ericardomuten/CERN-QGAN
A CERN openlab Summer Student Programme 2021 Project Repository. In this work, Quantum Generative Adversarial Networks are developed to simulate the ttH production processes in the LHC experiment.
This project helps high-energy physicists simulate particle collision events, specifically the ttH production process, at the Large Hadron Collider (LHC). It takes raw detector data from these events and generates new, synthetic event data that closely mimics real observations. Researchers in particle physics would use this to create large datasets for training analyses without running costly, time-consuming experiments.
No commits in the last 6 months.
Use this if you need to generate high-fidelity synthetic data for specific particle physics processes, like ttH production, to supplement experimental data or explore theoretical models.
Not ideal if you need a general-purpose data generation tool for domains outside of high-energy physics or if you are not working with quantum computing concepts.
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Jupyter Notebook
License
MIT
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Last pushed
Nov 22, 2021
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