Anno Accademico 2022/2023

  • Docente: Elisa Ercolessi
  • Crediti formativi: 6
  • SSD: FIS/02
  • Lingua di insegnamento: Inglese
  • Moduli: Elisa Ercolessi (Modulo 1) Daniele Bonacorsi (Modulo 2) Claudio Massimiliano Sanavio (Modulo 3)
  • Modalità didattica: Convenzionale - Lezioni in presenza (Modulo 1) Convenzionale - Lezioni in presenza (Modulo 2) Convenzionale - Lezioni in presenza (Modulo 3)
  • Campus: Bologna
  • Corso: Laurea Magistrale in Physics (cod. 9245)

Conoscenze e abilità da conseguire

At the end of the course students will acquire some fundamental knowledge on: - the theoretical framework for quantum information processing; - theory and applications of quantum programming; - models and methods of quantum machine learning. Students will be able to: - analyze quantum circuits and algorithms, hybrid quantum-classical protocols and quantum machine learning models; - use these tools to solve simple problems in fundamental and applied physics, also with the use of quantum emulators.


- Basics of Quantum Mechanics for Computing.

The qubits: states, evolution and measurements.

Separability and entanglement.

State preparation, distinguishability and fidelity.

Applications to simple quantum information processing protocols.

- Quantum circuits.

Introductions to circuit based universal computers.

Simple and universal quantum gates.

Examples of simple algorithms.

Non cloning theorem and classical computation.

Universality of quantum computation.

Quantum Fourier transform and applications.

Quantum search algorithms.

Quantum Phase Estimation.

Error Correction.

- Complements.

Notions about physical platforms for quantum computation.

Introduction to simulations and other purpose-specific quantum computation.

Hybrid algorithms for optimization problems.

- Quantum Machine Learning.

Review of classical machine learning.

Models and methods of quantum machine learning.



M.A. Nielsen and I.L. Chuang, Quantum Computation and Quantum information, Cambridge

J. Preskill, Quantum information and Computation and Quantum, http://theory.caltech.edu/~preskill/

Other readings will be suggested in class and posted in Virtuale

Metodi didattici

The course consists of:

- 40 hours of class lectures, orgnaized in 2 modules:

Quantum Computing (E. Ercolessi)

Quantum Machine Learning (D. Bonacorsi)

- 16 hours of laboratory of quantum computing, where students will be introduced to some of the available platforms for emulation and access to real quantum computers, such as IBM-QISKIT.

Modalità di verifica e valutazione dell'apprendimento

Oral exam/Project.


The student can choose to:

- take a traditional exam, during which he will be asked to discuss at least two/three topics covered during both parts of the course (prof. Ercolessi, prof. Bonacorsi)

- hand in (one week in advanced of the scheduled date of the exam) a short report or a program about an algorithm for EACH of the two parts of the course (prof. Ercolessi, prof. Bonacorsi), that will be then presented and discussed during the exam.


Students should demonstrate to be familiar and have a good understanding of the different subjects.

The organization of the presentation and a rigorous scientific language will be also considered for the formulation of the final grade.

The “cum laude” honor is granted to students who demonstrate a personal and critical rethinking of the subject.

Strumenti a supporto della didattica

Additional references and all didactic material will be available in the university repository Virtuale.

Orario di ricevimento

Consulta il sito web di Elisa Ercolessi

Consulta il sito web di Daniele Bonacorsi

Consulta il sito web di Claudio Massimiliano Sanavio