Calculus Quantum physics Advanced quantum physics Electromagnetic fields and waves Quantum Computing

Part 1.- Introduction to quantum computing (NISQ) ¿ Quantum Computing: general idea, types of quantum computing. ¿ Quantum gates and circuits ¿ Quantum Programming ¿ Quantum variational circuits: general idea Part 2.- Quantum Variational Algorithms (QAOA and VQE) ¿ Quantum Variational Algorithms ¿ Quantum Approximate Optimization Algorithm ¿ Variational Quantum Eigensolver ¿ Issues of QVA¿s: Barren plateaus, expressivity and measurements Part 3.- Quantum Support Vector Machines and Kernel Methods ¿ Classical Kernel Methods: support vector machines and classifiers. ¿ Quantum Kernel Methods ¿ Quantum support vector machines and classifiers Part 4.- Quantum Boltzmann Machines/Generative Models/Unsupervised ¿ Unsupervised classical machine learning and generative models ¿ Quantum Boltzmann Machines ¿ Quantum generative adversarial networks Part 5.- Quantum Neural Networks ¿ Quantum Neural Network classifier ¿ Data re-uploading ¿ Convolutional Quantum Neural Networks ¿ Quantum optical neural networks Part 6.- Recent advances, outlook, other paradigms (recent results)

Educational activities: - Theory lessons - Tutorial sessions - Practical quantum programming activities - Individual student work Educational Methodologies: - Classroom lessons by lecturers in which the main concepts will be developed. Bibliography will be provided to students as a complement to the main lessons - Solution of practical exercises in the classroom and also individually by students. - Practices on quantum programming.