Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Artificial Intelligence - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
1 course among the following2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud, Visual intelligence, Statistical learning - 1st and 2nd year: Computer Vision & Deep Learning)2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud - 1st and 2nd year: Computer Vision & Deep learning)2 courses among the following (A.A. 2023/24: Complex systems and Network Science not activated)Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Quantum Computing (2023/2024)
Teaching code
4S008917
Credits
6
Coordinator
Not yet assigned
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Courses Single
Authorized
The teaching is organized as follows:
Teoria
Credits
5
Period
Semester 2
Academic staff
Alessandra Di Pierro
Laboratorio
Credits
1
Period
Semester 2
Academic staff
Alessandra Di Pierro
Learning objectives
This course aims at introducing the basic concepts of Quantum Computing via the study of the physical phenomena characterising this paradigm with respect to the classical one. The course is articulated into three main parts: 1) the study of the quantum circuit model and its universality; 2) the study of the most important quantum techniques for the design of algorithms and their analysis; 3) the introduction of various quantum programming languages and of some of the existing quantum software platforms. it is expected that the students who attend the course, will be able at the end to autonomously undertake more specialised studies in the quantum research field of their choice.
Program
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UL: Teoria
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Part I: Quantum Computing
Basic notions from quantum mechanics and linear algebra
The qubit
Quantum Computability: The quantum circuit model, Universality
Quantum Algorithms and Complexity: The BQP class, Phase estimation, Factoring, Quantum search
NISQ Computers
Demonstration in Lab
Part II: Quantum Software
Quantum Languages
Quantum Compilation
Quantum Machine Learning
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UL: Laboratorio
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Part I: Quantum Computing
Basic notions from quantum mechanics and linear algebra
The qubit
Quantum Computability: The quantum circuit model, Universality
Quantum Algorithms and Complexity: The BQP class, Phase estimation, Factoring, Quantum search
Part II: Quantum Software
Quantum Languages
Quantum Compiling
Quantum Machine Learning
Learning assessment procedures
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UL: Teoria
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Written exam: Test composed by five problems to be solved in two hours.
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UL: Laboratorio
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Project to be implemented on Qiskit
