mlsanityData to 14 Oct 2026

Quantum Adversarial Learning for Kernel Methods

arXiv 2404.05824 ↗First posted 8 Apr 2024quant-ph cs.CR cs.LGcited by 6

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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.

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

11 of 38 references.

References · 11

  1. 2013Intriguing properties of neural networkscited by 5,155
  2. 2018Supervised learning with quantum enhanced feature spacescited by 665
  3. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 414
  4. 2020Power of data in quantum machine learningcited by 338
  5. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 271
  6. 2021Generalization in quantum machine learning from few training datacited by 216
  7. 2019Quantum Adversarial Machine Learningcited by 67
  8. 2022Experimental quantum adversarial learning with programmable superconducting qubitscited by 39
  9. 2022What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?cited by 21
  10. 2023Statistical Complexity of Quantum Learningcited by 11
  11. 2024Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysiscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2024Learning to Classify Quantum Phases of Matter with a Few Measurementscited by 2
  2. 2025Adversarial Robustness of Partitioned Quantum Classifierscited by 2
  3. 2025Differentially Private Federated Quantum Learning via Quantum Noisecited by 2
  4. 2024Certifiably Robust Encoding Schemescited by 1
  5. 2025On the Generalization of Adversarially Trained Quantum Classifierscited by 1
  6. 2026Benchmarking Quantum Machine Learning for Power-System Attack Detection: Evaluation Choices Decide the Outcome Before the Models Docited by 0