datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
cad-1000-hours
CAD 1000 Hours
CAD 1000 Hours is a computer-use dataset containing 1,021.64 hours of recorded work across 597 workflows and 10 CAD, BIM, structural-analysis, and visualization applications. The tables below summarize its software coverage.
Category distribution
Category
Included software
Workflows
Hours
Share of hours
Drafting and general CAD
AutoCAD
238
501.99
49.14%
Mechanical and product CAD
SOLIDWORKS, CATIA, Siemens NX
282
305.27
29.88%… See the full description on the dataset page: https://huggingface.co/datasets/QMlink001/cad-1000-hours.ibm-qml-kernel
IBM-QML-Kernel Branch-Transfer Benchmarks (ibm-qml-kernel)
Dataset Summary
This dataset is the reproducibility artifact bundle corresponding to the arXiv submission:
“Wigner's Friend as a Circuit: Inter-Branch Communication Witness Benchmarks on Superconducting Quantum Hardware.”
GitHub release checkpoint: v1.0-wigner-branch-benchmark
Paper page: https://huggingface.co/papers/2601.16004
GitHub: https://github.com/christopher-altman/ibm-qml-kernel
It snapshots the… See the full description on the dataset page: https://huggingface.co/datasets/Cohaerence/ibm-qml-kernel.CML-2-QML
CML-2-QML Generated Code Pairs
Dataset Summary
The dataset contains paired classical machine learning (ML) and quantum machine learning (QML) source files created with the seed_codebase/generate.py (https://github.com/runtsang/Q-Bridge) pipeline. Each pair extends a seed repository example by scaling the architecture, adding training utilities, or enriching the quantum circuit while keeping the classical and quantum variants aligned.
Dataset Structure in… See the full description on the dataset page: https://huggingface.co/datasets/runjiazeng/CML-2-QML.integrals
Quantum Electronic Integrals
This dataset contains quantum interaction integrals between randomly sampled pairs/quadruples of Gaussian-Type Orbitals (GTOs).The targets were computed in julia using GaussianBasis.jl.
Loading data from python
See qml/data/integrals.py.
Loading a mono-electronic integral dataset should be as simple as:
from qml.data import MonoIntegral
I_2_1 = MonoIntegral.h5read("integrals/mono_20k/mono_2_1.h5")
The MonoIntegral class inherits its h5read… See the full description on the dataset page: https://huggingface.co/datasets/qml/integrals.qm_ly_diff_cptsbuzz_sources_188_qmldeterminants
Determinants
This repository will contain N-electron wave functions computed with the CIPSI implementation of Quantum Package.
QML-Based-Network-vulnerability-Analysis-of-IIoTqm_ly_gy_soundnQML_fruit_DatasetQMLjSS9R3JR6D5eZqml-mimic-cxr-embeddings
MIMIC-CXR Embeddings Dataset
This dataset contains pre-extracted embeddings from MIMIC-CXR chest X-ray images using multiple state-of-the-art vision models. The embeddings are organized by coreset selection strategies for efficient training of quantum machine learning models.
Dataset Overview
Source: MIMIC-CXR Database
Total Seeds: 20 (seed_0 through seed_19)
Coreset Strategies: 3 per seed
Embedding Models: 5 vision transformer architectures
Total Samples: ~1,999–2,372… See the full description on the dataset page: https://huggingface.co/datasets/MITCriticalData/qml-mimic-cxr-embeddings.