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
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: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:2607. 05307v1 Announce Type: cross Abstract: A core task in quantum anomaly detection is to compute an anomaly score that quantifies how strongly a test quantum state deviates from a given quantum dataset assumed to be normal.
By Yewei Yuan, Michele Minervini, Mark M. Wilde, Nana 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:2605.25066v2 Announce Type: replace-cross
Abstract: As machine-learning workloads move onto quantum hardware they inherit a trust problem classical AI deployments have largely solved and quantu...
By Esra Yeniaras
arXiv:2607. 19782v1 Announce Type: cross Abstract: Kernel methods are powerful tools in machine learning but commonly used full-Gram kernels face three key limitations: (1) quadratic scaling with training set size; (2) the use of fixed, non-trainable kernels; and (3) the absence of an intrinsic formulation for multiclass classification.
By Kilian Tscharke, Pascal Debus
arXiv:2503. 17020v2 Announce Type: replace-cross Abstract: Kernel methods compare inputs through feature maps.
By Joachim Tomasi, Sandrine Anthoine, Hachem Kadri
arXiv:2607. 01080v1 Announce Type: new Abstract: We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert space (RKHS) induced by the quantum kernel.
By Yuqi Huang, Vincent Y. F. Tan, Sharu Theresa Jose
arXiv:2609.23476v1 Announce Type: cross
Abstract: A potential path forward is Quantum Machine Learning (QML), which aims to leverage quantum computing in conjunction with classical machine learning t...
By Anand Kumar Mishra, Ramanuj Awasthi
arXiv:2511.10831v3 Announce Type: replace
Abstract: Quantum kernel methods are a promising branch of quantum machine learning, but their per- formance across data types and encoding choices remains i...
By Jiang Yuhan, Matthew Otten
The paper investigates how removing an input‑scaling module in a four‑qubit quantum‑attention detector affects both the classifier and the perturbations that reach its encoder. It shows that robustness differences can arise from the comparison rule rather than the module itself, and that altering input scales disrupts detection unless the linear classification layer is retrained. The study highlights the need to clearly define perturbation budgets, ensure attacks preserve labels, and differentiate exploratory from confirmatory tests.
By Owen Friedewald, Srikar Alla, Ali Shiri Sichani, Chi-Ren Shyu