---
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).