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.

翻译：我们提出了首个关于混合量子-经典神经网络中机器去学习(MU)的实证研究。尽管机器去学习已在经典深度学习中得到了广泛探索，但其在变分量子电路(VQC)和量子增强架构中的行为仍很大程度上未被探索。首先，我们将一系列广泛的去学习方法适配至量子场景，包括基于梯度、基于蒸馏、基于正则化和经过认证的技术。其次，我们引入了两种针对混合模型定制的新去学习策略。在鸢尾花(Iris)、MNIST和Fashion-MNIST数据集上，针对子集移除和全类删除两种场景的实验表明，量子模型能够支持有效的去学习，但效果强烈依赖于电路深度、纠缠结构和任务复杂度。浅层VQC展现出高内在稳定性与极低记忆化，而深层混合模型则在效用、遗忘强度以及与重训练基准的对齐之间表现出更强的权衡。我们发现，某些方法（例如EU-k、LCA和认证去学习）在所有指标上始终能提供最佳平衡。这些发现为量子机器去学习建立了基线实证见解，并凸显了随着量子机器学习系统在规模和能力上的不断扩展，对量子感知算法和理论保证的需求。我们已在以下网址公开发布代码：https://github.com/CrivoiCarla/HQML。