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+
+# Transfer learning in hybrid classical-quantum neural networks
+A transfer learning approach applied to hybrid neural networks composed of classical and quantum elements.
+
+This repository contains the source code related to the research paper *"Transfer learning in hybrid classical-quantum neural networks"* [arXiv:xxxx.xxxx](https://arxiv.org/abs/xxxx.xxxx) (2019).
+
+![Figure](static/figure_c2q_notebook.png)
+
+## Contents
+* `dressed_circuit.ipynb`: Jupyter notebook to train and test a _dressed_ variational quantum circuit for the classification of a syntetic dataset of 2-dimensional points (spirals). 
+
+* `c2q_transfer_learning_ants_bees.ipynb`: Jupyter notebook to train and test a hybrid neural network for the classification of high-resolution images (_ants_ and _bees_). This example is based on a classical-to-quantum (CQ) transfer learning approach.
+
+* `c2q_transfer_learning_dogs_cats.ipynb`: Jupyter notebook to train and test a hybrid neural network for the classification of CIFAR images (_dogs_ and _cats_). This example is based on a classical-to-quantum (CQ) transfer learning approach.
+
+* `c2q_transfer_learning_cars_planes.ipynb`: Jupyter notebook to train and test a hybrid neural network for the classification of CIFAR images (_cars_ and _planes_). This example is based on a classical-to-quantum (CQ) transfer learning approach.
+
+* `q2c_transfer_learning.ipynb`: Jupyter notebook to train and test a hybrid neural network for the classification of continuous variable quantum states. This example is based on a quantum-to-classical (QC) transfer learning approach.
+
+* `q2q_transfer_learning.ipynb`: Jupyter notebook to train and test a hybrid neural network for the classification of continuous variable quantum states. This example is based on a quantum-to-quantum (QQ) transfer learning approach.
+
+* `pre_trained\`: folder containing pre-trained variational parameters saved as NumPy files. They are loaded by the notebooks `q2q_transfer_learning.ipynb` and `q2q_transfer_learning.ipynb`.
+
+* `static\`: folder containing some static images.
+
+## Usage and examples
+
+To visualize the content of the Jupyter notebooks without running the code there are two alternatives:
+1. Navigate with a browser the GitHub repository and simply click on the notebook file. GitHub will automatically visualize the notebook, but the rendering may not be good (especially for LaTeX formulas).
+2. Copy the URL of the notebook file and paste it into [nbviewer](https://nbviewer.jupyter.org).
+
+To open and run a local copy of a notebook one should apply the following steps:
+
+1. If missing, install [JupyterLab](https://jupyter.org/install).
+2. Run the command:
+```
+$ jupyter notebook
+```
+3. Navigate to the local notebook file and open it.
+
+## Requirements
+
+#### Software
+All notebooks require the installation of JupiterLab with a Python 3 kernel. In addition the library matplotlib is required for generating plots and images.
+
+The notebook `dressed_circuit.ipynb` requires the library PennyLane with the ProjectQ plugin.
+
+The notebooks `c2q_transfer_learning_*.ipynb` require the library PennyLane with the ProjectQ and PyTorch plugins.
+
+The notebooks `q2c_transfer_learning.ipynb` and  `q2q_transfer_learning.ipynb` require the library Strawberry Fields with the TensorFlow backend. **Due to subsequent interface upgrades, these notebooks will work only with Strawberry Fields version <= 0.10.0.**
+
+#### Datasets
+The notebook `c2q_transfer_learning_ants_bees.ipynb` requires to manually download the dataset, consisting of the _Hymenoptera_ subset of ImageNet (ants and bees). The dataset can be downloaded [here](https://download.pytorch.org/tutorial/hymenoptera_data.zip) and should extracted in the subfolder `[current directory]\data\hymenoptera_data\`.
+
+The notebooks `c2q_transfer_learning_dogs_cats.ipynb` and `c2q_transfer_learning_cats_planes.ipynb` will automatically download the CIFAR10 dataset.
+
+The datasets (random quantum states) for the notebooks `q2q_transfer_learning.ipynb` and `q2q_transfer_learning.ipynb` are automatically generated on the fly, during the training and testing phases.
+
+## Authors
+
+Author1, Author2, et al.
+
+If you are doing any research using this source code, please cite the following paper:
+
+> Author1, Author2, ... *"Transfer learning in hybrid classical-quantum neural networks"* [arXiv:xxxx.xxxx](https://arxiv.org/abs/xxxx.xxxx), (2019).
+
+## License
+
+This source code is free and open source, released under the Apache License, Version 2.0.