De elektronische studiegids voor het academiejaar 2026 - 2027 is onder voorbehoud.





Introduction to algorithms for quantum communication and computing (4902)

Coordinating lecturer:Prof. dr. Petr SIYUSHEV 


Credits: 3,0
Study load hours: 81
Period: semester 1 (3sp)

Language of instruction: English

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes

Sequentiality
Advising sequentiality bound on the level of programme components
 
 
Advice

It is advised that this course is taken in tandem with specialisation courses '4908 Quantum sensors for cross-disciplinary fields', '4909 Advanced quantum effects in biology', '4910 Condensed matter physics'



Prerequisites

The student should have prior knowledge of the following general physics topics:
• Basic knowledge of general optics (geometric optics, wave optics)
• Basic knowledge of linear algebra (vector space, matrices, eigenvectors and eigenvalues, linear operators...)
• Basic knowledge of quantum mechanics (postulates of quantum mechanics, Schrödinger equation, angular momentum, spin, particle identity) and basic concepts from solid state physics



Content

The aim of this course is to provide knowledge on basic concepts of quantum communication and computation which will allow students to familiarize themselves with this topic. During the course students will gain insight into existing problems in classical cryptography and what quantum mechanics can offer for secure communication, students will learn where limits of classical computational power are and why quantum computing can outperform classical approaches. The students will also obtain practical skills on simulation of basic quantum circuits using Python package QISKIT.

The course covers the following topics:

  • Quantum entanglement, EPR paradox, GHZ experiment, Bell inequality, Quantum logic
  • Classical cryptography, Non-cloning theorem, Quantum key distribution
  • Unitary transformations, Logic gates, Single qubit gates, Multiqubit gates
  • Quantum supremacy, Deutsch's test, Grover's algorithm, Shor's algorithm
  • Problem of decoherence, Quantum errors
  • Qubits in physical systems, DiVincenzo criteria, Possible implementations of quantum computer
  • Introduction to Python package QISKIT

Learning goals of this course are:

  • The student can understand the concepts and working principles of quantum communication and quantum computation as well as their applications in quantum technology
  • The student can independently review recent literature and improve his/her understanding of novel quantum communication protocols and quantum computation algorithms. The student can use the scientific literature to study certain topics on his/her own and propose them to the team
  • The student can independently process and apply the provided basic knowledge and skills to simulate simple quantum circuits
  • The student can translate a practical experimental problem into the computational context and report the results in writing and orally


Compulsory course material
 

All slides, readers, papers and other supporting materials will be provided on Blackboard. 

 

Recommended reading
 

Title: Quantum Information
Author: Stephen Barnett
Edition: 1
Publisher: Oxford University Press
ISBN: 9780198527633
Extra info: Oxford Master Series in Physics

Title: Quantum Computation and Quantum Information
Author: Michael A. Nielsen, Isaac L. Chuang
Edition: 10th Anniversary Edition
Publisher: Cambridge University Press
ISBN: 9781107002173
Extra info: /

Title: An Introduction to Quantum Communications Networks
Author: Mohsen Razavi
Edition: /
Publisher: Morgan & Claypool Publisher
ISBN: 9781681746524, Online ISBN: 9781681746531
Extra info: Online: https://iopscience.iop.org/book/mono/978-1-6817-4653-1

Title: An Introduction to Quantum Computing
Author: Phillip Kaye, Raymond Laflamme, and Michele Mosca
Edition: 1
Publisher: Oxford University Press
ISBN: 9780198570493
Extra info: /

Title: Principles of Pulse Electron Paramagnetic Resonance
Author: Arthur Schweiger, Gunnar Jeschke
Edition: 1
Publisher: Oxford University Press
ISBN: 9780198506348
Extra info: /

 

Recommended course material
 

Laptop/desktop, Python 3.x, IDE Jupyter Notebook, Python libraries: NumPy, Matplotlib, Qiskit

 

Mandatory software
 

Own PC is required by a student. Required software: Python 3.X.XX and Qiskit package (It is free software, nothing needs to be purchased).



Organisational and teaching methods
Organisational methods  
Lecture  
Response lecture  
Small group session  
Teaching methods  
Discussion/debate  
Educational learning conversation  
Exercises  
Homework  
Presentation  


Evaluation

Semester 1 (3,00sp)

Evaluation method
Written evaluation during teaching period20 %
Transfer of partial marks within the academic yearYes, with condition
Conditions transfer of partial marks within the academic yearThe student obtains at least 10/20.
Open-book
Homework
Open questions
Oral evaluation during teaching period20 %
Transfer of partial marks within the academic yearYes, with condition
Conditions transfer of partial marks within the academic yearThe student obtains at least 10/20.
Presentation
Oral exam60 %
Transfer of partial marks within the academic yearYes, with condition
Conditions transfer of partial marks within the academic yearThe student obtains at least 10/20.
Open questions
Additional information

Regarding the improper use of genAI in this course, we refer to the guidelines of the study program and the lecturer on Blackboard.

