Training and Research
PhD Programme Courses/classes
This page lists the training activities for the PhD programme for the academic year 2025/2026. Additional activities will be added during the year. Please check back regularly for updates!
Teoria dei Grafi
Credits: 6
Language: inglese
Teacher: Romeo Rizzi
Introduction to Blockchain
Credits: 3
Language: English
Teacher: Sara Migliorini
Automated Software Testing
Credits: 4
Language: English
Teacher: Mariano Ceccato
Introduction to Proof Theory
Credits: 4
Language: Italiano/Inglese
Teacher: Andrea Masini
Teoria dei Grafi
Credits: 6
Language: inglese
Teacher: Romeo Rizzi
Sicurezza dei Sistemi Ciberfisici
Credits: 3
Language: English
Teacher: Massimo Merro
Data Analysis Techniques on Healthcare Data
Credits: 3
Language: English
Teacher: Matteo Mantovani
Elements of Machine Teaching
Credits: 3
Language: English
Teacher: Ferdinando Cicalese
Introduction to Quantum Machine Learning
Credits: 3
Language: Italiano e Inglese
Teacher: Alessandra Di Pierro
LaTeX per la letteratura accademica
Credits: 3
Language: Inglese
Teacher: Enrico Gregorio
Apprendimento basato su Logica
Credits: 5
Language: Inglese/English
Teacher: Fabio Aurelio D'Asaro
MultiOmics Patien Stratification
Credits: 3
Language: Inglese
Teacher: Rosalba Giugno
Introduction to Quantum Machine Learning (2025/2026)
Teacher
Referent
Credits
3
Language
Italiano e Inglese
Class attendance
Free Choice
Location
VERONA
Learning objectives
The goal of this course is to show what benefits current and future quantum technologies can provide to machine learning in terms of achieving a better performance of methods that are computationally challenging for classical computers.
In particular, the students will be given the adequate notions and knowledge to be able to distinguish between quantum computing paradigms relevant for machine learning; identify problems in machine learning that would benefit from using quantum resources; implement learning algorithms on quantum computers using the available public platforms.
Prerequisites and basic notions
Linear Algebra; Probability and Statistics
Program
The course will award 3 CFU (12 hours frontal lectures). The following is preliminary list of topics that will be discussed:
•Introduction to Quantum Systems;
•Quantum Computation: Gate Model, Variational Circuits;
•Classical-Quantum Learning Algorithms: Encoding Classical Information, Quantum-enhanced Kernel Methods, Quantum Neural Networks;
•Fault-tolerant Quantum Machine Learning;
•Practice: Implementation of the discussed methods on real quantum computers.
Bibliography
Didactic methods
Slides and blackboard
Learning assessment procedures
Presentation of a research work on the subject of the course.
Assessment
Ability to understand the research work in the field.
Criteria for the composition of the final grade
no numerical evaluation
