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
How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection
Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Shahid Munir Shah, Asher Ali, Hamzah Siddiqui
2026 - arXiv preprint
Benchmarking Quantum Machine Learning for Power-System Attack Detection: Evaluation Choices Decide the Outcome Before the Models Do
Md Rezwanul Islam
2026 - arXiv preprint
Hypothesis testing between quantum ensembles
Jian Yao, Quntao Zhuang
2026 - arXiv preprint
Predicting Resource Efficient Hamiltonian Decomposition for Continuous-Time Quantum Walk Simulations
Mostafa Atallah, Rebekah Herrman, Zain H. Saleem
2026 - arXiv preprint
Comment on "Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency": Polynomial Evaluation of the Triplet-Block Readout
Erfan Amidi
2026 - arXiv preprint
Qkabrine: A Joint Architecture, Encoding, and Hyperparameter Search Framework for Quantum Machine Learning
Eric Jagwara
2026 - arXiv preprint