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arxiv: 2604.11541 · v1 · submitted 2026-04-13 · 🪐 quant-ph · cs.ET

A Systematic Study of Noise Effects in Hybrid Quantum-Classical Machine Learning

Bhavna Bose , Muhammad Faryad This is my paper

Pith reviewed 2026-05-10 15:54 UTC · model grok-4.3

classification 🪐 quant-ph cs.ET
keywords quantum machine learningnoise effectsvariational quantum classifierNISQ hardwareclassical noisequantum decoherencehybrid modelsTitanic dataset
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The pith

Classical input noise intensifies quantum decoherence effects in variational quantum classifiers, causing less stable training and lower accuracy.

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

The paper systematically tests a variational quantum classifier on the Titanic dataset while adding realistic noise at two stages: classical feature corruption before encoding, and quantum channel errors during circuit execution. It applies speckle, impulse, quantization, and dropout noise to the classical inputs, then layers depolarizing, damping, Pauli, and readout noise in the quantum simulator. Results show that the classical noise makes quantum decoherence markedly worse, producing unstable training curves and reduced classification accuracy. A reader should care because near-term quantum machine learning must run on imperfect hardware and imperfect data, so ignoring either noise source gives an unrealistic picture of performance.

Core claim

Using the Titanic dataset as a benchmark, a range of dataset-level noise models including speckle noise, impulse noise, quantization noise, and feature dropout are applied to classical features prior to quantum encoding using a ZZ feature map. In parallel, hardware-inspired quantum noise channels such as depolarizing noise, amplitude damping, phase damping, Pauli errors, and readout errors are incorporated at the circuit level using the Qiskit Aer simulator. The experimental results indicate that noise in classical input data can significantly intensify the effects of quantum decoherence, resulting in less stable training and noticeably lower classification accuracy.

What carries the argument

Variational quantum classifier with ZZ feature map, evaluated under simultaneous classical input noise models and quantum circuit noise channels simulated in Qiskit Aer.

Load-bearing premise

The selected classical noise models and quantum channels together with the Qiskit Aer simulator give a faithful picture of real NISQ hardware and data acquisition conditions.

What would settle it

Running the identical variational quantum classifier and noise injection protocol on physical NISQ hardware with real sensor data and measuring whether accuracy and training stability degrade to the same degree as in the simulator.

Figures

Figures reproduced from arXiv: 2604.11541 by Bhavna Bose, Muhammad Faryad.

Figure 1
Figure 1. Figure 1: FIGURE 1: Block diagram of the proposed methodology [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIGURE 2: ZZFeatureMap used for encoding classical [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIGURE 3: Hardware-efficient variational quantum neu [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIGURE 4: Complete variational quantum classifier obtained by composing the ZZFeatureMap with the hardware [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIGURE 5: Loss convergence curves for all circuit-level noise configurations under [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIGURE 6: Training and testing accuracy comparison under [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIGURE 7: Loss convergence curves under [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIGURE 8: Accuracy comparison under [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIGURE 9: Loss convergence curves under [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIGURE 10: Accuracy comparison under [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIGURE 11: Loss convergence curves under [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIGURE 12: Accuracy comparison under [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIGURE 13: Loss convergence curves under [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: FIGURE 14: Accuracy comparison under [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: FIGURE 15: Loss convergence curves under [PITH_FULL_IMAGE:figures/full_fig_p016_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: FIGURE 16: Accuracy comparison under [PITH_FULL_IMAGE:figures/full_fig_p016_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: FIGURE 17: Loss convergence curves under [PITH_FULL_IMAGE:figures/full_fig_p017_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: FIGURE 18: Accuracy comparison under [PITH_FULL_IMAGE:figures/full_fig_p017_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: FIGURE 19: Loss convergence curves under [PITH_FULL_IMAGE:figures/full_fig_p018_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: FIGURE 20: Accuracy comparison under [PITH_FULL_IMAGE:figures/full_fig_p018_20.png] view at source ↗
read the original abstract

Near-term quantum machine learning (QML) models operate in environments wherein noise is unavoidable, arising from both imperfect classical data acquisition and the limitations of noisy intermediate-scale quantum (NISQ) hardware. Although most existing studies have focused primarily on quantum circuit noise in isolation, the combined influence of corrupted classical inputs and quantum hardware noise has received comparatively little attention. In this work, we present a systematic experimental study of the robustness of a variational quantum classifier under realistic multi-level noise conditions. Using the Titanic dataset as a benchmark, a range of dataset-level noise models-including speckle noise, impulse noise, quantization noise, and feature dropout are applied to classical features prior to quantum encoding using a ZZ feature map. In parallel, hardware-inspired quantum noise channels such as depolarizing noise, amplitude damping, phase damping, Pauli errors, and readout errors are incorporated at the circuit level using the Qiskit Aer simulator. The experimental results indicate that noise in classical input data can significantly intensify the effects of quantum decoherence, resulting in less stable training and noticeably lower classification accuracy. Together, these observations emphasize the importance of designing and evaluating quantum machine learning pipelines with noise in mind, and highlight the need to consider classical and quantum noise simultaneously when assessing QML performance in the NISQ era

