Training and Research
PhD Programme Courses/classes
Non monotonic reasoning
Credits: 3
Language: English
Teacher: Matteo Cristani
Sustainable Embodied Mechanical Intelligence
Credits: 3
Language: English
Teacher: Giovanni Gerardo Muscolo
Brain Computer Interfaces
Credits: 3
Language: English
Teacher: Silvia Francesca Storti
A practical interdisciplinary PhD course on exploratory data analysis
Credits: 4
Language: English
Teacher: Prof. Vincenzo Bonnici (Università di Parma)
Multimodal Learning and Applications
Credits: 5
Language: English
Teacher: Cigdem Beyan
Introduction to Blockchain
Credits: 3
Language: English
Teacher: Sara Migliorini
Autonomous Agents and Multi-Agent Systems
Credits: 5
Language: English
Teacher: Alessandro Farinelli
Cyber-physical systems security
Credits: 3
Language: English/Italian
Teacher: Massimo Merro
Foundations of quantum languages
Credits: 3
Language: English
Teacher: Margherita Zorzi
Advanced Data Structures for Textual Data
Credits: 3
Language: English
Teacher: Zsuzsanna Liptak
AI and explainable models
Credits: 5
Language: English
Teacher: Gloria Menegaz, Lorenza Brusini
Automated Software Testing
Credits: 4
Language: English
Teacher: Mariano Ceccato
Elements of Machine Teaching: Theory and Appl.
Credits: 3
Language: English
Teacher: Ferdinando Cicalese
Introduction to Quantum Machine Learning
Credits: 4
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 (2023/2024)
Teacher
Referent
Credits
4
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
