Lectures

Quantum Artificial Intelligence

Vorlesung an der Universität des Saarlandes, Fachrichtung Informatik, LSF 153374

#	Lecture
1	Welcome Course organization Introduction: Quantum Computing and AI in a Nutshell Q/A
Part I: Quantum Computation
2	Basic Concepts (I) Postulates of Quantum Mechanics Qubits, quantum registers, operations and gates, entanglement, measurement No-cloning theorem
3	Basic Concepts (II) Quantum teleportation Implementation in Qiskit
4	Quantum Algorithms (I) Quantum parallelism and Deutsch Algorithm Implementation in Qiskit
5	Quantum Algorithms (II) Quantum parallelism and Deutsch-Josza Algorithm
6	Quantum Algorithms (III) Quantum search and Grover’s Algorithm Implementation in Qiskit
Part II: Quantum Computation for AI
7	Quantum Complexity and Quantum Machine Learning (I) Quantum prime factorization and Shor’s Algorithm and HHL (in brief) Quantum Complexity Introduction to Quantum Machine Learning
8	Quantum Machine Learning (II) Fault-Tolerant Quantum Algorithms for Supervised Learning Variational Quantum Algorithms and Quantum Neural Networks
9	Quantum Machine Learning (III) Implicit and Explicit Quantum Models: Implementation with Qiskit and Pennylane Introduction to Quantum Reinforcement Learning
10	Quantum Machine Learning and Optimization (I) Hybrid quantum reinforcement learning for safe navigation Adiabatic quantum computation in brief
11	Quantum Optimization (II) From NP-hard AI problem to gate-based or adiabatic quantum solution Q-seg: Quantum annealing-based unsupervised image segmentation
12	Selected QAI Algorithms for Applications (I) -- ONLINE Quantum Coalition Structure Generation Quantum Annealing-based Algorithm for Efficient Coalition Formation among Low-Earth-Orbit Satellites presented by Supreeth Mysore Venkatesh (RPTU Kaiserslautern, DFKI Saarbrücken)
13	Selected QAI Algorithms for Applications (II) -- ONLINE Supervised Classification with Quantum Kernels Symbolic Representation of a Parametric Quantum State in Quantum Neural Networks with QISKIT-Symb presented by Simone Gasperini (University of Bologna)

Selected background references:

Quantum Artificial Intelligence

Klusch, M., Lässig, J., Müssig, D., Macaluso, A., & Wilhelm, F.K. (2024): Quantum Artificial Intelligence: A Brief Survey. Künstliche Intelligenz, 4/24, Springer. Also: arxiv:2408.10726

Quantum Computing

De Wolf, R. (2023): Quantum Computing: Lecture Notes. arxive 1907.09415
Nielsen, M. & Chuang I. (2002): Quantum Computation and Quantum Information.
Biamonte, J. et al. (2017): Quantum Machine Learning. Nature, 549(7671):195-202.
Rieffel, E. G., & Polak, W. H. (2011). Quantum computing: A gentle introduction. MIT Press.
Kaye, P., Laflamme, R., & Mosca, M. (2007). An introduction to quantum computing. Rinton Press.
Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.

Artificial Intelligence

Russell, S. & Norvig, P. (2021). Artificial Intelligence: A modern approach. 4th Ed., Pearson Education
Mitchell, T. (1997): Machine Learning. McGraw-Hill
Goodfellow, I.J. et al. (2016): Deep Learning. MIT Press,
Nielsen, M. (2017): Neural Networks and Deep Learning.
http://deeplearning.net