Qiskit Variational Quantum Classifier on the Pulsar Classification Problem

Anna B. M. Souza; Clebson Cruz; Marcelo A. Moret

REVIEW 4 major objections 5 minor 71 references

Qiskit Variational Quantum Classifier on the Pulsar Classification Problem

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A Qiskit variational quantum classifier reaches 95 percent accuracy on pulsar candidates from the HTRU-2 survey.

desk verdict Routine QML application to HTRU-2 whose headline accuracy is arithmetically impossible given the stated class imbalance; the reported numbers don't add up. read the letter →

arxiv 2505.15600 v1 pith:PUGHY4AJ submitted 2025-05-21 quant-ph

Anna B. M. Souza , Clebson Cruz , Marcelo A. Moret This is my paper · ORCID

classification quant-ph

keywords quantummachinelearningvariationalclassifierpulsarclassificationHTRU-2featureselectionQiskitimbalanced

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

Quantum machine learning is still young, and this paper asks whether a ready-made variational circuit can handle a real astronomy task: separating pulsars from non-pulsars in the HTRU-2 candidate dataset, where only 9.8 percent of examples are true pulsars. The paper reports that the Qiskit Variational Quantum Classifier reaches an accuracy of 0.950 with three features, using the ZZ feature map and the EfficientSU2 ansatz, with the best Matthews correlation coefficient reaching 0.670. The point is constructive: a small three-qubit hybrid circuit is easy to deploy and can perform well on an imbalanced astrophysical dataset, but the choice of feature selection, data encoding, ansatz, and entanglement pattern matters measurably. The conclusion also notes that using the same circuit as both feature map and ansatz yields high precision at the cost of much lower accuracy.

What carries the argument

The machinery is the Variational Quantum Classifier (VQC), a hybrid quantum-classical algorithm built from two parameterized circuits: a feature map that encodes classical data into qubit rotations (the Pauli or ZZ feature map, both angle-embedding circuits) and an ansatz whose parameters are trained classically (Real Amplitudes or EfficientSU2). The number of qubits equals the number of features, and the circuits are repeated twice with linear, circular, or full CNOT entanglement. The VQC carries the argument because every reported comparison—feature count, feature-selection method, training size, encoding, ansatz, and entanglement—is a variation on this circuit recipe.

What would settle it

Rerun the three-feature VQC configuration with feature selection fitted inside each training fold of a cross-validation loop on the full HTRU-2 dataset, and compare the held-out accuracy with the reported 0.950. If the cross-validated accuracy falls materially below 0.950, the headline number came from leakage or from selecting the best of many configurations on the test set.

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Extended reading notes

Core claim

The paper's central claim is that the Variational Quantum Classifier, as implemented in Qiskit's machine learning library, is a usable and reasonably accurate tool for pulsar candidate classification on the HTRU-2 dataset. Concretely, a configuration with three features selected by a statistical score or by absolute correlation with the class, a ZZ or Pauli feature map, and an EfficientSU2 ansatz achieves accuracy up to 0.950 and MCC up to 0.670. The paper attributes the result to the interaction of circuit components rather than to any single ingredient: the best-performing models all use an EfficientSU2 ansatz, while using the data-encoding circuit itself as the ansatz gives high precision but much lower accuracy. The experiments are presented as evidence that hybrid quantum-classical classifiers can be applied to real astronomical surveys now, with tests on real quantum hardware and noisy simulators left to future work.

Load-bearing premise

The reported 0.950 accuracy is only a trustworthy estimate of future performance if feature selection was fitted on the training portion alone and if the best configurations were not chosen by looking at the test set; the paper does not state exactly when the feature-selection step was applied.

Editorial extensions

If this is right

A three-qubit VQC is enough to reach the paper's best results, so the practical resource cost of the classifier stays low on near-term hardware.
Circuit identity matters: every top configuration uses the EfficientSU2 ansatz, while reusing the feature map as the ansatz changes the precision-recall tradeoff and lowers accuracy.
Feature-selection method shifts the error balance: FS1 gives better recall and MCC, while FS2 gives slightly better precision at similar accuracy.
Training on 180 samples produced higher accuracy than training on 300 samples in this setup, which the paper reads as a sign that the model's performance depends on batch composition.
The results support treating hybrid quantum-classical classification as a candidate screening tool for pulsar surveys, with real-device and noisy-simulation tests as the next step.

