# Distribution-Guided and Constrained Quantum Machine Unlearning

arXiv: https://arxiv.org/abs/2601.04413 · HTML: https://mlsanity.com/paper/2601.04413/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 6 of 6)

- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Variational Bayesian Unlearning](https://mlsanity.com/paper/2010.12883/) (2020)
- [nabla τ: Gradient-based and Task-Agnostic machine Unlearning](https://mlsanity.com/paper/2403.14339/) (2024)
- [Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomy](https://mlsanity.com/paper/2511.00406/) (2025)
- [A More Practical Approach to Machine Unlearning](https://mlsanity.com/paper/2406.09391/) (2024)
- [Machine Unlearning in the Era of Quantum Machine Learning: An Empirical Study](https://mlsanity.com/paper/2512.19253/) (2025)

## Abstract (arXiv)

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.