Practical Quantum Computing

The course provides a hands-on introduction to quantum computing, combining fundamental concepts of quantum information with practical implementation and numerical experimentation. Students learn the basic models of quantum computation, quantum gates and simple quantum algorithms, and explore their behavior using Python-based quantum simulators. The course emphasizes understanding how theoretical ideas translate into practical implementations and how the limitations of current quantum technologies affect computational results.

The course Practical Quantum Computing offers an introductory, practice-oriented approach to quantum computation, aimed at students with a basic background in quantum mechanics, linear algebra and programming. The main goal of the course is to introduce key concepts and methods of quantum information processing and to examine how they operate in practice through numerical simulations and simple implementations.

Each week consists of a short theoretical introduction, discussion of selected topics and algorithms, and practical exercises focused on implementation and analysis. Students work with Python and software packages for quantum computing introduced during the classes, in particular the Qiskit framework, using locally installed quantum simulators. Optional additional tasks allow interested students to analyze and discuss results obtained in batch mode from real quantum devices.

The theoretical content covers the basic models of quantum computation, quantum gates and circuits, entanglement and measurement, selected quantum algorithms, and the physical limitations of present-day quantum hardware, such as noise and decoherence. The conceptual level of the course does not exceed that of a standard quantum information course, while emphasizing the practical meaning of the introduced notions. A reference textbook for the subject is M. Nielsen and I. Chuang, Quantum Computation and Quantum Information.

Nielsen, M.A., & Chuang, I.L. (2010). Quantum Computation and Quantum Information. (Cambridge University Press)

After completing the course, the student has knowledge of the basic concepts of quantum computation and the physical principles underlying contemporary quantum computing platforms. The student understands the circuit and analog models of quantum computation, the role of quantum gates, entanglement and measurement, and is familiar with selected quantum algorithms discussed during the course. The student understands the practical limitations of current quantum hardware, including the impact of noise, decoherence and finite measurement statistics on computational results.

After completing the course, the student is able to implement simple quantum algorithms using locally installed quantum simulators and to analyze their behavior in numerical experiments. The student can test and verify the correctness of implemented quantum circuits, interpret the obtained results, and relate them to the underlying theoretical models. The student is able to critically assess the performance of quantum algorithms and, optionally, compare simulation results with data obtained from real quantum devices.

After completing the course, the student is aware of the responsibility associated with the correct interpretation of results obtained in quantum simulations and experiments. The student is able to plan and carry out computational tasks independently and to systematically solve assigned problems. The student understands the need for continuous self-education in the rapidly developing field of quantum computing and is prepared to further deepen their knowledge and practical skills.

The achievement of the learning outcomes is verified through continuous assessment conducted during classes, based on correctly completed computational tasks related to the implementation and analysis of quantum algorithms using numerical simulations. Practical skills are evaluated with regard to the correctness, completeness and interpretation of results obtained in assigned programming tasks carried out throughout the semester. The final assessment includes a written examination at the end of the course, which evaluates the understanding of theoretical concepts and algorithms discussed during the lectures. Successful completion of the course requires obtaining positive results from both the continuous assessment of computational work and the final written examination.