Recommended prerequisite for participation in the module

Content, progress and pedagogy of the module

The course will cover the postulates of quantum mechanics, quantum circuits, quantum algorithms and their complexity. The course will also cover state-of-the-art quantum simulation and optimization tools, as well as quantum computers. The students will, through exercises, get hands-on experience with analyzing and implementing quantum algorithms.

Learning objectives

Knowledge

• The postulates of quantum mechanics, including superposition, measurements and entanglement. 

• State vector and density operator formalism for describing quantum states. 

• Basic quantum gates, including single-qubit gates such as Pauli (X, Y, Z) and Hadamard (H) gates, and multi-qubit gates such as CNOT, SWAP, Toffoli (CCNOT), etcetc., that form the building blocks of advanced quantum algorithms. 

• Quantum communication protocols, including quantum teleportation, superdense coding, and quantum key distribution. 

• Elements of complexity theory, including complexity classes for quantum computing. 

• Basic quantum algorithms, including Deutsch-Jozsa algorithm, fault-tolerant error correction, quantum Fourier transform, Shor’s factoring algorithm, Grover’s search algorithm. 

• Different forms of quantum noise and basic quantum error correction codes. 

• Quantum optimization algorithms (e.g., Quadratic unconstrained binary optimization (QUBO)), and quantum reinforcement learning 

Skills

• Must be able to analyze basic quantum circuits, quantum communication protocols and quantum algorithms. 

• Must be able to discuss noise in quantum computing and the need for error correction. 

• Must be able to formulate elementary problems within optimization and machine learning to be suitable for relevant quantum devices.

Competences

• Must have competences in understanding the strengths and weaknesses of quantum computing versus classical computing. 

• Must be able to implement and simulate basic quantum algorithms on classical computers. 

• Must be able to implement small-scale quantum algorithms on quantum hardware, e.g., through available cloud services. 

Type of instruction

The course will be taught through a combination of lectures, invited talks, demos of applications and exercises. 

Exam

Exams