Experimental Particle and Astro-Particle Physics Seminar

Stefanos Leontsinis (University of Zurich (CH))


Theoretical and algorithmic advances, availability of data, and computing power have opened the door to exceptional perspectives for application of classical Machine Learning in the most diverse fields of science, business and society at large, and notably in High Energy Physics (HEP).  In particular, Machine Learning is among the most promising techniques to analyse and understand the data the next generation HEP detectors will produce.

Machine Learning  is  also a  promising  task  for  near-term  quantum  devices  that  can  leverage compressed high dimensional representations and use  the  stochastic  nature  of quantum  measurements  as  random  source. Several architectures are being investigated.  Quantum  implementations of Boltzmann Machines, classifiers or Auto-Encoders, among the most popular ones, are being proposed for different applications. Born  machines  are  purely  quantum  models that can  generate probability distributions in a unique way, inaccessible to classical computers. One-class Support Vector Machines have proven to be very powerful tools in anomaly detection problems.

This talk will give an overview of the current state of the art in terms of Machine Learning on quantum computers with focus on their application to HEP. 

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