Quantum computer programming and quantum algorithms

Knowledge
- Understanding the quantum computational model - Students will understand the basic concepts of quantum computation, including quantum bits (qubits), superposition, entanglement, and measurement.
- Knowledge of Quantum Algorithms - Students will become familiar with the most important quantum algorithms, such as Grover's algorithm, Shore factorization, quantum Fourier transform, and QAOA, and understand their mathematical foundations and practical applications.
- Overview of Quantum Hardware and Simulators - Students will gain an understanding of current quantum computing platforms, including superconducting quantum processors (IBM Qiskit, Google Cirq), ion traps (IonQ, AQT), and photonic quantum systems (Quandela).
- Quantum Circuit Optimization - Students will understand methods for optimizing quantum circuits, including reducing circuit depth, minimizing the number of quantum gates and transpilers for various quantum processors.
- Hybrid Quantum-Classical Approaches - Students will understand the fundamental principles of hybrid algorithms, such as Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA), and their use in optimization problems.

Skills
- Practical Quantum Circuit Programming - Students will learn to create quantum circuits in Qiskit (IBM), Cirq (Google) and alternative platforms (IonQ, Quandela).
- Using Quantum Simulators and Real Quantum Processors - Students will be able to run quantum programs not only in simulated environments, but also on real quantum computers via the IBM Quantum Experience and Google Quantum AI cloud platforms.
- Quantum Algorithm Analysis and Implementation - Students will be able to implement quantum algorithms such as Grover's, Shor's, QFT and hybrid approaches (QAOA, VQE) and experimentally verify their performance.
- Quantum Circuit Optimization - Students will learn methods for reducing the complexity of quantum circuits, including compilation for specific quantum hardware.
- Design and Implementation of Quantum Computing Experiments - Students will learn to design, implement, and analyze quantum computing experiments that can be tested on real quantum systems.

Competencies
- Analytical Thinking - Students will learn to analyze quantum algorithms, their effectiveness and limits in real-world quantum computing settings.
- Critical evaluation of quantum experimental results - Students will gain the ability to critically evaluate quantum computations, debug errors in implementations, and validate results on different platforms.
- Applications of quantum algorithms in practice - Students will understand the potential applications of quantum computing in various fields such as optimization, cryptography, and artificial intelligence.
- Readiness for further advanced research - The course will prepare students for advanced study of quantum programming, quantum intelligence, quantum machine learning and optimization algorithms.

This course provides students with a basic education in quantum programming, enabling them to apply the knowledge they have gained in academic research and industrial practice.