Course Syllabus
Obiettivi
Il corso si tiene in lingua inglese. Si veda pagina in inglese.
Periodo di erogazione dell'insegnamento
Secondo semestre
Sustainable Development Goals
Aims
The aim of this course consists of enabling to identify which architecture (gate model, adiabatic, measurement based) of quantum computer is most suitable for solving a given problem by a quantum algorithm, and to program both simulators of quantum computers and actual quantum computers. Fundamental of quantum algorithms and their applications including quantum chemistry and quantum neural networks are introduced.
Contents
The mathematical tools of quantum mechanics relevant for quantum computing are introduced, together with their connection with actual up to date harware. Quantum hardware technologies are compared, with special enphasis on superconducting and trapped ion quantum computers. Next, major quantum algorithm with actual implementation are introduced, for both gate model quantum computers and adiabatic quantum computers. Practical examples of implementations of algorithms are developed during the laboratory activity.
Detailed program
Principles f quantum mechanics, the Qubit: Bloch Sphere and Single Qubit Rotations, Two qubits gates, Entanglement,. Di Vincenzo criteria and Physical implementation (Superconducting, Ions), Architectures: Gate model, Adiabatic Quantum Computer, Measurement based (or One Way) Quantum Computer. Fundamental Algorithms: Search Algorithms, Quantum Fourier Transform, Quantum annealing algorithms, Variational quantum algorithms, Quantum Boltzmann machine, Introduction to Quantum Chemistry, Quantum Neural Networks
Prerequisites
Linear algebra, Dirac notation of quantum mechanics, unitary operators, Ising model (from the AI models for Physics Course, held during the same semester).
Teaching form
Lectures and laboratory programming activity. Both of them will be held in presence. Attendance both to lectures and practical examples is warmly recommended.
The programming activity refers to the program by computational lessons in which students can simulate the models. The computational part will take place in Python.
Textbook and teaching resource
Nielsen and Chuang Quantum Computation and Quantum Information
Rieffel and Polack Quantum Computing a Gentle Introduction"
Morita and Nishimori, Mathematical Foundation of Quantum Annealing (Free online PDF) https://arxiv.org/pdf/0806.1859.pdf
Stefano Olivares, Lecture Notes on Quantum Computing (Free online PDF) https://sites.unimi.it/olivares/wp-content/uploads/2021/08/lectures_qc_olivares_v5.0.pdf
Semester
Second
Assessment method
Students are required to prepare a written report on one of the laboratory activities (or alternative, develop an independent project), the exam will then consist in oral questions on the topics covered during lectures.
Office hours
Wednesday 17-18
