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.

Syllabus

• 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.

Summaries (2025-25)

T Lectures

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).

TP Lectures

Sep 24 (16:00 - 18:00)

Review of the mathematical background for quantum computation: Hilbert spaces. (slides).

Bibliography

Quantum Computation and Algorithms

• 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.

Quantum Computation for Data Science

• 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.

Links

• Quantum Programming in PennyLane
• QSE - Quantum Software Group at INESCT TEC
• Quantum and Linear Optical Computation Group at INL

Pragmatics

Lecturers

• Luís Soares Barbosa
• André Sequeira

Assessment

• Group assignment with written report and individual oral defense (25%). This component is not assessed through exam.

  Oral: 14 December 2026

• Individual written test with restricted consultation (two A4 handwritten pages only) (75%)

  Test: 4 January 2027 (Exam: 25 January 2027)

Contact

• 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: 2026.09.20