Quantum Inteligence
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | Czech |
| Code | 460-4180/01 |
| Abbreviation | KI |
| Course title | Quantum Inteligence |
| Credits | 4 |
| Coordinating department | Department of Computer Science |
| Course coordinator | prof. Ing. Ivan Zelinka, Ph.D. |
Osnova předmětu
Lectures:
Introduction to quantum computing and quantum AI
Basic quantum algorithms and their role in AI
Quantum simulators and algorithm implementation in Qiskit and Cirq
Quantum neural networks: Basics and first implementations
Quantum algorithm for k-means clustering
Quantum Support Vector Machine (QSVM)
Quantum perceptron and its extension to deep quantum networks
Quantum stochastic gradient descent (QSGD) for quantum network optimization
Quantum reinforcement learning (QRL)
Variational Quantum Eigensolver (VQE) and its applications in AI
Quantum Approximate Optimization Algorithm (QAOA) in AI tasks
Quantum Generative Adversarial Networks (Quantum GANs)
Final project and presentation
Exercises (in PC classrooms):
Laboratory exercise 1: Introduction to quantum programming in AI.
Laboratory exercise 2: Implementation of basic quantum algorithms in AI.
Laboratory exercise 3: Quantum simulators and working with real quantum computers.
Laboratory exercise 4: Basic quantum neural network (QNN).
Laboratory exercise 5: Quantum k-means clustering.
Laboratory exercise 6: Quantum Support Vector Machine (QSVM).
Laboratory exercise 7: Optimization of quantum neural networks using QSGD.
Laboratory exercise 8: Variational Quantum Eigensolver (VQE) and its use in AI.
Laboratory exercise 9: Quantum GANs – Generative models.
Introduction to quantum computing and quantum AI
Basic quantum algorithms and their role in AI
Quantum simulators and algorithm implementation in Qiskit and Cirq
Quantum neural networks: Basics and first implementations
Quantum algorithm for k-means clustering
Quantum Support Vector Machine (QSVM)
Quantum perceptron and its extension to deep quantum networks
Quantum stochastic gradient descent (QSGD) for quantum network optimization
Quantum reinforcement learning (QRL)
Variational Quantum Eigensolver (VQE) and its applications in AI
Quantum Approximate Optimization Algorithm (QAOA) in AI tasks
Quantum Generative Adversarial Networks (Quantum GANs)
Final project and presentation
Exercises (in PC classrooms):
Laboratory exercise 1: Introduction to quantum programming in AI.
Laboratory exercise 2: Implementation of basic quantum algorithms in AI.
Laboratory exercise 3: Quantum simulators and working with real quantum computers.
Laboratory exercise 4: Basic quantum neural network (QNN).
Laboratory exercise 5: Quantum k-means clustering.
Laboratory exercise 6: Quantum Support Vector Machine (QSVM).
Laboratory exercise 7: Optimization of quantum neural networks using QSGD.
Laboratory exercise 8: Variational Quantum Eigensolver (VQE) and its use in AI.
Laboratory exercise 9: Quantum GANs – Generative models.
E-learning
For e-learning support, an AI assistant will be used to provide students with interactive and continuous assistance in learning quantum programming and quantum algorithms. The assistant will contain the complete knowledge base of the course, including theoretical concepts, practical demonstrations and laboratory exercises. Students will be able to ask questions, get explanations of algorithms, consult quantum circuit optimization, and receive feedback on their codes in Qiskit, Cirq, and other quantum frameworks. The AI assistant will enable a faster understanding of complex concepts, thus promoting more effective self-study and facilitating lab problem solving throughout the course.
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
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.
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.