Study programme competencies |
Code
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Study programme competences / results
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A4 |
CON_04 Have knowledge of quantum computing, algorithms, circuits, their programming in different languages and accessible platforms. |
A14 |
CON_14 Be aware of problem sets where quantum computing at its current stage of development can offer an advantage over classical computing: chemistry, biology, optimization, logistics, finance, etc. |
B1 |
HD01 Analyze and break down a complex concept, examine each part and see how they fit together |
B3 |
HD03 Compare and contrast and point out similarities and differences between two or more topics or concepts |
B6 |
HD11 Prepare accurately the relevant questions for a specific problem. |
B8 |
HD13 Improvise solutions in an innovative way to solve a problem. |
B12 |
HD23 Communicate using the expected norms for the chosen medium. |
B13 |
HD24 Actively participate in face-to-face activities in the classroom. |
B14 |
HD31 Assign resources and responsibilities so that all members of a team can work optimally |
B16 |
HD33 Set goals for the group to analyze the situation, decide what outcome is desired and clearly set an achievable goal. |
C1 |
C1. Adequate oral and written expression in the official languages. |
C2 |
C2. Mastering oral and written expression in a foreign language. |
C3 |
C3. Using ICT in working contexts and lifelong learning. |
C4 |
C4. Acting as a respectful citizen according to democratic cultures and human rights and with a gender perspective. |
C7 |
C7. Developing the ability to work in interdisciplinary or transdisciplinary teams in order to offer proposals that can contribute to a sustainable environmental, economic, political and social development. |
C8 |
C8. Valuing the importance of research, innovation and technological development for the socioeconomic and cultural progress of society. |
Learning aims |
Learning outcomes |
Study programme competences / results |
Know the state of the art of the use of quantum computing to develop numerical methods |
AJ4 AJ14
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BJ1 BJ3 BJ6 BJ8 BJ12 BJ13 BJ14 BJ16
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CJ1 CJ2 CJ3 CJ4 CJ7 CJ8
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Know the quantum algorithms related to functions of a variable, matrix numerical calculation, numerical methods of optimization and numerical and stochastic simulation |
AJ4 AJ14
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BJ1 BJ3 BJ6 BJ8 BJ12 BJ13 BJ14 BJ16
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CJ1 CJ2 CJ3 CJ4 CJ7 CJ8
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Know how to implement numerical methods in quantum computer simulators |
AJ4 AJ14
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BJ1 BJ3 BJ6 BJ8 BJ12 BJ13 BJ14 BJ16
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CJ1 CJ2 CJ3 CJ4 CJ7 CJ8
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Contents |
Topic |
Sub-topic |
1. Introduction to Numerical Methods in Quantum Computing |
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2. Quantum numerical methods on functions of one variable |
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3. Quantum algorithms for matrix numerical computation |
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4. Quantum algorithms of numerical optimization methods |
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5. Quantum algorithms for numerical and stochastic simulation |
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Planning |
Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
Guest lecture / keynote speech |
A4 A14 B1 B3 B6 B8 B12 B13 B14 B16 C1 C2 C3 C4 C7 C8 |
11 |
0 |
11 |
ICT practicals |
A4 A14 B1 B3 B6 B8 B12 B13 B14 B16 C1 C2 C3 C4 C7 C8 |
4 |
10 |
14 |
Case study |
A4 A14 B1 B3 B6 B8 B12 B13 B14 B16 C1 C2 C3 C4 C7 C8 |
2 |
8 |
10 |
Problem solving |
A4 A14 B1 B3 B6 B8 B12 B14 B16 C1 C2 C3 C4 C7 C8 |
4 |
10 |
14 |
Supervised projects |
A4 A14 B1 B3 B6 B8 B12 B14 B16 C1 C2 C3 C4 C7 C8 |
0 |
20 |
20 |
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Personalized attention |
|
6 |
0 |
6 |
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(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Guest lecture / keynote speech |
Presentation in the classroom of the contents of the subject |
ICT practicals |
Programming and use of simulators to solve examples |
Case study |
Presentation of use cases that propose quantum algorithms for different numerical methods |
Problem solving |
The student is given problems to solve individually or in a group |
Supervised projects |
Students are given assignments to prepare individually or in groups, which are monitored with personalized attention when necessary |
Personalized attention |
Methodologies
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Supervised projects |
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Description |
Supervised work is monitored, giving guidance and recommendations for its development |
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Assessment |
Methodologies
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Competencies / Results |
Description
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Qualification
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Problem solving |
A4 A14 B1 B3 B6 B8 B12 B14 B16 C1 C2 C3 C4 C7 C8 |
Problems of greater or lesser complexity are posed to be carried out individually or in groups, which may involve handling simulators. The student will deliver a document with his resolution |
50 |
Supervised projects |
A4 A14 B1 B3 B6 B8 B12 B14 B16 C1 C2 C3 C4 C7 C8 |
Supervised work is proposed to be carried out individually or in a group, depending on the complexity. The student must deliver a brief report on the work done and make a brief oral presentation about it, answering the teacher's questions |
50 |
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Assessment comments |
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Sources of information |
Basic
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Gómez, A., Leitao Rodriguez, A., Manzano, A., Nogueiras, M., Ordoñez, G., Vázquez, C. (2022). A survey on quantum computational finance for derivatives pricing and VaR. Archives of Computational Methods in Engineering, 29, 4137–4163.
Hadfield, S.A. (2018). Quantum algorithms for scientific computing and approximmate optimization. PhD Thesis, Columbia University
García-Ripoll, J.J. (2021). Quantum-inspired algorithms for multivariate analysis: from interpolation to partial differential equations. Quantum 5, 431 |
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Complementary
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Recommendations |
Subjects that it is recommended to have taken before |
Quantum Computing Tools/614551006 | Quantum Computing Architectures/614551022 | Programming and Implementation of Quantum Algorithms/614551007 | Quantum Computing and High Performance Computing/614551009 | Introduction to Quantum Computing/614551004 |
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Subjects that are recommended to be taken simultaneously |
Quantum Computing and Machine Learning/614551008 | Rule-Based Quantum Systems/614551029 |
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Subjects that continue the syllabus |
Master`s Dissertation/614551033 | Practical Applications of Quantum Computing/614551010 |
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