Reading between the lines

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

The paper does not compare the VQC with classical baselines on the same split, so the practical inference to draw is feasibility rather than superiority; a direct classical benchmark would put the 0.950 number in context.
If the feature-selection order is ambiguous, the result can be stress-tested by repeating the three-feature configuration with feature selection nested inside every training fold.
The strong dependence on entanglement pattern suggests that entanglement topology is a free hyperparameter of quantum classifiers, analogous to the kernel choice in classical support-vector machines, and worth tuning systematically.
Because the best numbers come from a search over many configurations, rerunning the same experiment with a pre-registered configuration list would show how much of the 0.950 is attributable to the search itself.

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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

I read the Qiskit VQC pulsar paper. Bottom line: it's a straightforward application of a standard QML algorithm to a public dataset, and it does that clearly enough, but the headline result doesn't survive contact with its own numbers.

What's new: it's the first VQC run on HTRU-2, as far as the cited literature shows. The paper compares two feature-selection methods, different feature maps and ansatze, and reports accuracy, precision, recall, F1, and MCC. That's a legitimate extension of the prior QCNN/QSVM work, and the circuit descriptions are reasonably clear.

What it does well: the writing is organized; the choice of MCC for an imbalanced dataset is sensible; the setup of the feature maps and ansatze is standard and reproducible from the text.

The soft spots are severe, not minor. First, the evaluation is not trustworthy. The paper selects best-performing models from a grid using test-set accuracy and then filters out configurations that don't make the cut for display. That's data snooping. There are no error bars or repeated seeds. No classical baseline is provided, so even the honest numbers don't tell us whether VQC adds anything.

Second, and more decisively, Table VII is internally inconsistent. With the stated 9.8% pulsar fraction in the test set, the first row's accuracy 0.950, precision 0.375, recall 0.750 is arithmetically impossible; the implied accuracy is about 0.853. The same contradiction repeats across the table. Unless the test set was rebalanced or the metrics were computed on different subsets, and the paper says nothing like that, the reported numbers cannot all be true. The conclusion's "accuracies as high as 0.950" comes straight from this table, so the central claim hangs on numbers that don't add up.

Citation pattern: fine. The relevant QML pulsar papers are cited, and the HTRU-2 reference is there. Self-citation isn't an issue.

Who is this for? Someone curious about whether VQC can touch this dataset might skim it. But as it stands, the paper doesn't support its own conclusion. A serious referee would need to send the authors back for a redesigned evaluation: proper nested model selection, multiple seeds, classical baselines, and a corrected table. That's a substantial rewrite.

My recommendation: desk reject. The internal inconsistency alone is disqualifying, and the contribution is a routine application with no demonstrated advantage. If the authors fix the evaluation, the paper might become a modest data point, but that's a different manuscript.

Referee Report

4 major / 5 minor

Summary. The manuscript applies Qiskit's Variational Quantum Classifier (VQC) to the HTRU-2 pulsar candidate dataset. It normalizes data with MinMaxScaler, compares two feature-selection orderings (FS1 via SelectKBest, FS2 via correlation with the class), varies the number of features/qubits between 2 and 8, training sample sizes of 180 and 300, feature maps (ZZ and Pauli), ansatze (Real Amplitudes and EfficientSU2), and entanglement patterns. Performance is reported via accuracy, precision, recall, F1, and MCC. The central conclusion is that the VQC is easy to use and performs well, with accuracies as high as 0.950 and a best MCC of 0.670.

