# Course Identification

## Lecturers and Teaching Assistants

## Course Schedule and Location

**Tutorials**

Tuesday, 13:15 - 14:00, Jacob Ziskind Building, Rm 155

## Field of Study, Course Type and Credit Points

Chemical Sciences: Lecture; Elective; 2.00 points

## Comments

## Prerequisites

1. Basic (undergraduate level) classical complexity theory: the boolean circuit model, probabilistic computation, analysis of algorithms, oracle machines.

2. Basic (undergraduate level) linear algebra: vectors, matrices, eigenvalues, unitary transformations, norms.

3. Basic (undergraduate level) probability theory.

4. Very basic algebra: familiarity with group terminology.

No background in physics or quantum mechanics is needed.

## Restrictions

## Language of Instruction

## Attendance and participation

## Grade Type

## Grade Breakdown (in %)

## Evaluation Type

**No final exam or assignment**

## Scheduled date 1

## Estimated Weekly Independent Workload (in hours)

## Syllabus

This is a basic class in quantum computing, covering basic definitions and algorithms.

1. The quantum model: superposition, measurement, density matrices.

2. Quantum circuits and quantum gates.

3. Effects of quantum entanglement: teleportation, superdense coding, the CHSH game.

4. Quantum Fourier Transform and quantum algorithms.

5. Foundations of quantum complexity theory.

6. Other topics as time permits.

## Learning Outcomes

Upon successful completion of the course the students will be able to:

* Understand of the quantum computational model.

* Demonstrate familiarity with quantum algorithms.

## Reading List

We will not follow a particular textbook, but for a very good reference on quantum computing and quantum information, it is always good to refer to "Quantum Computation and Quantum information" by Nielsen and Chuang.