Advanced Quantum Algorithms

6 EC

Semester 2, period 4

5334ADQA6Y

Owner Master Quantum Computer Science
Coordinator prof. dr. Stacey Jeffery
Part of Master Mathematics, specialization Algebra and Geometry, year 1Master Mathematics, specialization Analysis and Dynamical Systems, year 1Master Mathematics, specialization Stochastics , year 1Master Mathematics, specialisation Discrete Mathematics and Quantum Information, year 1Master Quantum Computer Science,

Course manual 2024/2025

Course content

A major claim to fame of quantum computers is that they will be able to solve some problems significantly faster than believed to be possible on a classical computer. The design of further quantum algorithms is an exciting active field of research, and there is still much left to explore in understanding which problems can be sped up by quantum computers, and by how much. In this course, students will learn about various advanced topics in quantum algorithms and algorithm design, as well as techniques for understanding for which problems it is possible to get a quantum speedup, and by how much. Students will be encouraged to dive deep into topics, and probe the cutting edge of current research. 

Study materials

Other

  • Lecture notes, assignments, links to papers for further reading

Objectives

  • Describe the breadth of cutting edge topics in quantum algorithms.
  • Understand one or more selected topics in quantum algorithms with sufficient depth to carry out research on this topic.
  • Formulate and prove formal statements about the correctness and complexity of quantum algorithms.
  • Clearly write and present mathematical proofs.
  • Design quantum algorithms using state-of-the-art techniques.

Teaching methods

  • Lecture
  • Self-study
  • Working independently on e.g. a project or thesis
  • Tutorials

Learning activities

Activity

Hours

  

Lecture

28

  

Tutorials

16

  

Self-study

121

  

Exam

3

 

Total

168

(6 EC x 28 hours)

Attendance

Additional requirements for this course:

Attendance is not mandatory but is highly encouraged.

Assessment

Item and weight Details

Final grade

0.6 (60%)

Tentamen

0.4 (40%)

Assignments

1 (17%)

Assignment 1

1 (17%)

Assignment 2

1 (17%)

Assignment 3

1 (17%)

Assignment 4

1 (17%)

Assignment 5

1 (17%)

Assignment 6
  • Assignments: 40% (best 4 out of 6)
  • Final Exam: 60%

Assignments will not be accepted after the deadline, however, the lowest two assignment grades will be dropped. There will be a resit exam, which will be worth 100% of the final grade (assignments will be dropped).

Inspection of assessed work

Assignment grades and feedback will be available in Canvas.
Students can review their exam with the coordinator. The date will be set after the exam. 

Assignments

Assignments may be discussed among students, but should be completed individually. Assignments will be graded. There are additional exercises in the lecture notes that are not part of assignments, and students are encouraged to work through these as well.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

week 1: Basics, hidden subgroup problem
week 2: Phase estimation
assignment 1 due
week 3: Quantum walks
assignment 2 due
week 4: Span programs
assignment 3 due
week 5: Quantum query complexity
assignment 4 due
week 6: Quantum singular value transform
assignment 5 due
week 7: QMA-completeness of k-local Hamiltonian, optional selected topic
assignment 6 due
week 8: exam review and final exam
 
The above timeline is a rough guide, and may be subject to change based on the actual pace of lectures and class discussions.
 
The schedule for this course is published on rooster.uva.nl

Contact information

Coordinator

  • prof. dr. Stacey Jeffery