# Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2502.11173 · HTML: https://mlsanity.com/paper/2502.11173/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 83 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [Quantum Recommendation Systems](https://mlsanity.com/paper/1603.08675/) (2016)
- [q-means: A quantum algorithm for unsupervised machine learning](https://mlsanity.com/paper/1812.03584/) (2018)
- [Quantum support vector machine for big data classification](https://mlsanity.com/paper/1307.0471/) (2013)
- [QUBO Formulations for Training Machine Learning Models](https://mlsanity.com/paper/2008.02369/) (2020)
- [Survey of Machine Learning Techniques for Malware Analysis](https://mlsanity.com/paper/1710.08189/) (2017)
- [Learn&Fuzz: Machine Learning for Input Fuzzing](https://mlsanity.com/paper/1701.07232/) (2017)
- [Graph-theoretic Simplification of Quantum Circuits with the ZX-calculus](https://mlsanity.com/paper/1902.03178/) (2019)
- [A Quantum Interior Point Method for LPs and SDPs](https://mlsanity.com/paper/1808.09266/) (2018)
- [Quantum Expectation-Maximization for Gaussian Mixture Models](https://mlsanity.com/paper/1908.06657/) (2019)
- [Quantum Regularized Least Squares](https://mlsanity.com/paper/2206.13143/) (2022)
- [Quantum tomography using state-preparation unitaries](https://mlsanity.com/paper/2207.08800/) (2022)
- [Quantum Perceptron Revisited: Computational-Statistical Tradeoffs](https://mlsanity.com/paper/2106.02496/) (2021)
- [Quantum algorithms for SVD-based data representation and analysis](https://mlsanity.com/paper/2104.08987/) (2021)
- [Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?](https://mlsanity.com/paper/2204.02784/) (2022)
- [Quantum Machine Learning for Material Synthesis and Hardware Security](https://mlsanity.com/paper/2208.08273/) (2022)

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

- [SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness](https://mlsanity.com/paper/2511.14989/) (2025)
- [How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection](https://mlsanity.com/paper/2608.18155/) (2026)
- [Candidate Comparability Before Promotion: Conditional Validation in Adaptive Network Intrusion Detection](https://mlsanity.com/paper/2609.04388/) (2026)

## Abstract (arXiv)

Quantum computing promises to revolutionize our understanding of the limits of computation, and its implications in cryptography have long been evident. Today, cryptographers are actively devising post-quantum solutions to counter the threats posed by quantum-enabled adversaries. Meanwhile, quantum scientists are innovating quantum protocols to empower defenders. However, the broader impact of quantum computing and quantum machine learning (QML) on other cybersecurity domains still needs to be explored. In this work, we investigate the potential impact of QML on cybersecurity applications of traditional ML. First, we explore the potential advantages of quantum computing in machine learning problems specifically related to cybersecurity. Then, we describe a methodology to quantify the future impact of fault-tolerant QML algorithms on real-world problems. As a case study, we apply our approach to standard methods and datasets in network intrusion detection, one of the most studied applications of machine learning in cybersecurity. Our results provide insight into the conditions for obtaining a quantum advantage and the need for future quantum hardware and software advancements.