updated readme

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,83 @@
+---
+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.

README.md

@@ -1,29 +1,83 @@
 ---
 license: mit
-title: "Vector Embeddings for Quantum ML (QuantumVE)"
+title: "Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning"
 ---
 
-For more information, visit the GitHub repository: [QuantumVE on GitHub](https://github.com/sebasmos/QuantumVE/tree/main).
+## Overview
 
-This work was supported by the Google Cloud Research Credits program under the award number GCP19980904.
+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:
 
-## Contributing to QuantumVE
+<model_name>_<embedding_dim>.csv
 
-Feel free to contact me at [sebasmos@mit.edu](mailto:sebasmos@mit.edu).
+Where:
 
-## License
+- `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**.
 
-QuantumVE is free and open source, released under the MIT License.
+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
 
-## Please Cite as
 
-```bibtex
+
+⸻
+
+Citation
+
+If you use this dataset collection, please cite:
+
 @misc{cajas_quantumve_2025,
-  author       = {Sebastián Andrés Cajas Ordóñez, Carlos Duran and Luis Torres},
-  title        = {QuantumVE: A versatile platform for quantum embeddings},
+  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.