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 ( Citation: Kasieczka, Nachman & al., 2021 Kasieczka, G., Nachman, B. & al. (2021). The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics. https://doi.org/10.1088/1361-6633/ac36b9 ) . 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 ( Citation: Preskill, 2018 Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum 2, 79 (2018). https://doi.org/10.22331/q-2018-08-06-79 ) 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.
- Kasieczka, Nachman & al. (2021)
- Kasieczka, G., Nachman, B. & al. (2021). The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics. https://doi.org/10.1088/1361-6633/ac36b9
- Preskill (2018)
- Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum 2, 79 (2018). https://doi.org/10.22331/q-2018-08-06-79