
At the end of the course the student is able to:
 explain where quantum and classical computing differ from each other
 program quantum circuits on gated quantum computers
 program hybrid classicalquantum algorithms
 optimize using quantum annealing
 implementing the most widely known quantum algorithms (DeutschJozsa, Grover, Simon, Shor)
 asses if and when a quantum approach is suitable for a given problem
 elaborate on the opportunities and threats of quantum computing and quantum technologies
 direct (or design or develop) a quantum approach for common problems encountered in machine learning, optimization, or partial differential equations
 articulate on speedups of quantum algorithms w.r.t. their classical counterparts
 discuss the limitations of quantum hardware


Since the discovery of the transistor chipmakers increased the computational power in computers mostly by reducing the size of the transistors. The transistor size is reduced in such magnitude that quantum effects may occur and therefore inhibit computation as is desired. Then it also turns out that actually making use of quantum mechanics to do computation gives (tremendous) speedups and, perhaps, enables computations that would be impractical for classical computers.
During this course, an introduction to quantum mechanical principles will be given by a mathematical approach (linear algebra and graphs) instead of explaining by physics to make it more familiar to computer science students. These quantum mechanical principles are used for building the first quantum circuits as if a student learned how logical gates are applied in classical computing. Based on this knowledge the prime algorithms that sparked the field of quantum computing are addressed. The course is then extended with various applications of quantum algorithms, for example optimization and machine learning. Finally, a critical look is given at existing quantum algorithms.
The course is intended to give students more than sufficient information to start developing and assessing quantum projects. Therefore, the examination of the course is done both by theory and by practice. Graded homework sets are given throughout the course where theoretical questions are asked along with programming questions (making use of Jupyter Notebook). A final project is done in groups where realworld applications are assessed or developed by the groups. Students get to pick one of the projects or may come up with their own proposed project. A presentation, demonstration and a report are handed in and accounts to the final grade along with the graded homework sets. Projects from last year were quantum machine learning, quantum optimization, quantum simulation, quantum monte carlo simulations, postquantum cryptography, quantum key distributions and how to leverage quantum projects in a socialbusiness perspective.
Time and schedule
A student will invest probably:
 20 hours on all lectures
 30 hours on all nongraded exercises
 40 hours on graded homework sets
 40 hours on the project
From 29th of April until 24th of June: every Friday from 13:4515:30 with the exception of 28th of May.
Lectures
Lectures are both prerecorded and are given live online. The recorded lectures can be found on Canvas on the day of the live lecture. The live lecture covers the same topics as the prerecorded one, but more slowly and a bit more detailed. There is also room for interactive discussions during the live lecture.
Depending on the appetite of the students and of the teacher, one or two lectures might be given on location, or perhaps the presentations and demonstrations of the projects.




 VoorkennisBasic Python programming skills Familiarity with Numpy Using Jupyter Notebooks Desired: Linear Algebra (if not present, an elective lecture is provided) 
  Verplicht materiaalCanvasCourse materials are provided on Canvas. These are slides, papers, and Jupyter notebooks (with tutorials). Also, project descriptions and assignments (graded and nongraded) are put on Canvas. 

 Aanbevolen materiaalWerkvormenHoorcollege
 OpdrachtAanwezigheidsplicht   Ja 
 Practicum
 Presentatie(s)Aanwezigheidsplicht   Ja 
 Zelfstudie geen begeleiding
 Zelfstudie met begeleiding

 ToetsenExam


 