Hi all,
Tomorrow Elie Bermot will tell us about his master thesis conducted at IBM entitled 'Quantum Generative Adversarial Networks for Anomaly Detection in High Energy Physics'. See below for the abstract. The talk will take place at 13:30 in HIT K 52 or on Zoom: https://ethz.zoom.us/j/362994444.
Best, Ladina
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The standard model (SM) of particle physics is an accomplishment of decades of theoretical and experimental work. It describes almost all known elementary particles and their interactions: strong, weak and electromagnetic. However, some of the observed events occurring in a particle accelerator, e.g., the Large Hadron Collider, correspond to anomalous and unpredictable events, whereby the underlying physics is not governed by the SM. The detection of anomalous events and, the corresponding potential discovery of new fundamental physics, is far from trivial. In fact – until recently – most of the high energy physics data analysis relied on model-specific selection process. In this work, we consider a quantum generative adversarial network (qGAN) based scoring function that identifies anomalous events by determining whether an event is characteristic for a certain background distribution such as the SM. We implement the qGAN training and the anomaly detection scheme with Qiskit and test the method on proof-of-principle examples using numerical simulations as well as actual IBM quantum processors.