Quantum Data Science 2026

Learning Outcomes

On successful completion of the course students should be able

To master the quantum computational model, understanding basic concepts of quantum information and quantum computation.
To design and analyse quantum algorithms.
To understand some techniques in quantum machine learning.

Motivation: Quantum effects as computational resources.
Part 1: Quantum information and computation.
- From bits to qubits: Single-state quantum systems.
- Superposition and interference.
- Hilbert spaces and the quantum state postulates: representation, composition, evolution, and measurement.
- Entanglement.
Part 2: Quantum Algorithms.
- Quantum gates and the circuit model.
- Query algorithms.
- Algorithms based on phase amplification.
- Algorithms based on the quantum Fourier transform.
- Quantum programming in PennyLane.
Part 3: Introduction to variational algorithms and quantum machine learning.

Sep 14 (09:00 - 11:00)	Introduction to quantum computation and information (slides). Contents and dynamics of this course.
Sep 21 (09:00 - 11:00)	What's in a qubit? Representation and evolution of a single-state quantum system. (slides).
Oct 8 (16:00 - 18:00)	Multi-state quantum systems (slides). Introduction to quantum algorithms (slides)
Oct 19 (09:00 - 11:00)	Quantum algorithmics: The phase kick-back technique (slides).
Oct 19 (11:00 - 13:00)	Examples: The algorithms of Varziani-Bernstein and Deutch-Joza.
Oct 26 (09:00 - 11:00)	Unstructured search problems and the Grover's algorithm (slides).
Nov 2 (09:00 - 11:00)	Exercises on phase kick-back algorithms (conclusion).
Nov 9 (09:00 - 11:00)	Grover's algorithm for multiple solutions, with exercise 3.4 worked out (xlsx).
Nov 16 (09:00 - 11:00)	Exercises on the quantum Fourier transform (pdf).

Sep 24 (16:00 - 18:00)	Review of the mathematical background for quantum computation: Hilbert spaces. (slides).
Oct 1 (16:00 - 18:00)	Exercises on the mathematical background for quantum computation (pdf).
Oct 12 (09:00 - 11:00)	Animating quantum algorithms: An introduction with pennylane. From bits and logical gates to qubits and unitary matrices. Single qubit states, Pauli and Hadamard matrices. (slides) and (notebook).
Oct 22 (16:00 - 18:00)	Animating quantum algorithms (cont.) (slides) and (notebook).
Oct 29 (16:00 - 18:00)	Exercises on phase kick-back algorithms (pdf).
Nov 5 (16:00 - 18:00)	Geometric interpretation of Grover's algorithm. Exercises (pdf).
Nov 12 (16:00 - 18:00)	Quantum phase estimation (slides) and an introduction to the quantum Fourier transform (slides).
Nov 19 (16:00 - 18:00)	Entanglement as a computational resource. The teleportation protocol (slides). Exercises (pdf).

M. A. Nielsen and I. L. Chuang. Quantum Computation and Quantum Information (10th Anniversary Edition). Cambridge University Press, 2010
E. Rieffel and W. Polak. Quantum Computing: A Gentle Introduction. MIT Press, 2011.
N. S. Yanofsky and M. A. Mannucci. Quantum Computing for Computer Scientists. Cambridge University Press, 2008.

Peter Wittek. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier. 2014.
Elías F. Combarro and Samuel González-Castillo. A Practical Guide to Quantum Machine Learning and Quantum Optimization. Packt Publishing, 2023.

Group assignment with written report and individual oral defense (25%). This component is not assessed through exam.
Oral: 17 December 2026
Individual written test with restricted consultation (two A4 handwritten pages only) (75%)
Test: 4 January 2027 (Exam: 25 January 2027)

Appointments (please send an email the day before)
Mon, 18:00-20:00 and Wed, 18:00-20:00
Email: lsb at di dot uminho dot pt (Luis) and andre.m.sequeira at inesctec dot pt (André)
Last update: 2026.11.11