Near-term quantum computing applications

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Physics

Synopsis:

The goal of the course is to provide a baseline knowledge and skills for implementing applications of presently available quantum computers. The course includes an overview of basic concepts and fundamental algorithms, and focusses on algorithms suitable for so called NISQ noisy intermediate-scale quantum computers. All class materials are provided on an on-line learning platform, containing interactive tutorials, video recordings, exercises and assessment. The course is suitable for bachelor and masters students from all years and familiarity with quantum mechanics or quantum information is recommended, however, not a necessary requirement. The classes are held entirely online to get the most out of the learning material and make it internationally accessible. Python quantum SDKs will be used during the course. Use of own computers is required.

The course is provided in cooperation with the University of Latvia, QURECA, QWorld and the European Insitute of Technology. To complete the course, students are required to complete the modules hosted on the e-learning platform and submit a final capstone project.

Requirements:

Knowledge of linear algebra and fundamentals of quantum mechanics.

Syllabus of lectures:

1.Historical context and future outlook of quantum computing and technology

2.Quantum bits, Bloch sphere, quantum circuit

3.DeutschJozsa algorithm, Grovers algorithm for unstructured search

4.Quantum Fourier Transform and applications: Shors algorithm for integer factorisation

5.Variational optimization algorithms: parametrised quantum circuits, quantum approximate optimization algo-rithm, variational quantum eigensolver

6.Overview of quantum computing platforms (superconducting, trapped ion, photonic), quantum SDKs, hybrid quantum computing

7.Quantum errors and noise models, error mitigation techniques (zero-noise extrapolation, probabilistic error amplifi-cation and cancellation), error correction (quantum error correction codes, fault-tolerant quantum computing)

8.Kernel-based quantum machine learning, quantum data and quantum features, quantum support vector machine

9.Quantum neural networks: optimizers, training, application for classification

10.Capstone project: on-line teamwork on a project related to course content. Suggested topics are posted during the semester.

Syllabus of tutorials:

Study Objective:

Credit for completing weekly tasks.

Study materials:

Key references:

[1] Link to e-learning platform available from https://people.fjfi.cvut.cz/gabriaur/02NISQ

Recommended references:

[2] Learn Quantum Computation using Qiskit: https://qiskit.org/textbook

[3] Cirq: An open source framework for programming quantum computers https://quantumai.google/cirq

[4] PennyLane Documentation: https://docs.pennylane.ai/

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: