# Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions

arXiv: https://arxiv.org/abs/2512.15286 · HTML: https://mlsanity.com/paper/2512.15286/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 72 references that are in the map.
- First posted on arXiv 17 Dec 2025.

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

- [Investigating and Mitigating Barren Plateaus in Variational Quantum Circuits: A Survey](https://mlsanity.com/paper/2407.17706/) (2024)
- [Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering](https://mlsanity.com/paper/2404.09243/) (2024)
- [Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey](https://mlsanity.com/paper/2409.07626/) (2024)
- [Quantum Generative Adversarial Networks: Bridging Classical and Quantum Realms](https://mlsanity.com/paper/2312.09939/) (2023)
- [Quantum Clustering for Cybersecurity](https://mlsanity.com/paper/2408.02314/) (2024)
- [QML-IDS: Quantum Machine Learning Intrusion Detection System](https://mlsanity.com/paper/2410.16308/) (2024)
- [Machine Learning Applications of Quantum Computing: A Review](https://mlsanity.com/paper/2406.13262/) (2024)
- [Quantum Machine Learning for Malware Classification](https://mlsanity.com/paper/2305.09674/) (2023)
- [An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management](https://mlsanity.com/paper/2410.03217/) (2024)
- [Quantum Computing Methods for Malware Detection](https://mlsanity.com/paper/2510.06803/) (2025)
- [Quantum Artificial Intelligence for Mission-Critical Systems: Foundations, Architectural Elements, and Future Directions](https://mlsanity.com/paper/2511.09884/) (2025)

## Abstract (arXiv)

The increasing number of cyber threats and rapidly evolving tactics, as well as the high volume of data in recent years, have caused classical machine learning, rules, and signature-based defence strategies to fail, rendering them unable to keep up. An alternative, Quantum Machine Learning (QML), has recently emerged, making use of computations based on quantum mechanics. It offers better encoding and processing of high-dimensional structures for certain problems. This survey provides a comprehensive overview of QML techniques relevant to the domain of security, such as Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Variational Quantum Circuits (VQCs), and Quantum Generative Adversarial Networks (QGANs), and discusses the contributions of this paper in relation to existing research in the field and how it improves over them. It also maps these methods across supervised, unsupervised, and generative learning paradigms, and to core cybersecurity tasks, including intrusion and anomaly detection, malware and botnet classification, and encrypted-traffic analytics. It also discusses their application in the domain of cloud computing security, where QML can enhance secure and scalable operations. Many limitations of QML in the domain of cybersecurity have also been discussed, along with the directions for addressing them.