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