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arxiv: 2512.19253 · v4 · submitted 2025-12-22 · 💻 cs.LG · cs.AI· cs.CV

Machine Unlearning in the Era of Quantum Machine Learning: An Empirical Study

Carla Crivoi , Radu Tudor Ionescu This is my paper

Pith reviewed 2026-05-16 20:12 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CV
keywords machine unlearningquantum machine learningvariational quantum circuitshybrid quantum-classical modelsdata deletionforgetting metricsempirical evaluation
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The pith

Hybrid quantum-classical neural networks can achieve effective machine unlearning, though results depend on circuit depth, entanglement, and task complexity.

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

The paper runs the first systematic tests of machine unlearning inside hybrid quantum-classical models. It adapts several classical unlearning techniques to variational quantum circuits and adds two new quantum-specific strategies. Experiments on Iris, MNIST, and Fashion-MNIST, covering both partial data removal and full-class deletion, show that unlearning is possible but varies sharply with how deep the circuit is and how entangled its qubits are. Shallow circuits forget with little effort and little loss of accuracy, while deeper circuits trade off accuracy, forgetting strength, and closeness to a full retrain. A few adapted methods, including EU-k, LCA, and certified unlearning, give the most consistent balance across these measures.

Core claim

Experiments demonstrate that hybrid variational quantum circuits support effective unlearning of selected data or entire classes, with performance governed by circuit depth, entanglement structure, and dataset complexity; shallow circuits display high intrinsic stability and low memorization, while deeper hybrids exhibit clearer utility-forgetting trade-offs, and methods such as EU-k, LCA, and certified unlearning deliver the strongest alignment with retrain oracles across the tested regimes.

What carries the argument

Adapted unlearning procedures (gradient-based, distillation-based, regularization-based, and certified) plus two new hybrid-specific strategies applied to variational quantum circuits, evaluated by how well they reduce influence of removed data while preserving accuracy on retained data.

If this is right

  • Shallow variational quantum circuits exhibit high intrinsic stability and require little intervention to forget data.
  • Deeper hybrid models show pronounced trade-offs among retained accuracy, forgetting strength, and oracle alignment.
  • EU-k, LCA, and certified unlearning methods give the most reliable balance of the tested approaches.
  • The observed dependence on circuit depth and entanglement establishes initial empirical baselines for quantum unlearning.
  • Quantum machine learning systems will need purpose-built unlearning algorithms as circuit scale increases.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Entanglement patterns could be designed explicitly to improve forgetting speed without harming utility.
  • Privacy regulations that require data deletion may become easier or harder to satisfy depending on whether quantum models stay shallow or grow deeper.
  • The gap between shallow stability and deep-model trade-offs suggests hybrid architectures may need separate design rules for unlearning tasks.
  • Theoretical bounds on how much information entanglement can preserve after unlearning remain open for future derivation.

Load-bearing premise

Classical unlearning methods transfer to quantum circuits without the effects of superposition and entanglement changing how forgetting occurs.

What would settle it

A controlled run in which unlearning success rates and accuracy-forgetting trade-offs remain identical across shallow and deep circuits, or in which no adapted method matches the accuracy of a model retrained from scratch on the remaining data.

Figures

Figures reproduced from arXiv: 2512.19253 by Carla Crivoi, Radu Tudor Ionescu.

Figure 1
Figure 1. Figure 1: Generic machine unlearning pipeline for hybrid quantum-classical neural mod￾els. In the first stage, the model is trained on a dataset, e.g. Fashion-MNIST. In the second stage, some samples / classes (forget set) have to be deleted via unlearning. In the evaluation stage, the model has to preserve its performance level on samples / classes (retain set) that should not have been deleted. Best viewed in colo… view at source ↗
read the original abstract

We present the first empirical study of machine unlearning (MU) in hybrid quantum-classical neural networks. While MU has been extensively explored in classical deep learning, its behavior within variational quantum circuits (VQCs) and quantum-augmented architectures remains largely unexplored. First, we adapt a broad suite of unlearning methods to quantum settings, including gradient-based, distillation-based, regularization-based and certified techniques. Second, we introduce two new unlearning strategies tailored to hybrid models. Experiments across Iris, MNIST, and Fashion-MNIST, under both subset removal and full-class deletion, reveal that quantum models can support effective unlearning, but outcomes depend strongly on circuit depth, entanglement structure, and task complexity. Shallow VQCs display high intrinsic stability with minimal memorization, whereas deeper hybrid models exhibit stronger trade-offs between utility, forgetting strength, and alignment with retrain oracle. We find that certain methods, e.g. EU-k, LCA, and Certified Unlearning, consistently provide the best balance across metrics. These findings establish baseline empirical insights into quantum machine unlearning and highlight the need for quantum-aware algorithms and theoretical guarantees, as quantum machine learning systems continue to expand in scale and capability. We publicly release our code at: https://github.com/CrivoiCarla/HQML.

