Learning Outcomes
On successful completion of the course students should be able
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To master the quantum computational model, understanding basic concepts of quantum information and quantum computation.
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To design and analyse quantum algorithms.
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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.
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TP Lectures
| Sep 24 (16:00 - 18:00) |
Review of the mathematical background for quantum computation.
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Bibliography
Quantum Computation and Algorithms
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M. A. Nielsen and I. L. Chuang. Quantum Computation and Quantum Information (10th
Anniversary Edition). Cambridge University Press, 2010
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E. Rieffel and W. Polak. Quantum Computing: A Gentle Introduction. MIT Press, 2011.
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N. S. Yanofsky and M. A. Mannucci. Quantum Computing for Computer Scientists. Cambridge
University Press, 2008.
Quantum Computation for Data Science
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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
Pragmatics
Lecturers
Assessment
Contact