Sudhir878786/QML-HEP-GSoC-2023-Tasks

QML-HEP GSoC 2023 Tasks

Implementation of Quantum Generative Adversarial Networks to Perform High Energy Physics Analysis at the LHC

The project description can be found here The tasks description can be read here.

Below are the clickable file links for each task.

• Task I is trivial and straightforward. Task I: Quantum Computing Part

• Task II Task II: Classical Graph Neural Network (GNN)

  • Given to use ParticleNet’s data for Quark/Gluon jet classification available here

• Task III: These is an Open Task. Task III: Open Task

• Task IV Task IV: Quantum Generative Adversarial Network (QGAN)

  • Input data samples (simulated with Delphes) is provided in NumPy NPZ format Download Input.

• Task VII Task VII: Equivariant quantum neural networks

• Task VIII Task VIII: Vision transformer/Quantum Vision Transformer

• model_checkpoints contains the checkpoints for the particle net lite model.

• particle_net_lite_history.json contains the history for particle net lite model. These are generated during the Task IV.

• tf_keras_model.py is used during Task II.

• circuit generated in task I,y_train.gif, model.png are images generated during the tasks.

Pdf for Tasks Performed

• Task I
• Task II
• Task IV
• Task VII
• Task VIII