Recommended prerequisite for participation in the module

The module builds on basic knowledge of linear algebra and probability theory. More advanced topics from the above topics will be introduced during the course.

Content, progress and pedagogy of the module

Quantum technology is among the most anticipated new technologies of the 21st century. In particular, large-scale quantum computers have the possibility of providing exponential speedups in various kinds of computations. During the last decade, small-scale quantum computers have emerged, with companies like IBM, Google and Microsoft as the frontrunners. The fundamental computational building block in quantum computing is the qubit. While millions of qubits are needed to outperform classical supercomputers, current state-of-the-art quantum computers only have hundreds of qubits available. However, with the rapid development in the field, we can expect this number to grow in the coming years and decades. 

In other words, a quantum computer is no longer a dream and far-fetched future; it will be an accessible computing tool for computer scientists and engineers in the coming years. Therefore, the course will introduce the participants to the main concepts of quantum computing.

The course will cover the subject of  postulates of quantum mechanics, quantum circuits, quantum algorithms and their complexity. Special attention will be paid to currently available "noisy" quantum computers consisting of a few qubits - so-called NISQ (noisy intermediate-scale quantum) computers.

The students will, through exercises, get hands-on experience with analyzing and implementing quantum algorithms on state-of-the-art quantum hardware.

Learning objectives

Knowledge

Students must have knowledge about:

• The postulates of quantum mechanics, including superposition, measurements and entanglement.
• State vector and density operator formalism for describing quantum states.
• Basic quantum gates and quantum circuits, including single-qubit gates such as Pauli and Hadamard gates, and multi-qubit gates such as CNOT, SWAP, Toffoli, etc.
• Quantum communication protocols, including quantum teleportation, superdense coding, quantum key distribution.
• Elements of complexity theory, including complexity classes for quantum computing.
• Basic quantum algorithms, including Deutsch-Jozsa algorithm, quantum Fourier transform, Shor’s factoring algorithm, Grover’s search algorithm.
• Different forms of quantum noise and basic quantum error correction codes.
• Noisy Intermediate-Scale Quantum (NISQ) devices and basic NISQ algorithms, including variational quantum algorithms and quantum neural networks.

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 NISQ 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 type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.

Extent and expected workload

It is expected that the student uses 30 hours per ECTS, which for this activity means 150 hours

Exam

Exams

Name of exam	Quantum Information and Computing
Type of exam	Written or oral exam
ECTS	5
Permitted aids	Can be found on the coursepage in MOODLE
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures