Sciences
Subject: INTRODUCTION TO QUANTUM INFORMATION PROCESSING (A.A. 2022/2023)
master degree course in COMPUTER SCIENCE
| Course year | 1 |
|---|---|
| CFU | 6 |
| Teaching units |
Unit Quantum physics and information
Related or Additional Studies (lesson)
|
|
Unit Quantum gates and algorithms
Related or Additional Studies (lesson)
|
|
| Moodle portal | |
| Exam type | oral |
| Evaluation | final vote |
| Teaching language | english |
Teachers
Andrea BERTONI
Filippo TROIANI
Overview
At the end of the course the student will know the basic concepts of quantum information processing, he/she will be able to understand, implement and use simple quantum algorithms, he/she will know a technology platform form quantum computing and its physical implementation. The student will be able to use a software platform for designing and testing of quantum gate networks, and run the computational process on quantum computing simulators and a real quantum computer.
Admission requirements
Linear algebra.
Course contents
Module 1 : "Quantum physics and information"
- Needs for a quantum description of physical systems. Uncertainty principle. Quantum states and Bra/Ket notation, their unitary time evolution, and the quantum measurement process. Quantum coherence and entanglement From bits to qubits. Quantum parallelism. (1 CFU)
- Born rule. No-cloning theorem. Quantum state preparation. One- and Two-qubit gates. The Toffoli gate. Deutsch's problem. (1 CFU)
- Quantum solution of the Bernstein-Vazirani problem. Simon's problem. Pure two-qubit states and their density matrix. Von Neumann entropy as a mean to quantify entanglement. (1 CFU)
Module 2 "Quantum gates and algorithms"
- Quantum period finding and quantum Fourier transform. (1 CFU)
- Searching with a quantum computer. The Grover iteration. Quantum error correction. (1 CFU)
- Quantum teleportation protocol and Bell states. Quantum communication: Elements af quantum criptography and quantum key distribution protocols. (1 CFU)
Teaching methods
Lessons will be held face-to-face with classroom-taught lesson and practice exercises based on quantum computing IBM platform. Lesson notes will be given, together woth lectures-related news, through the MOODLE page of the course. (Warning: the course infos and references are on the MOODLE page of the Informatica course of the FIM department). The student will also be asked to discuss the methods and tools of scientific communication starting from his own naturalistic skills, thus creating a space for comparing knowledge of the content and dissemination strategy.
Assessment methods
Oral face-to-face interview with blackboard or other free-drawing tools. Test of proficency with IBM Quantum Composer online tool. The single exam/session will include both course modules. Interview via videoconference systems (e.g. Google Meet) when dictated by University rules.
Learning outcomes
Knowledge and understanding; Understanding of basic quantum mechanics and of how its peculiarity can be exploited for quantum information processing.
Applying knowledge and understanding; Ability of reproducing the quantum algorithms introduced and of designing simple networks of quantum logic ngates.
Making judgements; Ability to evaluate the sppedup induced by a quantum approach and to assess the employment of the methods presented in a manufacturing environment.
Communication skills: Ability to describe basic concepts of quantum mechanics and quantum entanglement to professionals working in computer science fields, with no specific knowledge of quantum mechanics. Ability to undersand lectures and textbooks given/written in english.
Learning skills: Capability to understand new quantum algorithms and to assess their benefit in different contexts.
Readings
MAIN:
N. David Mermin, Quantum Computer Science. An Introduction.
Cambridge University Press
AUXILIARY:
Giuliano Benenti, Principles of Quantum Computation and Information Volume 1.
New Jersey: World Scientific.
Scott Aaronson, Quantum Computing since Democritus.
Cambridge University Press