COMP7309A - Quantum computing and artificial intelligence

Semester 2, 2020-21

Instructor	Dr. Daniel Ebler
Teaching assistant	Mr. Fei Meng
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 their potential applications in AI.
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	Course Learning Outcomes Relevant Programme Learning Outcome CLO1. Understand the differences between classical and quantum computing. PLO1, PLO2, PLO4, PLO5, PLO6, PLO7, PLO8, PLO10 CLO2. Apply the concepts of quantum mechanics to understand basic quantum algorithms. PLO1, PLO2, PLO4, PLO5, PLO6, PLO7, PLO8, PLO10, PLO14 CLO3. Utilize quantum algorithms to make the transmission from classical AI to quantum AI. PLO1, PLO2, PLO4, PLO5, PLO6, PLO7, PLO8, PLO10, PLO14 View Programme 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	Course Content No. of Hours Course Learning Outcomes Introduction to the field 2 CLO1 Basics of quantum computation 5 CLO1, CLO2 Limitations and opportunities of quantum computation 2 CLO1, CLO2 Standard quantum algorithms 6 CLO1, CLO2, CLO3 Quantum neural networks 6 CLO1, CLO2, CLO3 Quantum classification and clustering 6 CLO1, CLO2, CLO3 Quantum causality 3 CLO1, CLO2, CLO3
Assessment	Description Type Weighting * Examination Period ^ Course Learning Outcomes 5 written assignments Continuous Assessment 50% - CLO1, CLO2, CLO3 2-hour midterm examination Continuous Assessment 10% - CLO1, CLO2, CLO3 3-hour final examination Written Examination 40% May 10 to May 29, 2021 CLO1, CLO2, CLO3 * The weighting of coursework and examination marks is subject to approval ^ The exact examination date uses to be released when all enrolments are confirmed after add/drop period by the Examinations Office.  Students must oblige to the examination schedule. Students should NOT enrol in the course if they are not certain that they will be in Hong Kong during the examination period.  Absent from examination may result in failure in the course. There is no supplementary examination for all MSc curriculums in the Faculty of Engineering.
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	Date Time Venue Remark Session 1 23 Jan 2021 (Sat) 12:30pm - 3:30pm KB-223 Hybrid Session 2 30 Jan 2021 (Sat) 12:30pm - 3:30pm KB-223 Hybrid Session 3 6 Feb 2021 (Sat) 12:30pm - 3:30pm KB-223 Hybrid Session 4 27 Feb 2021 (Sat) 12:30pm - 3:30pm CB-C Hybrid Session 5 6 Mar 2021 (Sat) 12:30pm - 3:30pm CB-C Hybrid Session 6 20 Mar 2021 (Sat) 12:30pm - 3:30pm CB-C Hybrid Session 7 27 Mar 2021 (Sat) 12:30pm - 3:30pm CB-C Hybrid Session 8 10 Apr 2021 (Sat) 12:30pm - 3:30pm CB-C Hybrid Session 9 17 Apr 2021 (Sat) 12:30pm - 3:30pm CB-C Hybrid Session 10 24 Apr 2021 (Sat) 12:30pm - 3:30pm CB-C Hybrid CB - Chow Yei Ching Building KB - Knowles Building
Add/drop	18 January, 2021 - 1 February, 2021
Quota	90   [For Engineering TPG students]