99248 - QUANTUM COMPUTING

Learning outcomes

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.

Course contents

- 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.
Applications.

Readings/Bibliography

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

Teaching methods

The course is organized in 3 modules that will cover Quantum Computing and Quantum Machine Learning Techniques. Hands-on sessions will be organized, students will be introduced to some of the available platforms for emulation and access to real quantum computers, such as IBM-QISKIT.

Assessment methods

Students with Specific Learning Disabilities (SLD) or temporary/permanent disabilities are advised to contact the University Office responsible in a timely manner (https://site.unibo.it/studenti-con-disabilita-e-dsa/en ). The office will be responsible for proposing any necessary accommodations to the students concerned. These accommodations must be submitted to the instructor for approval at least 15 days in advance, and will be evaluated in light of the learning objectives of the course.

Office hours

See the website of Tommaso Calarco

See the website of Elisa Ercolessi

See the website of Daniele Bonacorsi