QML-HEP

This project is my attempt to solve the tasks given as a part of screening process for Quantum Machine Learning for High Energy Phiysics (QML-HEP) Google Summer of Code (GSOC) projects. However, I didn’t submit a project proposal🥲

Task 1 - Quantum Computing

I had to implement basic quantum circuits involving different gates and measure probabilities of states using the Google Cirq.

Task 2 - Quantum Generative Adversarial Network (QGAN)

I had to explore how to apply a QGAN to solve a High Energy Data analysis issue, more specifically, separating the signal events from the background events. I had to use the Google Cirq and Tensorflow Quantum libraries.

Task 3 - Quantum Convolutional Neural Network (QCNN)

I had to setup and apply a quantum CNN on particle physics data to perform a binary classification on electrons and photons. Again I had to use Tensorflow Quantum library.

Task 4 - Classical Graph Neural Network (GNN)

I had to use ParticleNet’s data for Quark/Gluon jet classification and use 2 GNN architectures of my choice to classify the jets as being quarks or gluons. I did this using Deep Graph Library.

Task 5 - Open Task

I had to comment on quantum computing or quantum machine learning. I had to include one quantum algorithm or one quantum software that I was familiar with. I will post my solution as a blog post soon.

For the codes and jupyter notebooks, please check here.