QUANTUM COMPUTATION

The course aims to provide an introduction to the principles and models of quantum computation and to the main algorithms in the literature. The goal is to bring students to have critical skills in choosing and implementing a solution based on quantum computation.

1. Knowledge and understanding
1.1. acquire the main models of quantum computing
1.2. acquire the main models of information representation and the link with quantum algorithms
1.3 acquire the main concepts of quantum information theory


2. Ability to apply knowledge and understanding
2.1. know how to apply the models studied to real problems.
2.2. know how to critically evaluate the performance and behavior of a solution based on quantum computing applied to a concrete problem;

3. Judgment
3.1. knowing how to understand which features of the various quantum computation models fit a given problem;
3.2. knowing how to critically evaluate the theoretical characteristics of the proposed models;

Familiarity with classical computation is required.

Quantum Mechanics, quantum states and Dirac notation
Quantum Bits
Quantum Gates
Measurements
Quantum Circuits
Structure of a Quantum Algoritm
Quantum Teleportation
Quantum Parallelism
Deutsch's algorithm
Deutsch-Joza's algorithm
Grover Search
Quantum Fourier Transform
Shor's algorithm
Hidden Abelian Subgroup Problem
HMM algorithm
Quantum SVM and Quantum Neural Networks
Introduction to Quantum Information Theory
Introduction to Quantum Communication and Networking
Introduction to Quantum Error Correction
Quantum Annealing

M. A. Nielsen, I. L. Chuang (2010). Quantum Computation and Quantum Information. Cambridge University Press.
R. Loredo (2020). Quantum Computing with Python and IBM: Quantum Experience. Packt (opzional)

Teaching is aimed at allowing the student to acquire practical skills in the implementation and analysis of quantum algorithms.
Effective learning is verified through an oral exam, but this can optionally be integrated by a series of projects with in-itinere delivery. This allows the student to acquire and demonstrate practical skills in the selection, implementation and analysis of quantum algorithms.

Teaching is aimed at allowing the student to acquire practical skills in the choice and critical analysis of the techniques and methodologies of quantum computing.
Effective learning is verified through a compulsory oral exam and a series of optional ongoing projects. The objective of the projects is to allow the student to immediately apply and verify the skills acquired, in particular the ability to critically analyze the behavior and applicability of the algorithms studied.

English

oral