# Quantum Machine Learning for Malware Classification

arXiv: https://arxiv.org/abs/2305.09674 · HTML: https://mlsanity.com/paper/2305.09674/

## 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 (most cited first, 15 of 15)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Generalization in quantum machine learning from few training data](https://mlsanity.com/paper/2111.05292/) (2021)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Absence of Barren Plateaus in Quantum Convolutional Neural Networks](https://mlsanity.com/paper/2011.02966/) (2020)
- [Theory of overparametrization in quantum neural networks](https://mlsanity.com/paper/2109.11676/) (2021)
- [Exploiting symmetry in variational quantum machine learning](https://mlsanity.com/paper/2205.06217/) (2022)
- [Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning](https://mlsanity.com/paper/1801.08917/) (2018)
- [Representation Theory for Geometric Quantum Machine Learning](https://mlsanity.com/paper/2210.07980/) (2022)
- [Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning](https://mlsanity.com/paper/2203.15368/) (2022)
- [Effective dimension of machine learning models](https://mlsanity.com/paper/2112.04807/) (2021)
- [MERLIN -- Malware Evasion with Reinforcement LearnINg](https://mlsanity.com/paper/2203.12980/) (2022)
- [Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability](https://mlsanity.com/paper/2107.11100/) (2021)
- [AI-based Malware and Ransomware Detection Models](https://mlsanity.com/paper/2207.02108/) (2022)
- [Resource frugal optimizer for quantum machine learning](https://mlsanity.com/paper/2211.04965/) (2022)

## Built on by (most cited first, 3 of 3)

- [Towards an in-depth detection of malware using distributed QCNN](https://mlsanity.com/paper/2312.12161/) (2023)
- [Quantum Computing Methods for Malware Detection](https://mlsanity.com/paper/2510.06803/) (2025)
- [Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions](https://mlsanity.com/paper/2512.15286/) (2025)

## 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.