GRADUATE SCHOOL
M.SC. in Computer Engineering (With Thesis)
CE 532 | Course Introduction and Application Information
| 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
|
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| 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;
|
| 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. |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 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 |
|
EVALUATION SYSTEM
| 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 |
ECTS / WORKLOAD TABLE
| 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
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
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 | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest