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26E064KI - Quantum Informatics

Course specification
Course title Quantum Informatics
Acronym 26E064KI
Study programme Electrical Engineering and Computing
Module Computer Engineering and Informatics, Information and Communication Technologies - Audio and Video Technologies, Information and Communication Technologies - Internet and Mobile Communications, Physical Electronics - Biomedical and Nuclear Engineering
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition none
The goal Introducing students to the basic concepts of quantum informatics and its applications in computing and engineering.
The outcome Upon completing this course, students should have a deeper understanding of quantum computing and quantum communication processes and their importance in modern technologies.
Contents
URL to the subject page http://nobel.etf.bg.ac.rs/studiranje/kursevi/of4ki/
Contents of lectures The importance of quantum informatics. Qubits. Mathematical foundations of quantum computation. Basic quantum information protocols (Quantum teleportation, cryptography, and superdense coding). Quantum algorithms in computing (Deutsch's, Deutsch-Jozsa's, Simon's, Shor's, Grover's, etc.). State-of-the-art applications (quantum machine learning, optimization, quantum chemistry, etc.).
Contents of exercises Solving selected problems related to quantum computing approaches. Seminars on selected topics in quantum computing and quantum algorithms.
Literature
  1. M.A. Nielsen, I.L. Chuang, Quantum Computation and Quantum Information, Cambridge University Press, Cambridge, 2000. (Original title)
  2. M. Dugić, Fundamentals of Quantum Informatics and Quantum Computing, PMF, Kragujevac, 2009.
  3. M. Nakahara, T. Ohmi, Quantum Computing: From Linear Algebra to Physical Realizations, CPC Press, Taylor & Francis Group, 2008. (Original title)
  4. M. Ohya, I. Volovich, Mathematical Foundations of Quantum Information and Computation and Its Applications to Nano- and Bio-systems, Springer, Heidelberg, 2011. (Original title)
  5. M. Schuld, F. Petruccione, Machine Learning with Quantum Computers, Springer Nature Switzerland, Cham, 2021. (Original title)
Number of hours per week during the semester/trimester/year
Lectures Exercises OTC Study and Research Other classes
3 1 1
Methods of teaching 45 hours of lectures and seminars + 15 hours of practice + 15 hours of computer-aided emulations, seminars, demonstrations in other centers and laboratories, student seminars with presentations, and a pre-exam by the end of the semester. Approximately 75 hours of personal study and exercise (3 hours per week during the semester, and approximately 30 hours of preparation during exam term).
Knowledge score (maximum points 100)
Pre obligations Points Final exam Points
Activites during lectures Test paper 60
Practical lessons Oral examination
Projects
Colloquia
Seminars 40