IQM-Challenge

C-NOTpolitecnico

This repository contains all the materials for the 2023 ETH Zurich Quantum Hackathon and IQM-Challenge about exploting symmetries in quantum machine learning. The team members are Chiara Ballotta, Davide Cugini, Francesco Ghisoni, Francesco Scala.

Folders

Tic-tac-toe

This folder contains all the Python code for the tic-tac-toe problem. The sub-folders are non symmetric, partially symmetric, and symmetric. These folders contain the results from different quantum neural network layouts.

In addition, you have three Jupyter Notebook files used to generate the respective results:

• tic_tac_toe_partially_symm.ipynb
• tic_tac_toe_random_QNN.ipynb
• tic_tac_toe_symm.ipynb

Highlight

Thanks to the particular libraries employed we were able to have high performance simulations like the symmetric QNN with 4 layers and 4 sublayers that the authors of the paper were not able to simulate

Schwinger

This folder contains all the Python code for the Quantum Field theory analysis using geometric quantum machine learning techniques. The sub-folders are asymmetric and symmetric, which contain the results from the experiments.

In addition, you have three iPython files used to run the experiments:

• Schwinger_circuit_asym.ipynb
• Schwinger_circuit.ipynb
• Generator.ipynb

The Generator.ipynb file is used to generate the dataset.

Extra foleders

The repository also contains the original .zip file of the challenge and the correspondant folder: eth-qec-hackathon-2023-main

Presentation

Qhack_Presentation.pdf is a slide presentation summing up the main results of the project.

Getting Started

To get started with this repository, simply clone it to your local machine using the following command:

git clone https://github.com/fran-scala/eth-qec-hackathon-2023.git

Once you have cloned the repository, you can navigate to the relevant folder to access the Python code.

Libraries

Particular libraries required are: pennylane, jax and optax. If you need to install them you can just do it by typing the following lines in your terminal:

pip install pennylane
pip install jax
pip install optax

Pennylane is a quantum computing library designed for variational quantum algorithms and quantum machine learning. JAX is NumPy on the CPU, GPU, and TPU, with great automatic differentiation for high-performance machine learning research and Optax is a gradient processing and optimization library for JAX.