# Q-Detection: A Quantum-Classical Hybrid Poisoning Attack Detection Method

arXiv: https://arxiv.org/abs/2507.06262 · HTML: https://mlsanity.com/paper/2507.06262/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 44 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [Anti-Backdoor Learning: Training Clean Models on Poisoned Data](https://mlsanity.com/paper/2110.11571/) (2021)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [Adversarial Unlearning of Backdoors via Implicit Hypergradient](https://mlsanity.com/paper/2110.03735/) (2021)
- [Adversarial Machine Learning -- Industry Perspectives](https://mlsanity.com/paper/2002.05646/) (2020)
- [Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks](https://mlsanity.com/paper/2006.12557/) (2020)
- [Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information](https://mlsanity.com/paper/2204.05255/) (2022)
- [Weakly Supervised Energy-Based Learning for Action Segmentation](https://mlsanity.com/paper/1909.13155/) (2019)
- [Energy-based learning algorithms for analog computing: a comparative study](https://mlsanity.com/paper/2312.15103/) (2023)
- [Benchmarking Adversarially Robust Quantum Machine Learning at Scale](https://mlsanity.com/paper/2211.12681/) (2022)
- [Training an Ising Machine with Equilibrium Propagation](https://mlsanity.com/paper/2305.18321/) (2023)
- [Label Poisoning is All You Need](https://mlsanity.com/paper/2310.18933/) (2023)
- [Towards quantum enhanced adversarial robustness in machine learning](https://mlsanity.com/paper/2306.12688/) (2023)
- [Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers](https://mlsanity.com/paper/2502.01146/) (2025)
- [QTrojan: A Circuit Backdoor Against Quantum Neural Networks](https://mlsanity.com/paper/2302.08090/) (2023)
- [MG-Net: Learn to Customize QAOA with Circuit Depth Awareness](https://mlsanity.com/paper/2409.18692/) (2024)
- [Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era](https://mlsanity.com/paper/2411.14412/) (2024)
- [Probabilistic Quantum SVM Training on Ising Machine](https://mlsanity.com/paper/2503.16363/) (2025)

## 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)
- [FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models](https://mlsanity.com/paper/2604.15488/) (2026)
- [Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods](https://mlsanity.com/paper/2605.18821/) (2026)

## Abstract (arXiv)

Data poisoning attacks pose significant threats to machine learning models by introducing malicious data into the training process, thereby degrading model performance or manipulating predictions. Detecting and sifting out poisoned data is an important method to prevent data poisoning attacks. Limited by classical computation frameworks, upcoming larger-scale and more complex datasets may pose difficulties for detection. We introduce the unique speedup of quantum computing for the first time in the task of detecting data poisoning. We present Q-Detection, a quantum-classical hybrid defense method for detecting poisoning attacks. Q-Detection also introduces the Q-WAN, which is optimized using quantum computing devices. Experimental results using multiple quantum simulation libraries show that Q-Detection effectively defends against label manipulation and backdoor attacks. The metrics demonstrate that Q-Detection consistently outperforms the baseline methods and is comparable to the state-of-the-art. Theoretical analysis shows that Q-Detection is expected to achieve more than a 20% speedup using quantum computing power.