Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region

Jamal Al-Karaki; Marwan Omar; Muhammad Al-Zafar Khan

REVIEW 4 major objections 6 minor 35 references

Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper reports that a quantum support-vector classifier reaches 75% accuracy on a 32-point water-quality dataset from Durban, outperforming every quantum neural network variant it tested.

desk verdict Honest small-case QML application, but the missing train/test split and an ill-defined QNN make the QSVC-vs-QNN comparison unsupported. read the letter →

arxiv 2411.18141 v1 pith:TJ4VOHQY submitted 2024-11-27 quant-ph cs.AIcs.LG

Muhammad Al-Zafar Khan , Jamal Al-Karaki , Marwan Omar This is my paper · ORCID

classification quant-phcs.AIcs.LG

keywords WaterQualityPredictionQuantumMachineLearningSupportVectorClassifierNeuralNetworkDeadNeuronProblemClassImbalanceUmgeniCatchmentEscherichiacoli

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 applies two quantum machine learning methods to a small field dataset from the Umgeni catchment (U20A) in Durban, where 32 sampling locations were labeled acceptable or not acceptable for recreation by an E. coli threshold of 235 MPN/100 mL. Its central claim is that the quantum support-vector classifier (QSVC) is easier to implement and more accurate than the quantum neural network (QNN) on this data: the polynomial and RBF quantum kernels both reach 75% accuracy, while the best QNN variant reaches 50%. The QNN repeatedly collapsed into a dead-neuron state in which every output was constant, so only accuracy and loss could be compared. The authors position the work as a new application domain for QML rather than a new algorithm, and they deliberately do not compare against classical models.

What carries the argument

The load-bearing object is the quantum kernel $K(x_i,x_j)=|\langle\psi(x_i)|\psi(x_j)\rangle|^2$: classical water-quality features are encoded into quantum states by a variational feature map, the kernel measures state overlap, and classical SVM machinery then finds the separating hyperplane. For the QNN, the central object is the parametrized variational circuit $U(\theta)$ with angle or amplitude encoding, trainable rotation gates, and measurement of an observable $\langle\hat{O}\rangle$; its gradient-based training is what collapsed into the dead-neuron regime. The paper's comparison is therefore between a kernel method whose quantum part is a fixed feature map and a variational circuit whose parameters must be optimized.

What would settle it

Run leave-one-out cross-validation on the original 32 measurements, keeping the three acceptable samples intact, and report accuracy for each fold; if the QSVC's average held-out accuracy is at or near 50%, or if it never predicts the acceptable class, the claimed QSVC advantage over the QNN is not established.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that a quantum kernel method can classify recreational water quality from E. coli measurements more reliably than a variational quantum neural network can. Using a quantum feature map to encode the field measurements into quantum states, the QSVC with polynomial and RBF kernels produced identical metrics, including accuracy 0.7500, F1 0.8000, and perfect recall; the linear kernel scored lower. The QNN, tested across optimizers, learning rates, weight initializations, and noise models, either stayed at loss near 0.4996 or drifted from 0.4167 to 0.5000 accuracy, and its precision, recall, and F1 were all 0 because of the dead-neuron failure. The paper concludes that for this dataset the QSVC is the pragmatic choice, and that the raw data alone is insufficient for an effective QNN without feature engineering.

Load-bearing premise

The whole comparison rests on the assumption that 32 spatially scattered field samples, with only 3 'acceptable' readings before random oversampling, represent the U20A catchment well enough for classifier accuracy to mean anything.

Editorial extensions

If this is right

If QSVC is the better tool, practitioners building small, imbalanced environmental datasets can start with a quantum kernel classifier rather than a variational QNN.
The identical polynomial and RBF performance suggests the chosen feature map makes the classes separable in a way that is insensitive to kernel shape; tuning degree or $\gamma$ is the next test.
The dead-neuron failure means QNN results on raw tabular data should be reported with caution; without feature engineering, precision and recall can be undefined even when accuracy is near 50%.
The paper's 0.75 accuracy is the reported number to beat for future quantum models on the U20A recreation-use dataset.

Reading between the lines

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

Not stated in the paper but likely: because no held-out test set or cross-validation is reported, the 0.75 QSVC accuracy is an in-sample figure and the true generalization error on new Umgeni sites could be substantially lower.
The perfect recall and lower precision of the linear kernel suggest the model almost always predicts the majority class; after random oversampling of only three acceptable samples, minority-class performance estimates are fragile.
A natural extension the paper leaves implicit is a spatial split: train on some monitoring sites, test on others, to see whether the QSVC advantage survives geographic extrapolation.
Since the paper explicitly refuses classical baselines, a skeptical reader could test a classical SVM with the same kernels on the same 32 points; if it matches 0.75, the quantum encoding adds nothing measurable on this dataset.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: the paper is an honest, clearly written application of standard QSVC and QNN methods to a small water-quality dataset, and it explicitly disclaims novelty. But the evaluation protocol is not adequate to support the headline claim that QSVC beats QNN. I agree with the reader's REJECT, and the stress-test note lands: the metrics in Tables 1 and 2 appear to be computed on the oversampled 32-point set with no held-out split, so they measure memorization rather than generalization.

What the paper does well: it is candid about its limitations—data availability is N/A, code is N/A, the authors acknowledge the methods are not novel, and they note the QNN is 'inherently flawed' and that future work with more data points is needed. The writing is clear and the choices are explained. The application domain is genuinely new.

The soft spots are load-bearing. First, no train/test split, no cross-validation, no error bars. With 3 positive samples and random oversampling, 0.75 accuracy is exactly what you'd expect from majority-class memorization. Second, the QNN comparison is ill-defined: Section 4.2 blames the 'ReLU function in the intermediate layers' for the dead neuron problem, but Section 3.2 describes a variational circuit with rotation gates and measurements, with no ReLU layers anywhere. That is a real inconsistency. Third, the paper's refusal to compare to classical baselines, defended as comparing 'apples to bananas,' is a stance that reduces the practical value of the study, though it is not dishonest.

The paper does not deserve a serious referee in its current form. The missing evaluation protocol is a fundamental flaw that would have to be fixed by collecting more data and running proper held-out evaluation, which is beyond a simple revision. I would not cite it, and I would not bring it to reading group. If the authors redo the evaluation on a larger dataset with classical baselines, the application might be worth another look. Desk reject, with a note to the authors that the topic is interesting and the honesty is appreciated.

Referee Report

4 major / 6 minor

Summary. The paper applies two quantum machine learning methods, a quantum support vector classifier (QSVC) and a quantum neural network (QNN), to classify 32 water-quality measurement points in the Umgeni catchment (U20A) as acceptable or unacceptable for recreation based on an E. coli threshold. The authors report QSVC accuracies of 0.5833 (linear) and 0.7500 (polynomial and RBF kernels), and QNN accuracies between 0.4167 and 0.5000 with near-constant loss around 0.5, concluding that the QSVC is easier to implement and yields higher accuracy. The paper contains no train/test split, no error bars, no classical baseline, and no data or code availability.

Significance. If the results were properly validated, the paper would provide a useful application of QML to environmental water-quality monitoring, an area with limited prior QML work. The authors are candid about the QNN's failure and about the small dataset. However, the central comparison between QSVC and QNN rests on metrics that are not shown to estimate generalization, and the QNN model itself is not sufficiently specified. The paper ships no reproducible artifacts (Data availability: N/A, Code availability: N/A), which further weakens its contribution.

major comments (4)

[Section 4, Tables 1 and 2] No train/test split, spatial cross-validation, or any held-out evaluation is reported; the metrics in Tables 1 and 2 appear to be computed on the same 32-point dataset that was balanced by random oversampling. With only 3 original positive samples, random oversampling duplicates the minority class, so the perfect recall (1.0000) and F1/precision values in Table 1 can reflect memorization of duplicated training points rather than generalization. This is load-bearing for the abstract's claim that the QSVC 'yields a higher accuracy.' Please report evaluation on a held-out test set or repeated stratified cross-validation, with per-fold metrics and error bars.
[Section 4.2 vs. Section 3.2] The QNN failure analysis cites ReLU 'dead neuron' problems, Xavier weight initialization with fan-in/fan-out, and 'intermediate layers,' but Section 3.2 defines a QNN as a variational quantum circuit with rotation gates, a unitary U(θ), and a measurement (Eqs. 5-7), with no ReLU activations or classical intermediate layers. The actual trained model is therefore ill-defined, and the comparison in Table 2 is not interpretable. Specify the exact circuit ansatz, number of qubits, feature-encoding scheme, observable, and whether any classical layers with ReLU were included.
[Section 4.2, Eqs. (10)-(11) and Table 2] The noise models (depolarizing noise, amplitude damping) are described mathematically, but no simulation details are given regarding where the noise is inserted, the number of shots, the simulator backend, or the qubit count, and only a single run is reported per configuration. The near-constant loss values (0.4996-0.5000) across epochs and optimizers are thus not diagnostic: they are consistent with a model that always predicts the majority class after oversampling, which would also explain the reported accuracy of 0.5000 and all-zero F1/precision/recall. Multiple random seeds and loss curves are needed to support any ranking among optimizers.
[Section 1 and Section 5] The introduction asserts that QML 'can achieve comparable or superior accuracy to classical methods,' but the paper explicitly declines to build any classical baseline (Section 5), calling such a comparison 'unwarranted.' The reported 0.75 QSVC accuracy has no reference point, so the absolute performance and any claim of superiority over classical methods are unsupported. Either remove the introduction's claim or add a classical SVM trained on the same features and evaluation protocol.

minor comments (6)

[Abstract] The abstract contains a typo ('accraucy' should be 'accuracy'), and the phrase 'weight initializations were considered' is grammatically incomplete.
[Section 4.1, text after Table 1] The sentence 'Both kernels perform similarly well' is ambiguous because the preceding discussion contrasts the linear kernel with the polynomial and RBF kernels; please clarify that 'both' refers to polynomial and RBF.
[Table 2] The column header 'Architectural Components' actually lists hyperparameters (learning rate, optimizer, noise probabilities); rename the column to 'Model Configuration' or similar.
[Eq. (5)] The amplitude-encoding sum is written as (P_{2^n-1}_{i=1} x_i |i>) with the normalization condition; the summation index should start at 0, and the normalization should be enforced on the coefficients, not on the state itself as written.
[References [25] and [26]] The publisher locations for Schuld's books are listed as '???' and should be completed.
[Declarations] The Data availability and Code availability both say 'N/A', making the reported results impossible to reproduce or verify; at minimum, list the exact features used, the preprocessing pipeline, the oversampling procedure, and the software library employed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the QSVC/QNN comparison is an empirical report with no derivation chain that reduces to its inputs; any evaluation-protocol concerns are not circularity.

full rationale

The paper makes no formal derivation from first principles; its central claim is an empirical comparison of QSVC and QNN accuracy on a 32-point dataset. The reported metrics in Tables 1 and 2 are measurements, not quantities defined in terms of the fitted parameters or data in a way that would make the conclusion true by construction. There is no equation in which a predicted value is defined as the fitted value, and no fitted parameter is renamed as a prediction. The paper's self-citations appear only as background references or as proposed future work (e.g., [34]), and none of them supplies the load-bearing premise for the QSVC-vs-QNN comparison. The absence of a described train/test split and the use of random oversampling are serious evaluation-validity concerns, but they are not instances of self-definitional circularity: the paper does not state that the reported numbers are generalization estimates, nor does any equation reduce the comparison to its own inputs. Therefore, under the strict circularity criteria, no circular step is exhibited, and the appropriate score is 0.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claims rest on the reliability of a small proprietary dataset, the validity of the E.coli threshold, the appropriateness of random oversampling, and the correctness of undeclared QML library implementations. No new physical entities are introduced, and the only invented numerical inputs are hyperparameters tuned on the same dataset.

free parameters (6)

Learning rate (eta) = 0.1, 0.01, 0.001
Grid-searched in the QNN experiments; best result reported with 0.01.
Optimizer choice = Adam, Gradient Descent, RMSProp, COBYLA
Selected by training accuracy; Adam reported best.
Depolarizing noise probability p = 0.05
Chosen for noisy QNN experiment.
Amplitude damping rate gamma = 0.02
Chosen for noisy QNN experiment.
Polynomial kernel degree D = 1 (implied)
Authors note degree 1 'might be too simplistic', implying it was set to 1.
RBF kernel gamma = near 0 (implied)
Authors note gamma -> 0 might not be optimal.

assumptions (4)

domain assumption E.coli threshold of 235 MPN/100mL is a valid binary label for recreational water safety.
Used in Section 4 to create the Acceptable/Not Acceptable target variable; drawn from water quality standards.
domain assumption Random oversampling is an appropriate class-balancing method without introducing bias.
Applied in Section 4 to handle 3 vs 29 class imbalance; no comparison with other balancing methods.
ad hoc to paper The quantum feature maps and kernels are correctly implemented by the undisclosed software library.
Section 3.1 describes the general QSVC procedure without specifying the library, circuit, or backend.
domain assumption The 32 field measurements are a representative sample of the U20A study region.
Section 4 states the data came from 32 locations; no sampling design or spatial coverage analysis is provided.

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Cite this review

Pith. "Pith review of Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region." pith.science (2026). https://pith.science/paper/TJ4VOHQY

@misc{pith2026241118141,
  author       = {Pith},
  title        = {Pith review of: Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TJ4VOHQY}},
  note         = {Machine review of arXiv:2411.18141}
}

read the original abstract

In this study, we consider a real-world application of QML techniques to study water quality in the U20A region in Durban, South Africa. Specifically, we applied the quantum support vector classifier (QSVC) and quantum neural network (QNN), and we showed that the QSVC is easier to implement and yields a higher accuracy. The QSVC models were applied for three kernels: Linear, polynomial, and radial basis function (RBF), and it was shown that the polynomial and RBF kernels had exactly the same performance. The QNN model was applied using different optimizers, learning rates, noise on the circuit components, and weight initializations were considered, but the QNN persistently ran into the dead neuron problem. Thus, the QNN was compared only by accraucy and loss, and it was shown that with the Adam optimizer, the model has the best performance, however, still less than the QSVC.

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Water Quality Research Journal 53(1), 3–13 (2018)

Haghiabi, A.H., Nasrolahi, A.H., Parsaie, A.: Water quality prediction using machine learning methods. Water Quality Research Journal 53(1), 3–13 (2018)

work page 2018

[2] [2]

Journal of Hydrology 578, 124084 (2019)

Ahmed, A.N., Othman, F.B., Afan, H.A., Ibrahim, R.K., Fai, C.M., Hossain, M.S., Ehteram, M., Elshafie, A.: Machine learning methods for better water quality prediction. Journal of Hydrology 578, 124084 (2019)

work page 2019

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Eco- Environment & Health 1(2), 107–116 (2022)

Zhu, M., Wang, J., Yang, X., Zhang, Y., Zhang, L., Ren, H., Wu, B., Ye, L.: A review of the application of machine learning in water quality evaluation. Eco- Environment & Health 1(2), 107–116 (2022)

work page 2022

[4] [4]

Journal of Hydrology 585, 124670 (2020)

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Azrour, M., Mabrouki, J., Fattah, G., Guezzaz, A., Aziz, F.: Machine learning algorithms for efficient water quality prediction. Modeling Earth Systems and Environment 8(2), 2793–2801 (2022)

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Talukdar, S., Ahmed, S., Naikoo, M.W., Rahman, A., Mallik, S., Ningthou- jam, S., Bera, S., Ramana, G., et al.: Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms. Journal of Cleaner Production 406, 136885 (2023)

work page 2023

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Water 14(19), 2939 (2022)

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Water 14(21), 3359 (2022) 11

Rustam, F., Ishaq, A., Kokab, S.T., Torre Diez, I., Maz´ on, J.L.V., Rodr ´ ıguez, C.L., Ashraf, I.: An artificial neural network model for water quality and water consumption prediction. Water 14(21), 3359 (2022) 11

work page 2022