fidelity labels, build a QoT dataset, and train a machinelearning surrogate. The lower part shows the online decision flow: a fidelity-estimation request is mapped to analytical computation when the formula is valid and its inputs are fresh, detailed simulation when runtime allows, or the trained machine-learning surrogate when repeated simulation is too costly at decision time.

Learning the Expected Fidelity On a dataset of 390 distinct configurations spanning two to four hops, we trained a range of regressors and compared them, under five-fold cross-validation, against the analytical formula calibrated per fold. A single effective per-hop quality factor g, applied as g h to the Werner product of an h-hop path, is fit by least squares on each training fold, so that the formula receives the same data advantage as the learned models (Fig. 3). Tree ensembles, namely random forests [11], extremely randomized trees [12], and gradient boosting, as implemented in scikit-learn [13], predict the expected fidelity with a mean-squared error roughly five to six times lower than the calibrated formula, and about 2.5× lower mean absolute error. The conclusion does not hinge on model class: a plain linear regressor performs almost as well as the tree ensembles, which tells us that the mapping from features to fidelity is close to linear once the right features are supplied, so a compact, interpretable model already captures it. A neural network, the model with the most capacity, does worst of all. It is the only learned model that fails to beat the formula, and its error varies the most across folds. This is not a tuning problem. When the target is simple and the dataset small, a flexible network has no rich pattern to learn and ends up fitting noise. The ordering is itself the message: capacity beyond what the modest, path-local target requires buys nothing, which is precisely why a heavier structural model

is unnecessary in this regime. A learning curve (Fig. 3) shows the surrogate overtaking the formula after roughly eighty labeled configurations and continuing to improve without early saturation, which both justifies the dataset size and confirms that the model is fitting signal rather than the small label noise. The operational payoff is the point of the exercise: once trained, the surrogate returns a prediction in well under a millisecond, against seconds for a simulator run, so it can sit inside a control loop where re-simulation cannot. Generalizing to Unseen Path Regimes The deployment question is not whether a surrogate fits configurations like those it trained on, but whether it transfers to networks it has never seen, and the baseline it must beat there is not the formula but re-running the simulator. We probe this with three families of paths whose link-length regimes are deliberately distinct: a short-link “grid” family, an intermediate “ring” family, and a longhaul “backbone” family. We train on two families and test on the unseen third (Fig. 4). Every model beats the calibrated formula on every held-out family, by roughly a factor of two in mean absolute error, and leave-onefamily-out extrapolation is nearly as accurate as ordinary interpolation. This is evidence that the learned mapping is a genuine, transferable function of the path features rather than a memorized lookup. More precisely, these are unseen

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Fig. 3. Learning the expected fidelity. a) Five-fold cross-validated error of six regressors against the per-fold-calibrated formula; tree ensembles cut the error ∼5–6×. b) Learning curve: the surrogate overtakes the formula after ∼80 configurations and keeps improving.

From Estimation to Action

Fig. 4. Generalization to unseen path regimes. Leave-one-familyout error over 270 paths (90 per family; train on two of ring/grid/backbone, test on the third) beside pooled-interpolation error; every model beats the formula on each unseen family, and extrapolation is close to interpolation.

path and link regimes, not unseen meshed topologies. In this regime the fidelity is a path-local quantity and the graph structure carries no additional signal; learning over true network structure, where a graph neural network would earn its inductive bias, requires a setting in which a path’s fidelity depends on the rest of the network, which we revisit among the open challenges. To summarize, the above study establishes a clear boundary. In the simple regime the formula is accurate enough that a surrogate is unnecessary. In the complex regime the formula is biased due to its inability to account for decoherence. While the simulator remains accurate, it operates too slowly for a real-time decision loop. A learned estimator successfully bridges this gap, achieving simulator-grade accuracy at formula-grade speed. Notably, it demonstrates this capability both on trained configurations and on unseen path regimes. That intersection, not machine learning by default, is where learning belongs.

A fidelity estimate is only useful as it feeds a decision, and several control-plane functions already depend on one: fidelity-aware routing and purification schemes assume such an estimate is available at path-selection time [14]. Path and swap-order selection need a ranking of candidate routes before any resource is committed. Deciding whether to purify a pair, and how many rounds, trades delivered fidelity against rate and requires an estimate of what the pair would otherwise arrive with. Memory allocation, that is, how many attempts and how much waiting time to budget for a request, is itself an important factor in the fidelity a path will deliver. Each of these currently leans on the same two options this article has been contrasting: a fast but regime-blind formula, or infrequent and quickly stale monitoring. A learned estimator is fast enough to rank many candidate paths or swap orders online, which a simulator cannot be. In the provisioning language optical networks already use, this narrows the fidelity margin a controller must reserve against uncertainty, which translates directly into fewer wasted purification rounds and less over-allocated memory. Finally, while this study focuses on establishing the foundational premise, that is, delivering an accurate, lowlatency fidelity prediction suitable for a control loop, it sets the stage for further development. Future research can extend these findings to integrate routing and scheduling policies, addressing complex challenges such as contention and joint optimization across concurrent requests. Open Challenges Several questions decide whether learned estimators reach deployment: • Sim-to-real. There is no corpus of real quantumnetwork telemetry. How do we validate and transfer to hardware?

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Data-generation cost. Active learning and design of experiments confront the surrogate paradox: we simulate in order to learn not to simulate. • Stochastic targets. Fidelity is a random variable; predicting distributions or quantiles enables risk-aware provisioning. • Physics-informed models. Encoding the Werner and decay structure and learning only the residual buys sample efficiency and interpretability. • Structural generalization. Graph neural networks are the right tool for axis B, and for joint fidelity, rate, and latency targets. •

Conclusion This article studied when machine learning is useful for estimating end-to-end fidelity in quantum networks. We have argued that the choice among computing, simulating, and learning is a structured decision, not a default. Learning should replace computation only when the deployable formula is biased or unfeedable, and it should replace simulation only when accuracy is needed faster, or at a scale, that repeated simulation cannot provide. Learning is most useful at the intersection of those two conditions. Our simulation-based study located that intersection concretely. In a well-provisioned regime, the analytical formula is accurate enough that a surrogate is unnecessary. Once decoherence over random waiting times sets in, the formula is biased by a dynamical effect it cannot represent. While the simulator remains accurate, it operates too slowly for a real-time decision loop. A learned estimator bridges this gap, achieving simulator-grade accuracy at formula-grade speed from physical features alone, on unseen path regimes as well as familiar ones. The optical QoT experience supplies the roadmap, but the quantum twists, namely decoherence, no-cloning, and consumption, redefine which regime dominates and, with it, when learning is truly the right tool. Disclaimer Any mention of commercial products or reference to commercial organizations is for information only; it does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the products mentioned are necessarily the best available for the purpose. References [1] S. Wehner, D. Elkouss, and R. Hanson, “Quantum internet: A vision for the road ahead,” Science, vol. 362, no. 6412, p. eaam9288, 2018. [2] A. Abane, M. Cubeddu, V. S. Mai, and A. Battou, “Entanglement routing in quantum networks: A comprehensive survey,” IEEE Transactions on Quantum Engineering, 2025. [3] C. Cicconetti, M. Conti, and A. Passarella, “Quality of service in quantum networks,” IEEE Network, vol. 36, no. 5, pp. 24–31, 2022. [4] A. Ferrari, M. Filer, K. Balasubramanian, Y. Yin, E. Le Rouzic, J. Kundrát, G. Grammel, G. Galimberti, and V. Curri, “GNPy: an open source application for physical layer aware open optical networks,” Journal of Optical Communications and Networking, vol. 12, no. 6, pp. C31–C40, 2020.

[5] A. D’Amico, S. Straullu, G. Borraccini, E. London, S. Bottacchi, S. Piciaccia, A. Tanzi, A. Nespola, G. Galimberti, S. Swail et al., “Enhancing lightpath QoT computation with machine learning in partially disaggregated optical networks,” IEEE Open Journal of the Communications Society, vol. 2, pp. 564– 574, 2021. [6] M. Ibrahimi, H. Abdollahi, C. Rottondi, A. Giusti, A. Ferrari, V. Curri, and M. Tornatore, “Machine learning regression for QoT estimation of unestablished lightpaths,” Journal of Optical Communications and Networking, vol. 13, no. 4, pp. B92–B101, 2021. [7] I. Khan, M. Bilal, M. Siddiqui, M. Khan, A. Ahmad, M. Shahzad, and V. Curri, “QoT estimation for light-path provisioning in un-seen optical networks using machine learning,” in 2020 22nd international conference on transparent optical networks (ICTON). IEEE, 2020, pp. 1–4. [8] X. Wu, A. Kolar, J. Chung, D. Jin, T. Zhong, R. Kettimuthu, and M. Suchara, “SeQUeNCe: a customizable discrete-event simulator of quantum networks,” Quantum Science & Technology, vol. 6, no. 4, p. 045027, 2021. [9] T. Coopmans, R. Knegjens, A. Dahlberg, D. Maier, L. Nijsten, J. de Oliveira Filho, M. Papendrecht, J. Rabbie, F. Rozpędek, M. Skrzypczyk et al., “NetSquid, a NETwork Simulator for QUantum Information using Discrete events,” Communications Physics, vol. 4, no. 1, p. 164, 2021. [10] L. Prielinger, Á. G. Iñesta, and G. Vardoyan, “Surrogate-guided optimization in quantum networks,” npj Quantum Information, vol. 11, no. 1, p. 89, 2025. [11] L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001. [12] P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine learning, vol. 63, no. 1, pp. 3–42, 2006. [13] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al., “Scikit-learn: Machine learning in python,” the Journal of machine Learning research, vol. 12, pp. 2825– 2830, 2011. [14] J. Li, M. Wang, K. Xue, R. Li, N. Yu, Q. Sun, and J. Lu, “Fidelity-guaranteed entanglement routing in quantum networks,” IEEE Transactions on Communications, vol. 70, no. 10, pp. 6748–6763, 2022.

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