# Robust Decentralized Quantum Kernel Learning for Noisy and Adversarial Environment

arXiv: https://arxiv.org/abs/2504.13782 · HTML: https://mlsanity.com/paper/2504.13782/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2025.

## Built on by (most cited first, 6 of 6)

- [Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting](https://mlsanity.com/paper/2505.09395/) (2025)
- [Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing](https://mlsanity.com/paper/2505.08474/) (2025)
- [Adversarial Robustness in Distributed Quantum Machine Learning](https://mlsanity.com/paper/2508.11848/) (2025)
- [Quantum Machine Learning for UAV Swarm Intrusion Detection](https://mlsanity.com/paper/2509.01812/) (2025)
- [Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series](https://mlsanity.com/paper/2511.02301/) (2025)
- [Special-Unitary Parameterization for Trainable Variational Quantum Circuits](https://mlsanity.com/paper/2507.05535/) (2025)

## Abstract (arXiv)

This paper proposes a general decentralized framework for quantum kernel learning (QKL). It has robustness against quantum noise and can also be designed to defend adversarial information attacks forming a robust approach named RDQKL. We analyze the impact of noise on QKL and study the robustness of decentralized QKL to the noise. By integrating robust decentralized optimization techniques, our method is able to mitigate the impact of malicious data injections across multiple nodes. Experimental results demonstrate that our approach maintains high accuracy under noisy quantum operations and effectively counter adversarial modifications, offering a promising pathway towards the future practical, scalable and secure quantum machine learning (QML).