← Back to all news
arXiv AI

Quantum Computing for Network Security Classification: Near-Term Classification and Long-Term Memory Efficiency

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.

Related stories

arXiv AI
Aug 20

How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection

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
benchmarks
More like this →
arXiv Machine Learning
1d ago

On Evaluating Quantum Kernel Robustness for Low-Resource Cross-Corpus Audio Deepfake Detection

The paper investigates quantum kernel methods for detecting audio deepfakes when training data are scarce and evaluation corpora differ from training corpora. Using only 200 labeled samples and four‑dimensional embeddings, the authors compare a Quantum Support Vector Machine (QSVM), a classical SVM, and a multilayer perceptron (MLP) across several cross‑corpus datasets. While the QSVM outperforms the MLP under severe domain shift from ASVspoof 2019 to ADD 2023, its advantage is inconsistent in other transfer directions, indicating that quantum kernels can be competitive but not universally superior in low‑resource, cross‑corpus settings.

By Lisan Al Amin, Lei Zhang, Vandana P. Janeja
ragsafety
More like this →
arXiv Machine Learning
Jul 13

Is data-efficient learning feasible with quantum models?

arXiv:2508. 19437v2 Announce Type: replace-cross Abstract: The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive.

By Alona Sakhnenko, Christian B. Mendl, Jeanette M. Lorenz
benchmarkssafety
More like this →
arXiv Machine Learning
1d ago

Neural Fourier Surrogates for Data Reuploading Quantum Neural Networks

The paper introduces Neural Fourier Surrogates (NFS), a stochastic classical neural network that learns coefficients over a finite Fourier series support similar to quantum neural networks (QNNs). By testing on tabular benchmark datasets, NFS demonstrates competitive classification performance against established classical baselines and data‑reuploading QNNs. The authors also compare the learned Fourier spectra of QNNs and NFS on synthetic data, positioning NFS as a natural classical baseline for evaluating QNN performance.

By Oliver Knitter, Jonathan Mei, Sang Hyub Kim, Chi Chen, Masako Yamada, Martin Roetteler
benchmarks
More like this →
arXiv Machine Learning
Jul 27

Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification

arXiv:2604. 26675v2 Announce Type: replace-cross Abstract: We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum circuit defines a nonlinear data embedding while the readout determines how this representation is exploited.

By Ralntion Komini, Aikaterini Mandilara, Georgios Maragkopoulos, Dimitris Syvridis
rag
More like this →