To get started with this project, clone the repository and install the required dependencies. You can do this by running the following commands:

git clone https://github.com/username/QuantumTransferLearning.git cd QuantumTransferLearning pip install -r requirements.txt

Replace "username" with your GitHub username.

To train and evaluate the model on the CIFAR10 dataset, run the following command:

python train.py

This will train the model and print out the validation accuracy.

We welcome contributions to this project! To contribute, fork the repository and create a new branch for your changes. When you're ready to submit your changes, create a pull request against the main branch. Please include a detailed description of your changes in the pull request.

This is a revised and modified version of the "quantum_transfer_learning" project from Xanadu, which can be found at the following URL: https://github.com/XanaduAI/quantum-transfer-learning/blob/master/c2q_transfer_learning_cifar.ipynb. We have made several modifications to the original code, including:

• Changing the binary classification task to a multi-class classification task
• Writing our own PyTorch version of the train and validate modules, which are more organized and easier to understand

This project is licensed under the MIT License. See the LICENSE file for more information.