GRADUATE SCHOOL
Ph.D. In Computer Engineering
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
|
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| Course Language |
English
|
|||||
| Course Type |
Elective
|
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| 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 | ||
| 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 | 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. | |||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest