FYS9411 – Computational Physics II: Quantum Mechanical Systems
Schedule, syllabus and examination date
Exams after the reopening
As a general rule, exams will be conducted without physical attendance in the autumn of 2021, even after the reopening. See the semester page for information about the form of examination in your course. See also more information about examination at the MN Faculty in 2021.
This is an advanced course on computational physics with an emphasis on quantum mechanical systems with many interacting particles. The course covers Stochastic methods like various Monte Carlo methods,many-body methods like coupled-cluster theory and others, as well as machine learning applied to quantum mechanical systems, quantum computing and quantum machine learning.
The applications and the computational methods are relevant for research problems in such diverse areas as nuclear, atomic, molecular, and solid-state physics, chemistry, and materials science. A theoretical understanding of the behavior of quantum-mechanical many-body systems - that is, systems containing many interacting particles - is a considerable challenge since in general no analytical or closed form solutions can be found; instead, numerical methods are needed for approximate but accurate simulations of such systems on modern computers. New insights and a better understanding of complicated quantum mechanical systems can only be obtained via large-scale simulations. The capability to study such systems is of high relevance for both fundamental research and industrial and technological advances.
After having completed the course:
- you will have knowledge on how to simulate complicated many-particle systems using stochastic methods (Variational and Diffusion Monte Carlo methods).
- you will have knowledge on resampling techniques for statistical data analysis.
- you will know how to implement efficiently your codes for high-performance computing applications
- you will learn about many-body methods like coupled cluster theory, Hartree-Fock theory and full configuration interaction theory.
- you will learn how to simulate many-particle systems using quantum computing algorithms
- you will learn how to perform data analysis using quantum machine learning algorithms.
- you will learn to implement machine learning algorithms for solving quantum mechanical many-particle systems.
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.
Recommended previous knowledge
- 10 credits overlap with FYS4411 – Computational Physics II: Quantum Mechanical Systems.
- 5 credits overlap with FYS4410 – Computational physics II (discontinued).
- 5 credits overlap with FYS9410 – Computational physics II (discontinued).
This course has 5 hours of teaching per week, and the teaching consists of:
- 2 hours of lectures
- 3 hours of computer laboratory
- Two large projects which are evaluated and graded. Each project counts 50% of the final grade. Final grade based on the two projects.
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: FYS4411 – Computational Physics II: Quantum Mechanical Systems
Grades are awarded on a pass/fail scale. Read more about the grading system.
Resit an examination
This course offers both postponed and resit of examination. Read more: