COMP7309A - Quantum computing and artificial intelligence

Semester 1, 2023-24

Professor	Daniel Ebler
Teaching assistant	Lorenzo Giannelli
Syllabus	This course offers a theoretical overview of selected topics from the interdisciplinary fields of quantum computation and quantum AI.  The scope of the lectures encompasses an accessible introduction to the fundamental concepts of quantum computation.  Importantly, the introduction does not require preliminary knowledge of quantum theory.  Detailed comparisons of computational principles and related phenomena in the classical and quantum domain outline the stark potential and challenges of quantum theory for fundamentally novel algorithms with enhanced processing power.  Thereupon, the theoretical capability of quantum computers is illustrated by analyzing a selection of milestone algorithms of quantum computation, and their potential applications to artificial intelligence.
Introduction by Professor	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: topics covered will include impact of quantum mechanics on search algorithms, neural networks, classification and clustering algorithms, as well as quantum approaches towards solving optimization problems. 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 and linear algebra.
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	Students need to be proficient in elementary linear algebra (calculations with vectors and matrices).  It is useful (but not necessary) if the students know 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 4 CLO1, CLO2, CLO3 Quantum optimization 2 CLO1, CLO2, CLO3 Quantum causality 3 CLO1, CLO2, CLO3
Assessment	Description Type Weighting * Examination Period ^ Course Learning Outcomes 5 written assignments Continuous Assessment 40% - CLO1, CLO2, CLO3 2-hour midterm examination Continuous Assessment 10% - CLO1, CLO2, CLO3 3-hour final examination Written Examination 50% 8 - 23 December 2023 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 are obliged to follow 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, 2014
Session dates	Date Time Venue Remark Session 1 (Cancelled) 2 Sep 2023 (Sat) 1:00pm - 4:00pm CB-C   Session 1 9 Sep 2023 (Sat) 1:00pm - 4:00pm CB-C   Session 2 16 Sep 2023 (Sat) 1:00pm - 4:00pm CYP-P3   Session 3 23 Sep 2023 (Sat) 1:00pm - 4:00pm CB-C   Session 4 7 Oct 2023 (Sat) 1:00pm - 4:00pm CB-C   Session 5 14 Oct 2023 (Sat) 1:00pm - 4:00pm CB-C   Session 6 28 Oct 2023 (Sat) 1:00pm - 4:00pm TT-404   Session 7 4 Nov 2023 (Sat) 1:00pm - 4:00pm CB-C   Session 8 11 Nov 2023 (Sat) 1:00pm - 4:00pm CB-C   Session 9 18 Nov 2023 (Sat) 1:00pm - 4:00pm CB-C   Session 10 25 Nov 2023 (Sat) 1:00pm - 4:00pm CB-C   CB - Chow Yei Ching Building CYP - Chong Yuet Ming Building TT - T.T. Tsui Building
Add/drop	1 September, 2023 - 18 September, 2023
Maximum class size	105