Quantum Articles Topic Guide
Quantum Machine Learning Papers
Browse curated quantum machine learning papers covering QML models, quantum kernels, variational classifiers, learning theory, and benchmarks.
Research Focus
Quantum machine learning research studies how quantum systems, kernels, circuits, and hybrid algorithms can support learning tasks. This page collects QML papers and related quantum AI research so readers can quickly inspect models, benchmarks, and practical limitations.
The collection is useful for following work on quantum kernels, data reuploading circuits, variational classifiers, quantum generative models, and resource-aware benchmarking.
Use the category links to move from this keyword page into the full Quantum Articles archive and adjacent algorithm or optimization research.
Latest Papers
Verifiable Sampling for Delegated Generative Quantum Machine Learning
Matteo Inajetovic, Anna Pappa, Petros Wallden
2026 - arXiv preprint
Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting
Jack Waller, Xing Liang, Dimitrios Makris, Rajagopal Nilavalan
2026 - arXiv preprint
On linearity or non-linearity in machine learning for quantum chaotic dynamics
Francesco Perciavalle, Agostino Gallo, Francesco Plastina, Gianluigi Greco, Nicola Lo Gullo, Carlo Adornetto
2026 - arXiv preprint
Quantum Machine Learning for Few-Shot Impersonation Detection in Digital Account Opening
Javiera Canales, Jose Ortega, Maximiliano Araya
2026 - arXiv preprint
Quantum-Assisted Optimization Guided by Machine-Learned Risk Maps for Aerial Surveillance Routing
Dorian Lauwerier, Alexis Vieloszynski, Yves B\'erub\'e-Lauzi\`ere, Victor Drouin-Touchette
2026 - arXiv preprint
Advantage of Sample Complexity in Quantum PAC Learning Requires Inverse Access to State-Preparation Unitaries
Natsuto Isogai, Satoshi Yoshida, Mio Murao
2026 - arXiv preprint