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arXiv 2601.04413cs.LGquant-ph
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基于分布和约束的量子机器学习去学习

Distribution-Guided and Constrained Quantum Machine Unlearning

Department of Electrical Engineering, Lahore University of Management Sciences, Lahore 54792, Pakistan.（电气工程系，拉合尔管理科学大学，巴基斯坦拉合尔）；Department of Physics, Lahore University of Management Sciences, Lahore 54792, Pakistan.（物理系，拉合尔管理科学大学，巴基斯坦拉合尔）

Nausherwan Malik, Zubair Khalid, Muhammad Faryad

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AI总结：

本文提出了一种基于分布和约束的量子机器学习去学习框架，通过可调节的目标分布和保留约束，有效控制遗忘与保留行为之间的权衡，提升去学习的可靠性与可解释性。

摘要：

机器去学习旨在在不完全重新训练的情况下从已学习模型中移除特定训练数据的影响。尽管最近的研究开始探索量子机器学习中的去学习，但现有方法大多依赖于固定的、均匀的目标分布，并未明确控制遗忘与保留模型行为之间的权衡。在本工作中，我们提出了一种面向类别的量子机器学习去学习框架，将去学习视为一个约束优化问题。我们的方法引入了一个可调节的目标分布，该分布来源于模型相似性统计，将被遗忘类别的置信度抑制与对保留类别再分布的假设解耦。我们进一步结合基于锚点的保留约束，明确保持在选定保留数据上的预测行为，从而产生一个受控的优化轨迹，限制与原模型的偏差。我们在训练于Iris和Covertype数据集上的变分量子分类器上评估了该方法。结果表明，对被遗忘类别的置信度有明显的抑制作用，保留类别的性能降解最小，并且与重新训练的模型基线相比更接近。这些发现突显了目标设计和基于约束的公式化对于可靠和可解释的量子机器学习去学习的重要性。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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