# Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses

arXiv: https://arxiv.org/abs/2204.03625 · HTML: https://mlsanity.com/paper/2204.03625/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Quantum Generative Models for Small Molecule Drug Discovery](https://mlsanity.com/paper/2101.03438/) (2021)
- [Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)](https://mlsanity.com/paper/2109.02862/) (2021)

## Built on by (most cited first, 5 of 5)

- [Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications](https://mlsanity.com/paper/2307.00908/) (2023)
- [STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloud](https://mlsanity.com/paper/2405.18746/) (2024)
- [Security Concerns in Quantum Machine Learning as a Service](https://mlsanity.com/paper/2408.09562/) (2024)
- [Quantum Machine Learning for Material Synthesis and Hardware Security](https://mlsanity.com/paper/2208.08273/) (2022)
- [Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security](https://mlsanity.com/paper/2507.08623/) (2025)

## Abstract (arXiv)

In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data and thus can efficiently solve complex learning tasks. Despite the increased interest in QML, there have not been many studies that discuss the security aspects of QML. In this work, we explored the possible future applications of QML in the hardware security domain. We also expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures.