[2512.19253] Machine Unlearning in the Era of Quantum Machine Learning: An Empirical Study

Computer Science > Machine Learning arXiv:2512.19253 (cs) [Submitted on 22 Dec 2025 (v1), last revised 8 Apr 2026 (this version, v3)] Title:Machine Unlearning in the Era of Quantum Machine Learning: An Empirical Study Authors:Carla Crivoi, Radu Tudor Ionescu View a PDF of the paper titled Machine Unlearning in the Era of Quantum Machine Learning: An Empirical Study, by Carla Crivoi and 1 other authors View PDF HTML (experimental) 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 Certif...