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Quantum Inteligence

Language of instruction angličtina, čeština
Code 460-4180
Abbreviation KI
Course title Quantum Inteligence
Coordinating department Department of Computer Science
Course coordinator prof. Ing. Ivan Zelinka, Ph.D.

Anotace

The course Quantum Intelligence focuses on the practical implementation of artificial intelligence algorithms in quantum computing environments. Students will gradually learn both basic and advanced methods of quantum machine learning and acquire experience with quantum simulators as well as real quantum computers. Upon completion of the course, they will be able to design and implement quantum neural networks, classification and optimization algorithms, generative models, and hybrid quantum–classical approaches. They will gain the ability to analyze the performance of these methods, compare them with classical approaches, and apply them to solving complex artificial intelligence tasks. The learning outcomes include the capability to work independently and in teams on the design and presentation of quantum-AI solutions using modern development frameworks.

Povinná literatura

Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information (10th Anniversary Edition). Cambridge University Press. ISBN-13: 978-1107002173.
https://www.cambridge.org/highereducation/books/quantum-computation-and-quantum-information/01E10196D0A682A6AEFFEA52D53BE9AE
Pokrytí lekcí: 1, 2, 3, 6 – Základní kvantové výpočty a algoritmy

Wittek, P. (2014). Quantum Machine Learning: What Quantum Computing Means to Data Mining. Academic Press. ISBN-13: 978-0128100402 .
https://www.amazon.com/Quantum-Machine-Learning-Computing-Elsevier-ebook/dp/B00NPVBN0W
Pokrytí lekcí: 4, 5, 6, 7 – Kvantové neuronové sítě, QSVM, a další algoritmy strojového učení

Schuld, M., & Petruccione, F. (2018). Supervised Learning with Quantum Computers. Springer. ISBN-13: 978-3319964232 .
https://link.springer.com/book/10.1007/978-3-319-96424-9
Pokrytí lekcí: 4, 5, 7, 9 – Kvantové neuronové sítě, kvantové strojové učení, a optimalizace

Pattanayak, S., 2017. Quantum machine learning with Python: Using Cirq from Google research and IBM Qiskit. Apress.
https://link.springer.com/book/10.1007/978-1-4842-6522-2
Pokrytí lekcí: 8, 10, 12 – Hybridní kvantové algoritmy, Quantum GANs, VQE

Zoufal, C., Lucchi, A., & Woerner, S. (2019). Quantum Generative Adversarial Networks for Learning and Loading Random Distributions. npj Quantum Information, 5(1), 103.
https://www.nature.com/articles/s41534-019-0223-2
Pokrytí lekcí: 12 – Kvantové GANs, pokročilé techniky generativního učení

Lamata, L., Alvarez-Rodriguez, U., & Solano, E. (2017). Quantum Reinforcement Learning with Superconducting Circuits. Springer. ISBN-13: 978-3319991252 .
https://link.springer.com/article/10.1007/s11128-023-03867-9
Pokrytí lekcí: 9 – Kvantové posílené učení

Doporučená literatura

Benenti, G., Casati, G., & Strini, G. (2019). Principles of Quantum Computation and Information: Basic Concepts. World Scientific. ISBN-13: 978-9813278221 .
https://www.worldscientific.com/worldscibooks/10.1142/5528?srsltid=AfmBOoqwMBA7lEXKK5Ivon8CkSLmnUNTsudukWF4Jc1m-JJvU8syhLNp
Pokrytí: Úvod do kvantových výpočtů a základní principy pro aplikace v AI, kvantové algoritmy.

Cerezo, M., Arrasmith, A., & Coles, P. J. (2021). Variational Quantum Algorithms. Nature Reviews Physics, 3(9), 625-644.
https://www.nature.com/articles/s42254-021-00348-9
Pokrytí: Hybridní algoritmy jako VQE a QAOA v AI aplikacích.

Dunjko, V., & Briegel, H. J. (2018). Machine Learning & Artificial Intelligence in the Quantum Domain. Reports on Progress in Physics, 81(7), 074001.
https://iopscience.iop.org/article/10.1088/1361-6633/aab406
Pokrytí: Moderní pohled na využití AI a strojového učení v kvantovém prostředí, včetně kvantového posilovaného učení.


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