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reports a simulation study on the Titanic dataset using a variational quantum classifier with ZZ feature map, systematically applying classical input corruptions (speckle, impulse, quantization, feature dropout) before encoding and quantum noise channels (depolarizing, amplitude damping, phase damping, Pauli, readout) via Qiskit Aer to assess combined effects on training stability and classification accuracy.

Significance. If the observations hold, the work supplies concrete empirical evidence that classical data noise can amplify quantum decoherence effects in hybrid QML, supporting the broader point that NISQ-era pipelines must treat both noise sources jointly. The use of a public benchmark dataset and standard simulator is a strength for reproducibility.

major comments (2)
  1. [Results] Results section: the reported accuracy reductions and training instability lack error bars, standard deviations across repeated runs, or any statistical significance tests, so the claim that classical noise 'significantly intensify' quantum effects rests on point estimates whose robustness cannot be evaluated.
  2. [Methods] Methods section: the paper asserts that the chosen classical noise models and quantum channels provide 'realistic multi-level noise conditions,' yet supplies no quantitative validation against measured NISQ hardware noise profiles or real data-acquisition statistics, leaving the generalizability of the intensification observation untested.
minor comments (2)
  1. [Abstract] The abstract and results text use qualitative phrases such as 'noticeably lower' and 'less stable' without reporting the actual accuracy deltas or stability metrics relative to the noiseless baseline.
  2. [Methods] Hyperparameter choices for the variational circuit (learning rate, number of layers, optimizer) and the precise noise strengths are not tabulated or justified, hindering exact reproduction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and positive assessment of the work's significance for NISQ-era QML. We address each major comment below and have revised the manuscript accordingly to improve statistical rigor and clarify the scope of the noise models.

read point-by-point responses

  1. Referee: [Results] Results section: the reported accuracy reductions and training instability lack error bars, standard deviations across repeated runs, or any statistical significance tests, so the claim that classical noise 'significantly intensify' quantum effects rests on point estimates whose robustness cannot be evaluated.

    Authors: We agree that reporting variability is essential for robust claims. In the revised manuscript we have rerun all experiments across 10 independent random seeds per noise configuration, added error bars representing standard deviation to the accuracy plots and tables, and included a short description of the repeated-run protocol in the results section. These additions directly support the observation that classical noise amplifies quantum effects by showing consistent trends across runs. revision: yes

  2. Referee: [Methods] Methods section: the paper asserts that the chosen classical noise models and quantum channels provide 'realistic multi-level noise conditions,' yet supplies no quantitative validation against measured NISQ hardware noise profiles or real data-acquisition statistics, leaving the generalizability of the intensification observation untested.

    Authors: The classical corruption models follow standard definitions from the signal-processing literature, and the quantum channels are the canonical NISQ models provided by Qiskit Aer. We acknowledge that the manuscript did not include a direct quantitative comparison to hardware-calibrated noise profiles. In the revision we have added citations to prior benchmarking studies that validate these models, expanded the methods text to justify the parameter ranges chosen, and inserted a dedicated limitations paragraph in the discussion that explicitly notes the simulation-based nature of the study and the consequent limits on device-specific generalizability. revision: partial

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper is a simulation-based empirical study using Qiskit Aer to apply classical noise models (speckle, impulse, quantization, feature dropout) and quantum channels (depolarizing, amplitude damping, etc.) to a ZZ feature map classifier on the Titanic dataset. No derivation chain, equations, or parameter fitting is present; results are direct observations from controlled experiments comparing accuracy and stability across noise conditions. The central claim follows from these comparisons without reduction to self-definitions, fitted inputs renamed as predictions, or load-bearing self-citations. This is a standard honest non-finding for an experimental report.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The study is purely empirical and introduces no new mathematical parameters, axioms, or postulated entities; all noise models are drawn from standard literature and the simulator.

pith-pipeline@v0.9.0 · 5526 in / 1075 out tokens · 41221 ms · 2026-05-10T15:54:27.346327+00:00 · methodology

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Reference graph

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