FYS4411 – Computational Physics II: Quantum Mechanical Systems

Schedule, syllabus and examination date

Course content

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 diverse areas such 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.

Learning outcome

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

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.

Overlapping courses

Teaching

This course has 5 hours of teaching per week and consists of:

  • 2 hours of lectures
  • 3 hours of computer laboratory

Examination

  • Two large projects which are evaluated and graded. Each project counts 50% of the final grade. Final letter 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: FYS9411 – Computational Physics II: Quantum Mechanical Systems

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

This course offers both postponed and resit of examination. Read more:

Special examination arrangements, use of sources, explanations and appeals

See more about examinations at UiO

Last updated from FS (Common Student System) Dec. 8, 2022 2:15:00 PM

Facts about this course

Credits
10
Level
Master
Teaching
Spring

If the course is offered, a minimum of four students is required for ordinary lectures to take place. If less than four students participate, an exam will be given, but one should not expect ordinary teaching.

Examination
Spring
Teaching language
English