Identifying Data 2023/24
Subject (*) Quantum Computing and Machine Learning Code 614551008
Study programme
Máster Universitario en Ciencia e Tecnoloxías de Información Cuántica
Descriptors Cycle Period Year Type Credits
Official Master's Degree 1st four-month period
 First Optional 3
Language
Spanish
Teaching method Face-to-face
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Enxeñaría de Computadores
Coordinador
Mosqueira Rey, Eduardo
 E-mail
eduardo.mosqueira@udc.es
Lecturers
Alvarez Estevez, Diego
Mosqueira Rey, Eduardo
 E-mail
diego.alvareze@udc.es
eduardo.mosqueira@udc.es
Web http://https://guiadocente.udc.es/guia_docent/index.php?centre=614&ensenyament=614551&assignatura=614551008&any_academic=20
General description Aprendizaxe Máquina e Computación Cuántica son materias relevantes e de crecente interese en investigación e desenvolvemento tecnolóxico na actualidade. Este tema ilustra como o Aprendizaxe Máquina pode ser optimizado utilizando técnicas de Computación Cuántica. Inclúese unha revisión exhaustiva de ambas as materias, para logo buscar sinerxias entre elas e atopar dominios de aplicación e procedementos que melloren o comportamento dos algoritmos actuais de Aprendizaxe Máquina e Computación Cuántica. Posteriormente, implementaranse e probaránse as aplicacións deseñadas, avaliando os seus resultados e contrastándoos cos métodos clásicos equivalentes, para comprobar o seu correcto funcionamento.

Study programme competencies
Code Study programme competences
A4 CON_04 Have knowledge of quantum computing, algorithms, circuits, their programming in different languages and accessible platforms.
A15 CON_15 Have knowledge of high-level aspects of quantum computing: learning quantum machines, quantum simulators, architectures, 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
Coñecer os distintos tipos de aprendizaxe automática AJ15
 BJ1
BJ3
BJ13
 CJ1
Comprender o funcionamento das redes neuronais artificiais AJ15
 BJ1
BJ3
BJ6
BJ8
BJ12
BJ13
BJ14
BJ16
 CJ1
CJ2
CJ3
CJ4
CJ7
CJ8
Ser capaz de deseñar modelos de aprendizaxe automática cuánticos con circuítos parametrizados e clasificación variacional AJ4
AJ15
 BJ1
BJ3
BJ6
BJ8
BJ12
BJ13
BJ14
BJ16
 CJ1
CJ2
CJ3
CJ4
CJ7
CJ8
Comprender o funcionamento das máquinas de vectores de soporte AJ15
 BJ1
BJ3
BJ6
BJ8
BJ12
BJ13
BJ14
BJ16
 CJ1
CJ2
CJ3
CJ4
CJ7
CJ8
Ser capaz de deseñar mapas cuánticos de características e kernels AJ4
AJ15
 BJ1
BJ3
BJ6
BJ8
BJ12
BJ13
BJ14
BJ16
 CJ1
CJ2
CJ3
CJ4
CJ7
CJ8

Contents
Topic Sub-topic
1. Introdución á aprendizaxe automática
2. Redes neuronais artificiais
3. Circuítos cuánticos parametrizados para a aprendizaxe automática
4. Máquinas de vectores de soporte e kernels
5. Análise de compoñentes principais

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Guest lecture / keynote speech A4 A15 B13 C1 C2 C8 10.5 15.75 26.25
Laboratory practice A4 A15 B1 B3 B6 B8 B12 B14 B16 C3 C4 C7 10.5 34.65 45.15
Objective test A4 A15 B1 B3 B8 C1 C2 C3 C8 2.6 0 2.6
 
Personalized attention 1 0 1
 
(*)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 Lectures explain the theoretical concepts using different digital resources.
Laboratory practice Laboratory activities are based on the knowledge that students are acquiring in lectures.
Objective test A test shall be administered to assess the theoretical and practical knowledge acquired by students

Personalized attention
Methodologies
Laboratory practice
Description
Personalized attention to students includes not only tutorials (either virtual or in-person) to discuss questions, but also the following actions:

- Monitor the work of laboratory practices proposed by the teacher.
- Evaluate of the results obtained in practice and seminars.
- Conduct personalized meetings to answer questions about the contents of the subject.

Assessment
Methodologies Competencies Description Qualification
Objective test A4 A15 B1 B3 B8 C1 C2 C3 C8 Test conducted at the end of the semester with theoretical and practical content. 40
Laboratory practice A4 A15 B1 B3 B6 B8 B12 B14 B16 C3 C4 C7 Practice exercises based on the knowledge acquired in the theoretical classes. 60
 
Assessment comments

Porcentaxes concretas de avaliación de cada parte. 

  • A avaliación da materia realizarase en dous partes: avaliación continua (prácticas) e proba obxectiva (parcial e/ou final).

Como se avalía o non presentado. 

  • A entrega dalgunha das actividades ou probas de avaliación continua supoñerá que o alumno optou por presentarse á materia. Por tanto, a partir dese momento, aínda non presentándose a proba obxectiva haberá consumido unha oportunidade. 

Cómo se avalía a segunda oportunidade. 

  • Na segunda oportunidade (xullo) conservaranse as notas da avaliación continua e/ou a proba obxectiva obtidas durante o cuadrimestre.

  • Se o alumno preséntase á segunda oportunidade na avaliación continua ou a proba obxectiva, a nota obtida na primeira oportunidade para esa parte anúlase, e a cualificación correspondente desa parte será a da segunda oportunidade.

  • A nota final da materia na segunda oportunidade calcularase co mesmo criterio que na primeira oportunidade. 

Plaxios 

  • A realización fraudulenta das probas ou actividades de avaliación, unha vez comprobada, implicará directamente a cualificación de suspenso "0" na materia na convocatoria correspondente, invalidando así calquera cualificación obtida en todas as actividades de avaliación de cara a convocatoria extraordinaria 

Sources of information
Basic Aurélien Géron (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Ed.. O'Reilly
François Chollet (2021). Deep Learning with Python, 2nd Ed.. Manning
Maria Schuld, Francesco Petruccione (2021). Machine Learning with Quantum Computers, 2nd Ed.. Springer
Qiskit (2023). Quantum machine learning. https://qiskit.org/learn/course/machine-learning-course/

Complementary Qiskit (2023). Qiskit documentation. https://qiskit.org/documentation/
Qiskit (2023). Qiskit Terra API Reference. https://qiskit.org/documentation/apidoc/terra.html


Recommendations
Subjects that it is recommended to have taken before
Introduction to Quantum Computing/614551004

Subjects that are recommended to be taken simultaneously
Quantum Computing Tools/614551006
Programming and Implementation of Quantum Algorithms/614551007

Subjects that continue the syllabus
Practical Applications of Quantum Computing/614551010

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.