Training and Research
PhD Programme Courses/classes
This page shows the PhD course's training activities for the academic year 2024/2025. Further activities will be added during the year. Please check regularly for updates!
Introduction to Blockchain
Credits: 3
Language: English
Teacher: Nicola Fausto Spoto
Principles and Applications of Abstract Interpretation
Credits: 3
Language: English
Teacher: Michele Pasqua
AI and explainable models
Credits: 5
Language: English
Teacher: Lorenza Brusini
Automated Software Testing
Credits: 4
Language: English
Teacher: Mariano Ceccato
Multi Omics Patient Stratification
Credits: 3
Language: English
Teacher: Rosalba Giugno
ACADEMIC WRITING IN LATEX
Credits: 3
Language: English
Teacher: Enrico Gregorio
A practical interdisciplinary PhD course on exploratory data analysis
Credits: 4
Language: English
Teacher: Rui Pedro Fernandes Ribeiro
Cyber-physical systems security
Credits: 3
Language: English
Teacher: Massimo Merro
Elements of Machine Learning
Credits: 3
Language: English
Teacher: Ferdinando Cicalese
Genomica informazionale: contenuto informativo dei genomi e s divergenza dalla randomicità
Credits: 3
Language: English
Introduction to Quantum Machine Learning
Credits: 3
Language: English
Teacher: Alessandra Di Pierro
Laboratory of quantum information in classical wave-optics analogy
Credits: 3
Language: English
Teacher: Claudia Daffara
Introduction to Quantum Machine Learning (2024/2025)
Teacher
Referent
Credits
3
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
This course aims to provide an introduction to Quantum Machine Learning (QML), starting from fundamental concepts and progressing to some of the main techniques exploiting quantum computation for machine learning.
Prerequisites and basic notions
Linear algebra, probability and statistics
Program
- Introduction to Quantum Systems
Quantum Computation
Gate Model
Adiabatic Quantum Computing
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 using Jupiter Notebook
Bibliography
Didactic methods
Slides and blackboard
Learning assessment procedures
Oral Exam
Assessment
The knowledge acquired will be evaluated on the basis of the presentation of a topic of your choice. Personal in-depth study and understanding of the subject will be assessed.
Criteria for the composition of the final grade
Score out of thirty
