Courses

Courses on quantum computing at the University of Tartu

Introduction to Quantum Computing: Theory and Practice

  • Institute of Physics: LTFY.04.012
  • 6 ECTS
  • Fall semester
  • Physics & math students: 1st or 2nd year of bachelor’s studies; CS/Engineering: take it whenever you want.
  • Content:
    • Superposition, qubits, gates, measurement
    • The math that’s needed
    • Lab sessions with cloud quantum computer devices (Qiskit)

Introduction to Quantum Algorithms

  • LTAT.04.008
  • 6 ECTS
  • Spring of 1st year master’s studies
  • Content: Basic quantum algorithms
    • Basics of quantum mechanics of closed systems
    • Quantum Fourier transform, Fourier sampling and applications (Shor’s algorithm)
    • Quantum Phase Estimation
    • Quantum Amplitude Amplification (Grover’s algorithm) and Quantum Amplitude Estimation
  • Details: Introduction to Quantum Algorithms

Quantum Error Correction & Fault-Tolerant Quantum Computing

Quantum Seminar

  • Course code: LTAT.04.004
  • 3 ECTS (4×)
  • Every semester
  • Content: Students read and present research papers on quantum computing and quantum cryptography
  • Companies with research in quantum computing are welcome to propose topics
  • Details: Quantum Seminar

Practical Training

In the following courses, students realize practical projects.
  • For Physics-Chemistry-Materials BSc students:
    – TFY.00.001 Practical Speciality Training (6 ECTS)
    – LOKT.00.024 Professional Practice (3 ECTS)
  • For Physics-Chemistry-Materials MSc students:
    – LOFY.00.007 Practical Experiences in Physics (12 ECTS)
  • For CS MSc students:
    – LTAT.00.008 3+3 ECTS
    – LTAT.04.005 12 ETCS

Companies with activities in quantum computing are welcome to propose topics.

Quantum Computer Software specialization

Master’s students in computer science can study Quantum Computer Software as part of the specialization module “Theoretical Informatics”. Here’s an example study plan:

The 3+3 ECTS project courses have the role of teaching mastery of software tools (e.g., Qiskit beyond the basic use). Students learn independently by implementing something from the papers they read in the Q-Seminar.

There’s still some work left to make this perfect. Most importantly: