Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data

quant-ph, cs.CR, cs.LG

Submitted: 2026-10-07

Updated: 2026-10-07

Comments: 13 pages, 5 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• Equivalence between exponential concentration in quantum machine learning kernels and barren plateaus in variational algorithms
• Quantum Machine Learning for Malware Classification
• Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines
• Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel
• Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors
• Supervised quantum machine learning models are kernel methods
• Covariant quantum kernels for data with group structure
• Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance

Related papers

• Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more
• Encrypted clones can leak: Classification of informative subsets in Quantum Encrypted Cloning
• Polynomial-time classical and quantum simulation of quantum impurity models
• A quasiparticle-protected superconducting qubit
• Theory of quantum-enhanced interferometry with general Markovian light sources
• A convergent hierarchy of spectral gap certificates for qubit Hamiltonians