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----
-license: mit
-title: "Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning"
----
-
-## Overview
-
-This repository contains a collection of embedding datasets for evaluating quantum-classical support vector machines (QSVMs) using pre-trained classical models.  
-Each dataset is named using the 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`, etc.)
-- `embedding_dim`: the dimensionality of the embedding vectors.
-- The **last column** in each CSV corresponds to the **class label**.
-
-Each file includes a tabular structure where:
-- Rows represent distilled training or testing samples.
-- Columns represent the embedding dimensions followed by a label column.
-
-These datasets are compatible with quantum kernel methods and hybrid learning pipelines as implemented in [QuantumVE](https://github.com/sebasmos/QuantumVE).
-
----
-
-## Repository Structure
-
-All datasets are stored in `.csv` format and follow the naming scheme:
-
-<model_name>_<embedding_dim>/
-├── train.csv   # Training embeddings + labels
-└── test.csv    # Testing embeddings + labels
-
-Example folders:
-- `efficientnet_1536/`
-- `vit_b_16_512/`
-- `vit_l_14@336px_768/`
-
----
-
-## Loading Datasets
-
-You can load and preview a dataset using Python:
-
-```python
-import pandas as pd
-
-df = pd.read_csv("vit_b_16_512/train.csv")
-X = df.iloc[:, :-1].values  # Embeddings
-y = df.iloc[:, -1].values   # Labels
-
-
-
-⸻
-
-Citation
-
-If you use this dataset collection, please cite:
-
-@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 is part of the QuantumVE project.