mlsanityData to 14 Oct 2026

Quantum Machine Learning for Malware Classification

arXiv 2305.09674 ↗First posted 9 May 2023cs.CR cs.ET cs.LGcited by 3

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Abstract (arXiv)

In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malware that has never been seen. We investigate the possible benefits of quantum algorithms for classification tasks. We implement two models of Quantum Machine Learning algorithms, and we compare them to classical models for the classification of a dataset composed of malicious and benign executable files. We try to optimize our algorithms based on methods found in the literature, and analyze our results in an exploratory way, to identify the most interesting directions to explore for the future.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 9 May 2023.

Builds on

15 of 29 references.

References · 15

  1. 2018Supervised learning with quantum enhanced feature spacescited by 665
  2. 2021Generalization in quantum machine learning from few training datacited by 216
  3. 2023Challenges and Opportunities in Quantum Machine Learningcited by 203
  4. 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 165
  5. 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 125
  6. 2021Theory of overparametrization in quantum neural networkscited by 100
  7. 2022Exploiting symmetry in variational quantum machine learningcited by 69
  8. 2018Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learningcited by 61
  9. 2022Representation Theory for Geometric Quantum Machine Learningcited by 40
  10. 2022Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learningcited by 16
  11. 2021Effective dimension of machine learning modelscited by 14
  12. 2022MERLIN -- Malware Evasion with Reinforcement LearnINgcited by 11
3 more
  1. 2021Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretabilitycited by 9
  2. 2022AI-based Malware and Ransomware Detection Modelscited by 7
  3. 2022Resource frugal optimizer for quantum machine learningcited by 3

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.

  1. 2023Towards an in-depth detection of malware using distributed QCNNcited by 1
  2. 2025Quantum Computing Methods for Malware Detectioncited by 1
  3. 2025Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directionscited by 0