Course syllabus
Course-PM
MCC155 / FCC155 Quantum computing, sp2 HT23 (7.5 hp)
The course is offered by the department of Microtechnology and Nanoscience
Contact details
- Examiner and lecturer: Anton Frisk Kockum, anton.frisk.kockum@chalmers.se
- Lecturer: Giulia Ferrini, ferrini@chalmers.se
- Teaching assistant: Ariadna Soro Álvarez, soro@chalmers.se
Administrative support for the Aachen-Chalmers international course exchange:
- Lisa Otten, lisa.otten@rwth-aachen.de
- Ann-Marie Endresen, ann-marie.endresen@chalmers.se
Student representatives:
- MPCAS: Frida Fjelddahl, fridafj@student.chalmers.se
- MPNAT: Hang Zou, hangzo@chalmers.se
- EMNAN: Maurizio Toselli, tosellim@student.chalmers.se
- Exchange programs: Lars Mester (larsmest@gmail.com) and Melina Pees (melina.pees@tum.de)
- RWTH Aachen: Dominik Seip, dominik.seip@rwth-aachen.de
Course purpose
The aim of this overview course is to familiarise the students with both important quantum algorithms (such as the quantum Fourier transform, phase estimation, and Shor's algorithm), variational quantum algorithms that utilise an interplay between classical and quantum computers [such as the variational quantum eigensolver (VQE), and the quantum approximate optimisation algorithm (QAOA), among others], and the intersection of quantum computing and machine learning. The course will also give the students practical experience of programming a quantum computer.
Quantum computers are rapidly improving, and recently ”quantum computational primacy” was achieved, i.e., a quantum computer was able to perform a computational task much faster than a classical computer. Quantum computing is expected to have applications in many areas of society. The course prepares the students for applying quantum computation to a variety of important problems.
Schedule
TimeEdit
Time: Mondays 13:15 (week 2: 15:15), Thursdays 8:00, Fridays 15:15 (week 1: Thursday 10:00)
Place: Most lectures in SB3-L110, the rest in EA; Zoom links are provided for the lectures for the Aachen students
Course literature
- Nielsen and Chuang, Quantum Information and Quantum Computation (available at Chalmers STORE)
- Course notes (available on this course homepage and as a preprint on the arXiv)
Course design
The course comprises lectures, tutorial exercise sessions, and a programming laboratory exercise. There are two hand-ins during the course and a final exam.
Learning objectives and syllabus
Learning objectives: After completion of the course, the student should be able to
- List modern relevant quantum algorithms and their purposes.
- Explain the key principles of the various models of quantum computation (circuit, measurement-based, adiabatic model).
- Explain the basic structure of the quantum algorithms addressed in the course that are based on the circuit model, and to compute the outcome of basic quantum circuits.
- Compare, in terms of time complexity, what quantum advantage is expected from the quantum algorithms addressed in the course with respect to their classical counterparts.
- Program simple quantum algorithms on a cloud quantum computer or a cloud simulator.
- Understand the basic principles of the continuous variable encoding for quantum information processing.
- Give examples of the motivation for applying quantum computing to machine learning and of what the obstacles are to achieving an advantage from doing so.
Content:
- Elementary quantum gates and basic quantum computing formalism
- Circuit model for quantum computation
- Foundational theorems for quantum computation: Solovey-Kitaev theorem; Gottesman-Knill theorem
- Grover's algorithm
- Quantum error correction
- Quantum Fourier transform and phase estimation algorithms
- Shor’s algorithm
- Quantum machine learning
- Quantum cloud computing exercise
- Other models for universal quantum computation beyond the circuit model: measurement-based quantum computation and adiabatic quantum computation
- Introduction to complexity classes and relevant conjectures
- Quantum algorithms for solving combinatorial optimization problems: quantum annealing and QAOA
- Variational quantum eigensolver
- Sampling models: boson sampling and instantaneous quantum polynomial
- Continuous-variable (CV) quantum computation, measurement-based quantum computation in CV and GKP encoding
Link to the syllabus on Studieportalen
Examination form
The assessment comprises two hand-ins and a a final written exam.
The credits distribution is as follows: each of the hand-ins counts for 15% towards the total grade, resulting in 30%, and 2 hp; the written exam counts for 70% towards the final grade, and 5.5 hp. The total points determine the grade (F, 3, 4, 5), according to the cut-offs 50% = 3, 70% = 4, 85% = 5. However, you need to score at least 40% both at the written exam and on the total of the two hand-ins in order to receive a passing grade.
For the written exam, you will be allowed to use one A4 page (front and back) of notes that you can prepare beforehand. Computers, cell phones, books, or course notes are not allowed. A basic calculator (not a big scientific calculator) is allowed.
Hand-in 1 is due November 16, 2023.
Hand-in 2 is due December 11, 2023.
The exam takes place in the morning of January 13, 2024.
Re-exams are offered in the afternoon of April 3, 2024 and in the morning of August 30, 2024.
Course summary:
| Date | Details | Due |
|---|---|---|