# Quantum Adversarial Learning for Kernel Methods

arXiv: https://arxiv.org/abs/2404.05824 · HTML: https://mlsanity.com/paper/2404.05824/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [A rigorous and robust quantum speed-up in supervised machine learning](https://mlsanity.com/paper/2010.02174/) (2020)
- [Generalization in quantum machine learning from few training data](https://mlsanity.com/paper/2111.05292/) (2021)
- [Quantum Adversarial Machine Learning](https://mlsanity.com/paper/2001.00030/) (2019)
- [Experimental quantum adversarial learning with programmable superconducting qubits](https://mlsanity.com/paper/2204.01738/) (2022)
- [What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?](https://mlsanity.com/paper/2210.05577/) (2022)
- [Statistical Complexity of Quantum Learning](https://mlsanity.com/paper/2309.11617/) (2023)
- [Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis](https://mlsanity.com/paper/2402.00176/) (2024)

## Built on by (most cited first, 6 of 6)

- [Learning to Classify Quantum Phases of Matter with a Few Measurements](https://mlsanity.com/paper/2409.05188/) (2024)
- [Adversarial Robustness of Partitioned Quantum Classifiers](https://mlsanity.com/paper/2502.20403/) (2025)
- [Differentially Private Federated Quantum Learning via Quantum Noise](https://mlsanity.com/paper/2508.20310/) (2025)
- [Certifiably Robust Encoding Schemes](https://mlsanity.com/paper/2408.01200/) (2024)
- [On the Generalization of Adversarially Trained Quantum Classifiers](https://mlsanity.com/paper/2504.17690/) (2025)
- [Benchmarking Quantum Machine Learning for Power-System Attack Detection: Evaluation Choices Decide the Outcome Before the Models Do](https://mlsanity.com/paper/2608.15617/) (2026)

## Abstract (arXiv)

We show that hybrid quantum classifiers based on quantum kernel methods and support vector machines are vulnerable against adversarial attacks, namely small engineered perturbations of the input data can deceive the classifier into predicting the wrong result. Nonetheless, we also show that simple defence strategies based on data augmentation with a few crafted perturbations can make the classifier robust against new attacks. Our results find applications in security-critical learning problems and in mitigating the effect of some forms of quantum noise, since the attacker can also be understood as part of the surrounding environment.