This year I’m participating in the Google Summer of Code with the ML4SCI organization. My project proposal deals with a quantum variational autoencoder (QVAE) for the anaysis of particle physics data. Such unsupervised learning paradigms can be used to search for new physics in a model-agnostic way (No matching key was found for `Kasieczka2021` in the references. Please make sure to provide an available ID in your `bib.json` file.) . These models are thereby trained on Standard Model data and search for anomalous events that deviate from the known physics. With the rise of NISQ-devices (No matching key was found for `Preskill2018` in the references. Please make sure to provide an available ID in your `bib.json` file.) the question comes up if quantum machine learning can enhance classical machine learning applications to hep problems.
Since we are encouraged by Google to publicly share our work, I set up this blog to document the project and share some of my scientific interests.
Bibliography called, but no references