Machine unlearning aims to remove the influence of specific training data from a learned model without full retraining. While recent work has begun to explore unlearning in quantum machine learning, existing approaches largely rely on fixed, uniform target distributions and do not explicitly control the trade-off between forgetting and retained model behaviour. In this work, we propose a distribution-guided framework for class-level quantum machine unlearning that treats unlearning as a constrained optimization problem. Our method introduces a tunable target distribution derived from model similarity statistics, decoupling the suppression of forgotten-class confidence from assumptions about redistribution among retained classes. We further incorporate an anchor-based preservation constraint that explicitly maintains predictive behaviour on selected retained data, yielding a controlled optimization trajectory that limits deviation from the original model. We evaluate the approach on variational quantum classifiers trained on the Iris and Covertype datasets. Results demonstrate sharp suppression of forgotten-class confidence, minimal degradation of retained-class performance, and closer alignment with the gold retrained model baselines compared to uniform-target unlearning. These findings highlight the importance of target design and constraint-based formulations for reliable and interpretable quantum machine unlearning.
Machine unlearning aims to remove the influence of specific training data from a learned model without full retraining. While recent work has begun to explore unlearning in quantum machine learning, existing approaches largely rely on fixed, uniform target distributions and do not explicitly control the trade-off between forgetting and retained model behaviour. In this work, we propose a distribution-guided framework for class-level quantum machine unlearning that treats unlearning as a constrained optimization problem. Our method introduces a tunable target distribution derived from model similarity statistics, decoupling the suppression of forgotten-class confidence from assumptions about redistribution among retained classes. We further incorporate an anchor-based preservation constraint that explicitly maintains predictive behaviour on selected retained data, yielding a controlled optimization trajectory that limits deviation from the original model. We evaluate the approach on variational quantum classifiers trained on the Iris and Covertype datasets. Results demonstrate sharp suppression of forgotten-class confidence, minimal degradation of retained-class performance, and closer alignment with the gold retrained model baselines compared to uniform-target unlearning. These findings highlight the importance of target design and constraint-based formulations for reliable and interpretable quantum machine unlearning.
The Right to Be Forgotten (RTBF), as set out in regulations such as the General Data Protection Regulation (GDPR), mandates mechanisms to remove specific data points from trained machine learning models. Machine unlearning is a concept in deep learning that has received significant attention but remains underexplored in Quantum Machine Learning (QML). This paper presents a new model for adaptive data deletion in Variational Quantum Circuits (VQCs), called Sensitivity-Guided Quantum Unlearning (SQU). SQU combines gradient-based sensitivity analysis with a dynamic allocation of privacy budgets and targeted noise injection to eliminate the impact of high-impact samples, then fine-tunes the model on the remaining data. Experiments on three benchmark datasets, Breast Cancer, MNIST, and Fashion-MNIST, indicate that SQU successfully unlearns specified samples without diminishing the model's utility. On the Breast Cancer dataset, SQU matches the accuracy of the gold-standard retrain-from-scratch method (84.4%) while providing a quantifiable and significant privacy benefit, reducing the Membership Inference Attack (MIA) AUC by 0.67%. MNIST and Fashion-MNIST results also indicate that SQU has high utility (97.9% and 79.2%, respectively), consistent with the retraining results. A comparison with a classical neural network (200×more parameters) further demonstrates that the VQC's compact parameterization enables an exact, complete sensitivity analysis for unlearning — an advantage that is infeasible at the classical scale. In general, SQU provides a computationally efficient, regulatory-compliant solution for privacy-aware QML. Code is available at https://github.com/masud1901/Sensitivity-Guided-Quantum-Machine-Unlearning.
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.