Significance. The empirical question addressed is of some interest for QML applications in astrophysics, and the manuscript is honest about the class imbalance by reporting MCC alongside accuracy. Its strength is a fairly systematic sweep of circuit components and feature-selection choices for a specific Qiskit implementation. However, the contribution is incremental relative to existing QML pulsar classification studies, and the central empirical claim is currently unsupported: the headline numbers in Table VII are internally inconsistent, the model selection procedure uses test performance, no classical baseline is given, and no code, seeds, or repeated runs are provided. If the experiments were rerun with proper validation and baselines, the paper could be a useful application note, but as it stands the reported 'good performance' is not a reliable measurement.

major comments (4)

[Section IV, Table VII] The headline accuracy entries are arithmetically inconsistent with the precision, recall, and the dataset's stated 9.8% pulsar rate. For a test set with pulsar fraction p, the metrics imply Acc = 1 - p[R(1-P)/P + (1-R)]. With p=0.098, row 1 (Acc 0.950, P 0.375, R 0.750) gives 0.853; row 2 (Acc 0.945, P 0.476, R 1.000) gives 0.892; and row 3 (Acc 0.940, P 0.429, R 1.000) gives 0.870. The paper does not report any rebalancing or filtering of the test set, and Section II A describes the data as having 9.8% pulsars. Either the accuracy or the other metrics are not from the same evaluation, and the Conclusion's 'accuracies as high as 0.950' is therefore unsupported by the reported numbers.
[Section IV, Figs. 7-10 and Table VII] The reported values are selected maxima over a large grid of configurations, with no validation set and no repeated runs. The text states that some of the 'best-performing (BP) models, based on accuracy' were selected and that Table VII entries whose counterpart 'was not high enough' were 'filtered out'. Selecting on the test set makes the headline accuracy an estimate of the search procedure, not of generalization. The authors should report held-out or nested-validation performance with means and standard deviations over repeated runs, or the claim of 'good performance' cannot be evaluated.
[Section II C] It is not stated whether the FS1 and FS2 feature-selection procedures were applied before or after the train/test split. If the feature ranking was computed on the full dataset, class labels of the test set influenced the preprocessing, and all downstream numbers are optimistically biased. This must be clarified and, if leakage exists, the experiments must be rerun with feature selection nested inside the training folds.
[Section IV and Conclusion] No classical baseline is reported for the same data and preprocessing, and no comparison is made to the HTRU-2 results in the cited literature or to the QML pulsar studies cited as Refs. [29,30]. Without a baseline, 'good performance' is not a supported claim: on a dataset with 9.8% positives, a degenerate classifier can already reach roughly 0.902 accuracy, so accuracy alone is insufficient evidence. A comparison against standard classical classifiers (e.g., logistic regression, random forest, SVM) on the same train/test protocol is needed.

minor comments (5)

[Section II A] 'HRTU-2' should be 'HTRU-2'.
[Fig. 8 and Fig. 10 captions] 'representend' should be 'represented', and 'est performing' should be 'best performing'.
[Table III] BP-A and BP-B list identical configurations (ZZ feature map, Real Amplitudes, linear entanglement); if this is not a typo, the labels cannot distinguish the two curves in Fig. 7.
[General] No Qiskit version, random seeds, or code availability are reported, which limits reproducibility of the single-run results.
[Table VII] The notation 'EfficientSU2' is written without a space in some rows; consistent naming would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper reports empirical VQC benchmark results on HTRU-2 and does not derive or predict any quantity from its own fitted inputs; internal metric inconsistencies are a soundness issue, not circular reasoning.

full rationale

The paper's central claim, 'The VQC circuit... achieving accuracies as high as 0.950,' is an observed benchmark result taken from Table VII, not a derived prediction. There is no derivation chain in which an output is defined in terms of an input: accuracy, precision, recall, F1, and MCC are all computed from TP/FP/FN/TN using the standard formulas in Appendix A, and no parameter is fitted and then renamed as a prediction. The feature-selection methods FS1 and FS2 are standard rankers whose outputs are not defined in terms of the final accuracy or MCC, and the feature-map/ansatz choices are varied as independent configuration options. The paper contains no load-bearing self-citations and no uniqueness theorem imported from prior work by the authors. The text does contain a serious empirical inconsistency in Table VII: for a test set with the stated 9.8% pulsar fraction, row 1 (Acc 0.950, Precision 0.375, Recall 0.750) would imply Acc approximately 0.853, and other rows are similarly inconsistent; this is a correctness or reporting defect in the evaluation setup, not circularity. Likewise, the statement that some configurations 'got filtered out, as a way to not introduce bias' indicates that the headline number is a selected maximum over a searched grid, which undermines generalization claims but is not definitional circularity. Because the paper makes no first-principles derivation and never equates an output with its own input by construction, the circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical or mathematical entities; it applies existing Qiskit circuits to a public dataset. The main unexamined costs are the hand-selected hyperparameters and the unstated assumptions about data preprocessing and splitting.

