CE 532 | Course Introduction and Application Information - Ph.D. In Computer Engineering

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
	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	Understands and applies the foundational theories of Computer Engineering in a high level.		-	-	-	-	-
2	Possesses a great depth and breadth of knowledge about Computer Engineering including the latest developments.		-	-	-	-	-
3	Can reach the latest information in Computer Engineering and possesses a high level of proficiency in the methods and abilities necessary to comprehend it and conduct research with it.		-	-	-	-	-
4	Conducts a comprehensive study that introduces innovation to science and technology, develops a new scientific procedure or a technological product/process, or applies a known method in a new field.		-	-	-	-	-
5	Independently understands, designs, implements and concludes a unique research process in addition to managing it.		-	-	-	-	-
6	Contributes to science and technology literature by publishing the output of his/her academic studies in respectable academic outlets.		-	-	-	-	-
7	Interprets scientific, technological, social and cultural developments and relates them to the general public with a commitment to scientific objectivity and ethical responsibility.		-	-	-	-	-
8	Performs critical analysis, synthesis and evaluation of ideas and developments in Computer Engineering.		-	-	-	-	-
9	Performs verbal and written communications with professionals as well as broader scientific and social communities in Computer Engineering, by using English at least at the European Language Portfolio C1 General level, performs written, oral and visual communications and discussions in a high level.		-	-	-	-	-
10	Develops strategies, policies and plans about systems and topics that Computer Engineering uses, and interprets the outcomes.		-	-	-	-	-

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