A Summer School promoted by the Department of Mathematics of the University of Trento
When: 10-15 July 2023
Where: University of Trento
Povo 1 building at polo F. Ferrari, Room A102
Via Sommarive 5, 38123 Povo - Trento
The school will be held exclusively in presence in Trento. In case of impediments due to the COVID-19 pandemic, the school will run remotely on the same dates.
Quantum Machine Learning is a rapidly emerging research area where the power of quantum computing is applied to machine learning tasks and represents one of the most promising applications of fault-tolerant quantum computers. Despite the large number of recent achievements in this area, several challenges are still present. Fundamental questions, such as the effective uses of quantum algorithms and the proof of quantum supremacy in this field, need to be addressed. To this end, effective mathematical techniques play a fundamental role.
The aim of the School is to present in an accessible way to a wide audience the mathematical theory underlying Quantum Machine Learning, through three mini courses held by researchers active in this field. Moreover, the School aims to provide an opportunity for different communities to meet up, fostering the interactions, allowing exchanges of ideas and methods and contributing to the diffusion of open problems.
The School is meant mainly for master and graduate students, but also for postdocs, young as well as senior researchers interested in approaching this blooming research field.
The ideal participant has a good background in Mathematics, Probability, Statistics or Data Science. However the application is open to everyone.
The course will be delivered in English.
Master and PhD students: 50euro
Non academics: 200euro
Registration includes coffee breaks and lunches.
Attendance is limited to 60 people. Registration is compulsory. To register follow this [TBD]: you will be asked some information about yourself and standard documentation. To receive full consideration please submit your application no later than 1 June 2023.
For further information, please contact firstname.lastname@example.org