free parameters (5)

Number of features = 3 (and 4 in some runs)
Best-performing models use 3 or 4 features; the number was selected after evaluating 2-8 features on the test set, not derived from first principles.
Feature map type = ZZFeatureMap
All top-accuracy results use the ZZ feature map; selected after comparing Pauli and ZZ feature maps.
Ansatz type = EfficientSU2
The best accuracies in the reported grid all use EfficientSU2; RealAmplitudes was not among the top models in the main comparisons.
Entanglement pattern = circular or full
Selected after comparing linear, circular, and full entanglement; no theoretical reason is given for the choice.
Training data size = 180 and 300
These two sizes were chosen by the authors to study under/overfitting; no criterion for choosing these specific sizes is provided.

assumptions (4)

domain assumption HTRU-2 dataset labels are correct and the eight features are as described in [31].
All classification metrics are computed against these labels; if the labels are noisy or the feature definitions are inaccurate, the reported performance is biased.
domain assumption Qiskit's VQC implementation faithfully realizes the described feature maps, ansatz, and optimizer (SLSQP) with default settings.
The results depend on the correctness of the Qiskit library; the paper provides no independent verification of the circuit implementations.
domain assumption MinMax scaling to [0, pi] preserves the information needed for the classification task.
The feature maps are angle embeddings, so the scaling choice directly affects the data representation; no comparison with other scaling methods is given.
domain assumption The train/test split is random and representative of the dataset distribution.
The paper does not describe the split procedure, random seed, or stratification; if the split is not representative, the reported accuracies do not generalize.

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Cite this review

Pith. "Pith review of Qiskit Variational Quantum Classifier on the Pulsar Classification Problem." pith.science (2026). https://pith.science/paper/PUGHY4AJ

@misc{pith2026250515600,
  author       = {Pith},
  title        = {Pith review of: Qiskit Variational Quantum Classifier on the Pulsar Classification Problem},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PUGHY4AJ}},
  note         = {Machine review of arXiv:2505.15600}
}

read the original abstract

Quantum Machine Learning is a new computational tool that combines the quantum properties from quantum computing with the pattern recognition from machine learning. In this paper, we apply the Variational Quantum Classifier algorithm to the problem of pulsar classification of candidates from the High Time Resolution Universe 2 dataset. We use Qiskit Machine Learning circuits to compare the performance of the model using different feature selection methods, various number of features and training data size. Comparisons on the model from changing the data encoding and ansatz options are also reported. Keywords: Quantum Computing, Quantum Machine Learning, Astrophysics, Pulsars

Figures

Figures reproduced from arXiv: 2505.15600 by the authors.

**Figure 2.** FIG. 2. Box plot of features related to the DM. Red shows [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** FIG. 3. Pearson correlation matrix of all features in the [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (7 more)

**Figure 5.** Figure 5: FIG. 5. Quantum circuit for the angular data encoding sub [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]

**Figure 8.** Figure 8: FIG. 8. Comparison between three and four features fixed to [PITH_FULL_IMAGE:figures/full_fig_p005_8.png]

**Figure 9.** Figure 9: FIG. 9. Comparison between the best-performing models for [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]

**Figure 10.** Figure 10: FIG. 10. Comparison of the est performing VQC models for [PITH_FULL_IMAGE:figures/full_fig_p006_10.png]

**Figure 11.** Figure 11: FIG. 11. CNOT-gate dispositions in a N qubits quantum [PITH_FULL_IMAGE:figures/full_fig_p007_11.png]

**Figure 12.** Figure 12: FIG. 12. CNOT-gate disposition for the full entanglement [PITH_FULL_IMAGE:figures/full_fig_p007_12.png]

**Figure 13.** Figure 13: FIG. 13. Complete ZZ Feature map circuit with linear entan [PITH_FULL_IMAGE:figures/full_fig_p008_13.png]

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