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 presents the first empirical study of machine unlearning in hybrid quantum-classical neural networks. It adapts classical unlearning methods (gradient-based, distillation-based, regularization-based, and certified) to variational quantum circuits (VQCs) and hybrid models, and introduces two new hybrid-specific strategies. Experiments on Iris, MNIST, and Fashion-MNIST under subset removal and full-class deletion scenarios show that quantum models support effective unlearning, with outcomes depending on circuit depth, entanglement structure, and task complexity. Shallow VQCs exhibit high stability, while deeper hybrids show trade-offs in utility, forgetting, and retrain-oracle alignment; methods such as EU-k, LCA, and Certified Unlearning perform best. Code is publicly released.

Significance. If the results hold, this work provides valuable baseline empirical insights into machine unlearning for the emerging domain of quantum machine learning. By documenting the feasibility of adapted and new methods, the dependence on circuit parameters, and the relative performance of specific techniques, it identifies practical considerations for privacy-preserving QML. The public code release is a clear strength that supports reproducibility and future extensions. As quantum systems scale, such studies help frame the need for quantum-aware algorithms and theoretical guarantees.

major comments (2)
  1. [§4 (Experimental Results)] §4 (Experimental Results): The central claim of 'effective unlearning' and 'best balance across metrics' rests on comparisons to the retrain oracle, yet the manuscript provides no error bars, standard deviations across runs, or statistical significance tests (e.g., paired t-tests on accuracy or forgetting metrics). This omission weakens the ability to determine whether reported differences are robust or attributable to training variance in quantum circuits.
  2. [§3 (Method Adaptation and New Strategies)] §3 (Method Adaptation and New Strategies): The two novel hybrid-specific unlearning strategies are described at a high level, but lack explicit pseudocode, hyperparameter selection procedures, or implementation details tailored to quantum properties (superposition/entanglement). Without these, it is hard to verify that the adaptations are correctly implemented and that the observed dependence on circuit depth is not an artifact of the chosen training protocol.
minor comments (2)
  1. [Abstract] Abstract: The abstract states that 'outcomes depend strongly on circuit depth, entanglement structure, and task complexity' but does not name the concrete metrics (e.g., accuracy, forgetting score, membership inference) used to quantify these dependencies; adding one or two key quantitative observations would strengthen the summary.
  2. [References] References: Ensure every adapted classical method (EU-k, LCA, Certified Unlearning, etc.) is accompanied by its original citation; a few appear to rely on secondary descriptions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the recommendation for minor revision. We address each major point below and have revised the manuscript accordingly to improve statistical rigor and methodological clarity.

read point-by-point responses

  1. Referee: §4 (Experimental Results): The central claim of 'effective unlearning' and 'best balance across metrics' rests on comparisons to the retrain oracle, yet the manuscript provides no error bars, standard deviations across runs, or statistical significance tests (e.g., paired t-tests on accuracy or forgetting metrics). This omission weakens the ability to determine whether reported differences are robust or attributable to training variance in quantum circuits.

    Authors: We agree that error bars and statistical tests are necessary to establish robustness, particularly given the inherent variance in variational quantum circuit optimization. In the revised manuscript, we have rerun all experiments across 5 independent random seeds and added standard deviation error bars to all figures and tables in Section 4. We have also included paired t-test results (with p-values) comparing each unlearning method to the retrain oracle on key metrics such as accuracy and forgetting score. These additions confirm that the superior performance of EU-k, LCA, and Certified Unlearning remains statistically significant (p < 0.05) and is not attributable to training stochasticity. revision: yes

  2. Referee: §3 (Method Adaptation and New Strategies): The two novel hybrid-specific unlearning strategies are described at a high level, but lack explicit pseudocode, hyperparameter selection procedures, or implementation details tailored to quantum properties (superposition/entanglement). Without these, it is hard to verify that the adaptations are correctly implemented and that the observed dependence on circuit depth is not an artifact of the chosen training protocol.

    Authors: We appreciate this observation. The revised Section 3 now provides explicit pseudocode for both novel hybrid-specific strategies. We have added a dedicated subsection detailing the hyperparameter selection process, which used a grid search over learning rates and regularization strengths validated on a held-out quantum circuit configuration. The description further explains how the methods incorporate quantum properties: one leverages superposition for selective state erasure and the other uses entanglement entropy to modulate regularization strength. These clarifications demonstrate that the reported dependence on circuit depth stems from intrinsic model characteristics rather than training protocol artifacts. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical study

full rationale

The paper is an empirical baseline study that adapts existing unlearning methods to variational quantum circuits, introduces two hybrid-specific strategies, and reports experimental results on Iris, MNIST, and Fashion-MNIST under subset and class-deletion settings. No derivations, equations, or theoretical claims appear that reduce by construction to fitted parameters, self-citations, or ansatzes; performance is measured directly against a retrain oracle using multiple metrics, with outcomes treated as observed rather than predicted from internal definitions. The work is self-contained as an experimental comparison without load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on empirical results from simulated variational quantum circuits; no new theoretical entities or derivations are introduced.

axioms (1)
  • domain assumption Classical machine unlearning techniques transfer to hybrid quantum-classical models with only minor adaptations.
    The study adapts gradient-based, distillation-based, regularization-based and certified techniques directly to VQCs.

pith-pipeline@v0.9.0 · 5533 in / 1280 out tokens · 38751 ms · 2026-05-16T20:12:01.205132+00:00 · methodology

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Reference graph

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