Instructor |
Dr. Daniel Ebler
|
Teaching assistant |
Miss Singh Swati
|
Syllabus |
This course offers an introduction to the interdisciplinary fields of
quantum computation and quantum AI. The focus will lie on an accessible
introduction to the elementary concepts of quantum mechanics, followed by a
comparison between computer science and information science in the quantum
domain. The theoretical capability of quantum computers will be illustrated
by analyzing fundamental algorithms of quantum computation and its potential
applications in AI. Finally, the ethics of quantum computation and the
potential impact of quantum computers on society will be discussed. |
Introduction by Instructor |
Quantum technology has become one of the most prominent
interdisciplinary fields of research. Frequent media coverage features
buzzwords like “qubits”, “entanglement” and “quantum supremacy”. But
what is quantum computation and AI all about? This module will focus on:
1) a comprehensive introduction to the concepts of quantum mechanics and
quantum computation, and explanations how its different structure offers
possibilities beyond the scope of conventional computers; 2) detailed
analyses of most common quantum algorithms and their role in quantum AI;
3) understanding quantum supremacy: it will be discussed how concepts of
quantum mechanics, such as entanglement and coherence, allow to compute
in more powerful ways than the best classical algorithms today; 4)
fields of applications of quantum AI. Topics covered will include impact
of quantum mechanics on search algorithms, neural networks,
classification and clustering algorithms. Finally, causality in quantum
mechanics and its impact on computation is discussed. Important: this
course chooses a special approach to standard quantum information and
computation - no knowledge in quantum mechanics is required, and all
necessary concepts will be introduced and motivated from the basics of
computer science. |
Learning Outcomes |
|
Pre-requisites |
Elementary linear algebra (vectors and
matrices), complex numbers. It can be useful, but is not necessary, if
students attended the course COMP3316 Quantum Information and Computation
given by Prof. Giulio Chiribella. |
Compatibility |
Nil |
Topics covered |
|
Assessment |
|
Course materials |
Recommended readings (only parts of each
book/publication):
- Isaac Chuang, Michael Nielsen, Quantum Computation and
Quantum Information, 10th Anniversary Edition, Cambridge
University Press, 2011.
- Maria Schuld, Ilya Sinayskiy, Francesco Petruccione, An
introduction to quantum machine learning, 2014.
- Peter Wittek, Quantum Machine Learning: What Quantum
Computing Means To Data Mining, Elsevier Insights, 20
|
Session dates |
|
Add/drop |
20 January, 2020 - 8 March, 2020 |
Quota |
100 [For MSc(CompSc) students] |