FYS9419 – Quantum computing and quantum machine learning
Course description
Schedule, syllabus and examination date
Course content
Quantum Computing is the intersection of computer science, mathematics, and quantum physics which utilizes the phenomena of quantum mechanics to perform computations that classical computers cannot perform.
Quantum computers are faster than classical computers and provide significant speedup for many problems in the natural sciences, from encryption algorithms to quantum computing.
The aim of this course is to present how quantum computing algorithms can be used to study quantum mechanical systems and how they can be used to solve machine learning problems. The course explores core concepts of quantum computing such as superposition, interference and entanglement as well as how to set up quantum gates and construct quantum circuits. It discusses also quantum gate decomposition and quantum circuit optimization of large quantum circuits and how to study quantum machine learning algorithms. Finally, it discusses how to run these algorithms on both classical and real quantum computers.
Learning outcome
After completing this course, you are able to:?
- apply quantum computing algorithms to selected quantum-mechanical many-particle systems.
- describe the differences between quantum and classical computation of quantum mechanical many-particle systems.??
- discern potential performance gains of quantum vs. classical algorithms.
- implement and design quantum circuits for studies of quantum mechanical systems.??
- run these algorithms on existing quantum computers.??
- understand the role of noise in quantum computing.??
- implement central machine learning algorithms on quantum computers.
- study both classical and quantum mechanical data sets.
Admission to the course
PhD candidates from the University of Oslo should apply for classes and register for examinations through?Studentweb.
If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.
PhD candidates who have been admitted to another higher education institution must?apply for a position as a visiting student?within a given deadline.
Capacity: 20 students
Recommended previous knowledge
A good background in mathematics is needed.
Other recommended courses:
- FYS3110 – Quantum Mechanics?
- FYS4480 – Quantum mechanics for many-particle systems?
- FYS-STK4155 – Applied Data Analysis and Machine Learning
- MAT3420 – Quantum Computing
Overlapping courses
- 10 credits overlap with FYS5419 – Quantum computing and quantum machine learning.
Teaching
- Two weekly lectures, 45 minutes each, and to projects which are to be graded.
Examination
-
Two projects (max. 10 pages per project) which are evaluated. Each project counts?50% and you need to pass both projects in order to pass the course. The projects are to be delivered in Inspera.
?
When writing your exercises make sure to familiarize yourself with the rules for use of sources and citations. Breach of these rules may lead to suspicion of attempted cheating.
It will also be counted as one of the three attempts to sit the exam for this course if you sit the exam for one of the following courses: FYS5419 – Quantum computing and quantum machine learning
Examination support material
All examination support material is allowed.
Grading scale
Grades are awarded on a pass/fail scale. Read more about?the grading system.
Resit an examination
Students who can document a valid reason for absence from the regular examination are offered a?postponed exam?at the beginning of the next semester.
New examinations?are offered at the beginning of the next semester for students who do not successfully complete the exam during the previous semester.
We do not offer a re-scheduled exam for students who withdraw during the exam.
More about examinations at UiO
- Use of sources and citations
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.