ST459      Half Unit
Quantum Computation and Information

This information is for the 2025/26 session.

Course Convenor

Prof Kostas Kardaras

Availability

This course is available on the MSc in Data Science, MSc in Financial Mathematics, MSc in Mathematics and Computation, MSc in Operations Research & Analytics, MSc in Quantitative Methods for Risk Management, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit. This course uses controlled access as part of the course selection process.

How to apply: Priority will be given to students from the Departments of Statistics and those with the course listed in their programme regulations.

Students from any other programmes should submit a short statement indicating a) why they think the course is suitable for them given their background knowledge and b) their motivation for their choice.

Deadline for application: Due to the nature of the method of application, interested students should apply as soon as possible after the opening selection and no later than 10.00am on Friday 26 September 2025.

Course lecturers will aim to make initial offers to students on LSE For You by Friday 26 September.

For queries contact: Stats-Msc@lse.ac.uk

Requisites

Additional requisites:

Advanced knowledge of linear algebra, as well as basics of complex numbers, at the level of MA222, are essential. Familiarity with Python is also required.

Students who have no previous experience in Python are required to complete an online pre-sessional Python course from the Digital Skills Lab before the start of the course (https://moodle.lse.ac.uk/course/info.php?id=8709).

Course content

The course will start with reminders on linear algebra and complex numbers. Then, foundational principles of quantum mechanics necessary for understanding quantum computation will be established: postulates of quantum mechanics, quantum superposition, and the measurement problem. The concept of qubits and their representation will be studied, as well as basic quantum gates, such as the Pauli and Hadamard gates. Subsequently, the course will move to discussing quantum algorithms, focusing on foundational ones such as Deutsch-Jozsa, and Grover's search algorithm. In terms of quantum information theory, concepts such as quantum entanglement and quantum teleportation will be discussed, as well as the no-cloning theorem. If time permits, quantum error correction will be covered.

For all the previous, hands-on exercises and demonstrations using Python through the quantum programming package Qiskit will enable students to gain practical experience in implementing and simulating quantum algorithms.

Teaching

30 hours of seminars in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

There will be a mixture of theory and examples/applications/homework solutions (roughly split in half between theory and practice). The third of the three weekly hours will be mostly used for working out problems from the formative coursework, and illustrations through computer code. A mid-term summative assessment will be provided, to be worked out by students during the reading week.

Formative assessment

Students will be expected to produce 9 problem sets in the WT.

Weekly problem sets will be provided, and will be discussed during the seminars.

 

Indicative reading

The main material will come from the first two sources:

- Quantum Computation and Quantum Information. M. A. Nielsen and I. L. Chuang, Cambridge University Press; 2010

- Lecture Notes on Quantum Computation and Information. A. Jacquier and K. Kardaras; 2024

- Mathematics of Quantum Computing: An Introduction. W. Scherer, Springer; 2020. 

- Classical and Quantum Computation. A. Yu. Kitaev, A. H. Shen, M. N. Vyalyi, American Mathematical Society; 2002

Assessment

Exam (80%), duration: 120 Minutes, reading time: 15 minutes in the Spring exam period

Problem sets (20%)

Coursework accounting for 20% of the grade will be given to be worked on during the reading week. This will consist of a number of exercises students will be required to solve throughout the term. 


Key facts

Department: Statistics

Course Study Period: Winter Term

Unit value: Half unit

FHEQ Level: Level 7

CEFR Level: Null

Total students 2024/25: 8

Average class size 2024/25: 8

Controlled access 2024/25: No
Guidelines for interpreting course guide information

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Personal development skills

  • Self-management
  • Team working
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Commercial awareness
  • Specialist skills