Quantum machine learning (QML) has the potential to achieve quantum advantage for specific tasks by combining quantum computation with classical machine learning (ML). In classical ML, a significant challenge is membership-privacy leakage, whereby an attacker can infer from model outputs whether specific data were used in training. When specific data are required to be withdrawn, removing their influence from the trained model becomes necessary. Machine unlearning (MU) addresses this issue by enabling the model to forget the withdrawn data, thereby preventing membership-privacy leakage. However, this leakage remains underexplored in QML. This raises two research questions: do QML models leak membership privacy about their training data, and can MU methods efficiently mitigate such leakage in QML models? We investigate these questions using two quantum neural network (QNN) architectures, a basic QNN and a hybrid QNN, evaluated in noiseless simulations and cloud quantum device demonstrations. To answer the first question, we analyze how quantum constraints shape membership-privacy leakage in QML and then formalize a realistic gray-box threat model accordingly. Based on this, we design a membership inference attack (MIA) tailored to QNN outputs, and our results provide clear evidence of membership leakage in both QNNs. To answer the second question, we propose a quantum machine unlearning (QMU) framework, comprising three MU mechanisms. Evaluations on two QNN architectures show that QMU removes the influence of the withdrawn data while preserving accuracy for retained data. A comparative analysis further characterizes the three MU mechanisms with respect to data dependence, computational cost, and robustness.
Quantum Machine Unlearning has emerged as a foundational challenge at the intersection of quantum information theory privacypreserving computation and trustworthy artificial intelligence This paper advances QMU by establishing a formal framework that unifies physical constraints algorithmic mechanisms and ethical governance within a verifiable paradigm We define forgetting as a contraction of distinguishability between pre and postunlearning models under completely positive trace-preserving dynamics grounding data removal in the physics of quantum irreversibility Building on this foundation we present a fiveaxis taxonomy spanning scope guarantees mechanisms system context and hardware realization linking theoretical constructs to implementable strategies Within this structure we incorporate influence and quantum Fisher information weighted updates parameter reinitialization and kernel alignment as practical mechanisms compatible with noisy intermediatescale quantum NISQ devices The framework extends naturally to federated and privacyaware settings via quantum differential privacy homomorphic encryption and verifiable delegation enabling scalable auditable deletion across distributed quantum systems Beyond technical design we outline a forwardlooking research roadmap emphasizing formal proofs of forgetting scalable and secure architectures postunlearning interpretability and ethically auditable governance Together these contributions elevate QMU from a conceptual notion to a rigorously defined and ethically aligned discipline bridging physical feasibility algorithmic verifiability and societal accountability in the emerging era of quantum intelligence.
The class imbalance problem presents a critical challenge in real-world applications, particularly in high-stakes domains such as healthcare, finance, disaster management, and fault diagnosis, where accurate anomaly detection is paramount. Class imbalance often disrupts the inherent symmetry of data distributions, resulting in suboptimal performance of traditional machine learning models. Conventional approaches such as undersampling and oversampling are commonly employed to address this issue; however, these methods can introduce additional asymmetries, including information loss and overfitting, which ultimately compromise model efficacy. This study introduces an innovative approach leveraging quantum machine learning (QML), specifically the Variational Quantum Classifier (VQC), to restore and capitalize on the symmetrical properties of data distributions without relying on resampling techniques. By employing quantum circuits optimized to mitigate the asymmetries inherent in imbalanced datasets, the proposed method demonstrates consistently superior performance across diverse datasets, with notable improvements in Recall for minority classes. These findings underscore the potential of quantum machine learning as a robust alternative to classical methods, offering a symmetry-aware solution to class imbalance and advancing QML-driven technologies in fields where equitable representation and symmetry are of critical importance.
This paper introduces a novel quantum machine learning framework designed for dynamic data distribution learning and pattern recognition. Traditional machine learning models often struggle with adapting to evolving data distributions, necessitating retraining or complex optimization strategies. This framework leverages quantum computing's inherent dynamic adaptability to achieve rapid learning and improved model accuracy. A key component of this design is the incorporation of quantum entanglement to enhance learning efficiency and model precision. We propose a method utilizing quantum algorithms to effectively capture and model complex data patterns, offering a potential paradigm shift in machine learning capabilities, particularly within dynamic environments. This work explores the potential benefits of this approach and presents preliminary results demonstrating its effectiveness in a specific benchmark dataset.