CE 532 | Course Introduction and Application Information - M.SC. in Computer Engineering (With Thesis)

Course Name

Applied Quantum Machine Learning

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 532	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	Dr. Öğr. Üyesi Kutluhan Erol
Course Lecturer(s)	Dr. Öğr. Üyesi Kutluhan Erol
Assistant(s)	-

Course Objectives

The field of machine learning is concerned with the question of how to construct computer programs that improve automatically with experience. The goal of this class is to introduce quantum computational models, quantum computational platforms, and programming on those platforms. It teaches the students how to design and implement machine-learning algorithms on these platforms in order to solve business problems.

Learning Outcomes

The students who succeeded in this course;

Will be able to Able to demonstrate Quantum Computing using Qubits platform
Will be able to apply Artificial Intelligence, Machine Learning, Deep Learning techniques to solve business problems
Will be able to design a Quantum Algorithm for Solving Linear Equations
Will be able to code quantum Fourier transform in Machine Learning Programming
Will be able to discuss Big Data and Quantum Mechanics
Will be able to classify Quantum neural networks

Course Description

This course will describe the advantages of quantum computation in order to improve efficiency of classical machine learning methods, and show how to analyze quantum systems using classical machine learning methods.

Course Category	Core Courses
	Major Area Courses	X
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation	Learning Outcome
1	Quantum Computers - Physical differences with Legacy CPU’s - Quantum Information Theory	https://www.kdnuggets.com/2018/01/quantum-machine-learning-overview.html
2	Artificial Intelligence, Machine Learning	https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html
3	Big Data and Quantum Mechanic	http://www.vip.gatech.edu/teams/big-data-and-quantum-mechanics https://ieeexplore.ieee.org/document/7876324/
4	Quantum Computation Models	https://arxiv.org/abs/1012.6035 http://tph.tuwien.ac.at/~oemer/doc/quprog/node9.html
5	Quantum-Like Learning on Classical Computers	Quantum Machine Learning Section 12 https://www.wired.com/2015/12/for-google-quantum-computing-is-like-learning-to-fly/
6	Quantum versions of ML algorithms for eigenvalues	https://www.fanaticalfuturist.com/2018/05/new-quantum-ml-algorithm-could-revolutionise-quantum-ai-before-it-even-begins/ https://scottaaronson.com/papers/qml.pdf http://www.qutisgroup.com/wp-content/uploads/2014/10/TFG-Cristian-Romero.pdf https://www.scottaaronson.com/papers/qml.pdf
7	Quantum Computational Intelligence	https://uwaterloo.ca/institute-for-quantum-computing/blog/post/quantum-computational-intelligence
8	Quantum Computers Service Providers; IBM, Microsoft, D-Wave	https://qiskit.org/ https://www.research.ibm.com/ibm-q/learn/quantum-computing-applications/ https://www.dwavesys.com/tags/quantum-programming
9	Introduction to Q#	https://docs.microsoft.com/en-us/quantum/quantum-qr-intro?view=qsharp-preview
10	Binary classification of qubit states	https://arxiv.org/abs/1704.01965 https://www.researchgate.net/figure/Quantum-learning-for-classification-of-qubits_fig2_232227181
11	Quantum algorithm to solve linear systems	http://www2.lns.mit.edu/~avinatan/research/matrix.pdf
12	Quantum Application for Solving Linear Equations	https://phys.org/news/2017-06-linear-equations-quantum-mechanics.html https://www.perimeterinstitute.ca/videos/quantum-algorithm-solving-linear-systems-equations
13	Classical ML to analyze quantum systems	https://qutech.nl/wp-content/uploads/2018/01/QIP18MLtutorial_Ronald-de-Wolf.pdf https://en.wikipedia.org/wiki/Quantum_algorithm_for_linear_systems_of_equations
14	Future possibilities of Quantum Analytics	https://tdwi.org/articles/2016/08/12/is-quantum-the-future-of-high-performance-analytics.aspx https://www.ibm.com/thought-leadership/technology-market-research/quantum-computing-report.html http://analytics-magazine.org/improving-the-future-with-prescriptive-analytics-quantum-computers/ https://www.ft.com/content/6711e5c2-0e83-11e7-b030-768954394623
15	Review of the semester
16	Final Exam

Course Notes/Textbooks

Quantum Machine Learning 1st Edition

What Quantum Computing Means to Data Mining

Authors: Peter Wittek

eBook ISBN: 9780128010990

Hardcover ISBN: 9780128009536

Paperback ISBN: 9780128100400

Suggested Readings/Materials

Semester Activities	Number	Weigthing
Participation	1	10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project	1	50
Seminar / Workshop
Oral Exams
Midterm
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	2	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	2	32
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16	1	16
Study Hours Out of Class	16	6	96
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project	1	30	30
Seminar / Workshop			0
Oral Exam			0
Midterms			0
Final Exam	1	30	30
		Total	204

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering; evaluates, interprets and applies information.		-	-	-	X	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	-	X	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines.		-	-	-	-	X
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	X	-
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs		-	-	-	-	X
7	Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process.		-	-	-	-	X
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility.		-	-	X	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	X	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	-	X	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	X	-	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	-	-	-	X

CE 532 | Course Introduction and Application Information

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

EVALUATION SYSTEM

ECTS / WORKLOAD TABLE

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

NEW GÜZELBAHÇE CAMPUS

GLOBAL CAREER

CONTRIBUTION TO SCIENCE

VALUING PEOPLE

BENEFIT TO SOCIETY