SPRING SCHOOL

Title: "An introduction to Quantum Machine Learning"

Abstract: Driven by advancements in computing power and algorithmic improvements, machine learning methods have evolved into effective instruments for identifying patterns within data. Quantum systems generate unique patterns which are believed to be inefficiently produced by classical systems, leading to the hypothesis that quantum computing might excel in machine learning tasks over traditional computing. Quantum machine learning investigates the creation and application of quantum software to potentially achieve machine learning at a speed surpassing that of conventional computers. In this lecture, i will review the recent advancement in the field woth practical examples.

Bio Dr. Paolo Stornati is a Theoretical physicist, currently holding a postdoctoral position in ICFO, Barcelona in the Quantum Optic theory group led by Prof. Maciej Lewenstein.  Dr. Paolo Stornati got his PhD from Humboldt university Berlin in 2021 with a dissertation titled 'Variational quantum simulation of Lattice Field Theory'. Dr. Paolo Stornati's interests include, among others, Quantum simulations, Monte Carlo simulations, Quantum machine learning, Classical generative models for Physics, Tensor network simulations, quantum many body systems and application of quantum simulations to High energy physical systems.