# Predominant Aspects on Security for Quantum Machine Learning: Literature Review

arXiv: https://arxiv.org/abs/2401.07774 · HTML: https://mlsanity.com/paper/2401.07774/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2024.

## Built on by (most cited first, 9 of 9)

- [Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learning](https://mlsanity.com/paper/2404.16417/) (2024)
- [A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks](https://mlsanity.com/paper/2504.12806/) (2025)
- [Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis](https://mlsanity.com/paper/2407.18021/) (2024)
- [Experimental robustness benchmarking of quantum neural networks on a superconducting quantum processor](https://mlsanity.com/paper/2505.16714/) (2025)
- [Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security](https://mlsanity.com/paper/2507.08623/) (2025)
- [Quantum Machine Learning: Performance and Security Implications in Real-World Applications](https://mlsanity.com/paper/2408.04543/) (2024)
- [Computable Model-Independent Bounds for Adversarial Quantum Machine Learning](https://mlsanity.com/paper/2411.06863/) (2024)
- [Detecting underdetermination in parameterized quantum circuits](https://mlsanity.com/paper/2504.03315/) (2025)
- [Is data-efficient learning feasible with quantum models?](https://mlsanity.com/paper/2508.19437/) (2025)

## Abstract (arXiv)

Quantum Machine Learning (QML) has emerged as a promising intersection of quantum computing and classical machine learning, anticipated to drive breakthroughs in computational tasks. This paper discusses the question which security concerns and strengths are connected to QML by means of a systematic literature review. We categorize and review the security of QML models, their vulnerabilities inherent to quantum architectures, and the mitigation strategies proposed. The survey reveals that while QML possesses unique strengths, it also introduces novel attack vectors not seen in classical systems. We point out specific risks, such as cross-talk in superconducting systems and forced repeated shuttle operations in ion-trap systems, which threaten QML's reliability. However, approaches like adversarial training, quantum noise exploitation, and quantum differential privacy have shown potential in enhancing QML robustness. Our review discuss the need for continued and rigorous research to ensure the secure deployment of QML in real-world applications. This work serves as a foundational reference for researchers and practitioners aiming to navigate the security aspects of QML.