FYS5419 – Quantum computing and quantum machine learning
Course description
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
Students admitted at UiO must?apply for courses?in Studentweb. Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.
Nordic citizens and applicants residing in the Nordic countries may?apply to take this course as a single course student.
If you are not already enrolled as a student at UiO, please see our information about?admission requirements and procedures for international applicants.
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 FYS9419 – Quantum computing and quantum machine learning.
Teaching
- Two hours of lectures per week.
The course includes two projects which are to be graded.?
Examination
Two projects (max. 10 pages per project) which are evaluated and graded. Each project counts?50 % of the final grade. The projects are to be delivered in Inspera. Final letter grade based on the two?projects.
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: FYS9419 – Quantum computing and quantum machine learning
Examination support material
All examination support material is allowed.
Grading scale
Grades are awarded on a scale from A to F, where A is the best grade and F?is a fail. Read more about?the grading system.
Resit an examination
In this course, postponed exams are not offered for exam candidates who are ill before the exam or who become ill during the exam. A deferred submission deadline can be offered. The illness must be documented with a doctor's certificate dated no later than the ordinary submission date. You must submit the doctor's certificate to the course's contact point before the submission deadline for the home exam. New exams are not offered to candidates who withdraw or do not pass the regular 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.