The study evaluates quantum machine learning (QML) models for network intrusion detection against well-tuned classical baselines across four standard datasets, using a leakage-controlled protocol and noise simulation. It introduces a quantum-attribution audit to determine whether any performance gains are truly due to quantum effects. While most tuned classical models match or surpass QML, two quantum approaches— a quantum-kernel SVM and a small hybrid circuit—show statistically significant advantages on specific metrics and tasks.
By Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Shahid Munir Shah, Asher Ali, Hamzah Siddiqui
arXiv:2608. 15617v1 Announce Type: new Abstract: Machine-learning detectors for power-system cyberattacks are themselves attack surfaces, and quantum machine learning has been proposed for them.
By Md Rezwanul Islam
arXiv:2609.05702v2 Announce Type: cross
Abstract: Quantum Machine Learning (QML) has shown rapid advances by utilizing quantum computing for machine learning tasks. Meanwhile, the privacy risks accom...
By Liou Tang, James Joshi, Ashish Kundu
arXiv:2607. 11843v1 Announce Type: cross Abstract: Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood.
By Junrui Zhang, Zemin Chen, Lusi Li, Mohammad Ghasemigol, Daniel Takabi, Rui Ning
Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood. Studies have shown that QNNs are vulnerable to backdoor attacks, yet existing quantum backdoors mostly rely on a fixed trigger shared by all poisoned inputs.
The paper investigates the role of quantum computing in network‑security classification through two experiments. First, it evaluates near‑term quantum‑kernel support vector machines on datasets such as KDD Cup 1999, CICIDS2017, and BoT‑IoT, finding that quantum kernels can match or sometimes improve classical baselines, though classical RBF kernels often remain stronger. Second, it explores long‑term memory efficiency using quantum oracle sketching (QOS), showing that quantum methods can achieve comparable accuracy with a smaller effective memory footprint than explicit storage, suggesting a potential advantage in memory‑efficient data access for streaming classification tasks.
By Yuqing Li, Poonam Bala Nehru, Yunpeng Zhang, Danindu Gammanpilage, Xin Jin, Zeguan Wu, Junyu Liu
arXiv:2610.17027v1 Announce Type: new
Abstract: Detecting credit card fraud is notoriously difficult because fraudulent charges make up less than 0.2% of total transactions. Quantum Machine Learning...
By Hitanshi Arora, Mohit Swarnkar, Aswani Kumar Cherukuri
arXiv:2607. 11095v1 Announce Type: cross Abstract: Adversarial perturbations threaten machine learning classifiers, including variational quantum classifiers.
By Bacui Li, Chandra Thapa, Tansu Alpcan, Udaya Parampalli
arXiv:2610.09717v1 Announce Type: cross
Abstract: Quantum kernel methods are leading candidates for a practical quantum advantage in machine learning, but assessing that potential requires two quanti...
By Boaz Micah, Nadia Milazzo, Maissa Beji, Borja Aizpurua, Lloren\c{c} Espinosa-Portal\'es, Esteban Payares, Ghada Ben Slama, Luc Andrea, Michel Kurek, Thomas Cope, Olivier Salomon
arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.
By Chi-Sheng Chen, En-Jui Kuo
The paper studies how an eavesdropper can adaptively attack quantum key distribution (QKD) systems when channel noise and device drift vary over time. By modeling the attack as a constrained Markov decision process and using reinforcement learning to jointly search gate structures and rotation angles, the authors construct compact attack circuits that perform near the theoretical upper bound for both device‑independent E91 and BB84 protocols under realistic noise models. The results show that adaptive attacks can significantly increase the eavesdropper’s information compared to fixed‑circuit strategies, and that the learned attacks recover known optimal cloners and key‑rate bounds.
By Marcel Mordarski, Benjamin Gras, Abdelrahman Shehata, Daniel Budina, Roberto Bondesan
arXiv:2607. 05281v1 Announce Type: cross Abstract: Present-day quantum computing is cloud-based, where a user submits a circuit to a service provider's proprietary backend hardware.
By Ben Priestley, Mina Doosti