In case of an exam contract, student will be asked to perform a task using Qiskit instead of the presentation and quizzes during the teaching period.


Second examination period

Evaluation second examination opportunity different from first examination opprt
No
Explanation (English)The written evaluation and presentation during the teaching period
cannot be retaken, but will be replaced with an alternative assignment.


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Master of Materiomics
  •  EC 
  • EC 3. The graduate of the Master of Materiomics programme has insight in how modelling or synthesis methods predict and affect functional properties and is able to design sustainable materials based on in-operando functionality making optimal use of the synergy between computational and experimental methods.

     
  •  DC 
  • DC3.8 The student has knowledge of computational concepts and methods. [learning pathway interdisciplinarity - identification: the student knows which phenomena are studied in the various disciplines and which methods and theories are used]

  •  EC 
  • EC 4. The graduate of the Master of Materiomics programme is able to autonomously consult, summarise and critically interpret international scientific literature, reference it correctly and use it to explore and identify new domains relevant to the field.

     
  •  DC 
  • DC4.1 The student is able to look up and select appropriate international scientific literature from a variety of disciplines related to materials-related problems or research questions.

     
  •  DC 
  • DC4.2 The student is able to correctly and completely reference to scientific literature.

     
  •  DC 
  • DC4.3 The student is able to critically interpret, evaluate, compare, and/or summarize relevant scientific literature related to materials-related problems or research questions.

     
  •  DC 
  • DC4.4 The student is able to use relevant scientific literature to solve materials-related problems and/or to identify and explore new areas relevant to the field.

  •  EC 
  • EC 5. The graduate of the Master of Materiomics programme can independently design and carry out scientific research: formulate a research question and hypothesis, select the appropriate methods and techniques, critically analyse and interpret the results, formulate conclusions, report scientifically and manage research data.

     
  •  DC 
  • DC5.3 The student is able to think critically about a (new) experimental or theoretical methodology to achieve the predefined research objective, select and/or develop valid methods and techniques, write them down and carry them out.

     
  •  DC 
  • DC5.4 The student knows and understands the methods required to process, analyze, and interpret data.

     
  •  DC 
  • DC5.5 The student can, within the possibilities and limitations of the given context or circumstances, accommodate and direct changes in the planning of a research process.

     
  •  DC 
  • DC5.6 The student is able to formulate appropriate conclusions, based on the data analysis and interpretation.

     
  •  DC 
  • DC5.7 The student is able to apply predetermined criteria to critically evaluate the quality of their own research and that of others.

     
  •  DC 
  • DC5.8 The student is able to formulate possible ideas for further research based on the conclusions of an investigation or assignment.

     
  •  DC 
  • DC5.10 The student is able to apply various scientific reporting methods e.g., project reporting, article, poster/oral presentation,....

     
  •  DC 
  • DC5.11 The student is able to manage a large (own) research project, consistently integrating various research components, from formulating the problem to reporting and critically discussing results.

  •  EC 
  • EC 6. The graduate of the Master of Materiomics programme is able to communicate in both written and spoken form and to take a well-argued position in a scientific discussion, going from a general to a specialist level, adapted to the target audience.

     
  •  DC 
  • DC6.1 The student is able to report orally and in writing in an adequate manner.

     
  •  DC 
  • DC6.2 The student is able to adapt to the purpose and target audience of the communication, i.e., can empathize with the target audience and make appropriate choices regarding language use and format.

     
  •  DC 
  • DC6.3 The student is able to take and defend a logically constructed position, based on relevant and scientifically supported arguments.

  •  EC 
  • EC 10. The graduate of the Master of Materiomics programme is able to autonomously acquire new knowledge and monitor, evaluate and adjust one’s learning process.

     
  •  DC 
  • DC10.1 The student can reflect on their own strengths and areas for improvement and use feedback to improve their own work and competences.

     
  •  DC 
  • DC10.3 The student is able to autonomously acquire, process, and critically interpret new information.

     
  •  DC 
  • DC10.6 The student is able to reflect critically on his/her own way of thinking/reasoning and that of fellow students about a specific (material) problem. On the basis of this, the student is able to improve his/her own reasoning and, if necessary, look for complementary views in function of a specific (material) problem. [learning pathway interdisciplinarity - reflection: the student considers different perspectives and is able to reflect critically on them]

 

Included in these programmesTolerance3
Y
2nd year Master of Materiomics traject opleidingsonderdelen Y
Exchange Programme materiomics Y



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.