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---
license: mit
title: "Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning"
---
## Overview
This repository provides a collection of embedding datasets for evaluating quantum-classical support vector machines (QSVMs) using embeddings from pre-trained classical models. Each dataset follows the naming convention:
```
<model_name>_<embedding_dim>.csv
```
Where:
- `model_name`: the architecture used to generate the embeddings (e.g., `vit_b_16`, `efficientnet`, `vit_l_14@336px`)
- `embedding_dim`: the dimensionality of the embedding vectors
- The **last column** in each CSV represents the **class label**
Each CSV file is structured as a table:
- Rows correspond to distilled training or testing samples
- Columns represent embedding values followed by the class label
These datasets are designed to support quantum kernel methods and hybrid pipelines, as implemented in [QuantumVE](https://github.com/sebasmos/QuantumVE).
This dataset collection includes subsets derived from public datasets:
- **MNIST** – in the public domain and freely available [here](http://yann.lecun.com/exdb/mnist/)
- **Fashion-MNIST** – distributed under the [MIT License](https://github.com/zalandoresearch/fashion-mnist)
We confirm that only subsets of these datasets are included (images and embeddings), and they are redistributed in accordance with their respective licenses. This repository is intended for non-commercial academic use.
---
## Repository Structure
Datasets are stored in `.csv` format and organized into folders named according to the embedding configuration:
```
<model_name>_<embedding_dim>/
├── train.csv # Training set with embeddings and labels
└── test.csv # Testing set with embeddings and labels
```
### Example Folders:
- `efficientnet_1536/`
- `vit_b_16_512/`
- `vit_l_14@336px_768/`
---
## Loading Datasets
Example code to load and preview a dataset:
```python
import pandas as pd
df = pd.read_csv("vit_b_16_512/train.csv")
X = df.iloc[:, :-1].values # Embedding features
y = df.iloc[:, -1].values # Class labels
```
---
## Citation
If you use this dataset collection, please cite:
```bibtex
@misc{cajas_quantumve_2025,
author = {Sebastián Andrés Cajas Ordóñez, Luis Torres, Mario Bifulco, Carlos Duran, Cristian Bosch, Ricardo Simon Carbajo},
title = {Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning},
year = {2025},
url = {https://github.com/sebasmos/QuantumVE},
note = {GitHub repository},
version = {v1.0},
howpublished = {\url{https://github.com/sebasmos/QuantumVE}},
```
---
## License
QuantumVE is free and open source, released under the MIT License.
---
## Contact & Contributions
This dataset collection is maintained as part of the QuantumVE project.
For questions or contributions, feel free to reach out or submit a pull request via the [GitHub repository](https://github.com/sebasmos/QuantumVE).
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