Compulsory courses (18-24 ECTS)

• AQAlg: Advanced Quantum Algorithms, 6 ECTS, Shor algorithms, Quantum Fourier Transform, phase estimations and applications, quantum random walks , query complexity (algorithms, lower bounds), HHL algorithm and machine learning applications, hidden subgroup algorithms.

• AQCrypt: Advanced Quantum Cryptography, 6 ECTS, The use of quantum states has many cryptographic applications. This course includes advanced quantum key distribution (various technologies and protocols, security proofs, practical problems, full and semi-device independence), but also other cryptographic protocols (bit commitment, oblivious transfer in the noisy storage model, secret sharing, verification

• QIT: Quantum Information Theory, 6 ECTS, The quantum analogue of Shanon theory and complexity theory are very rich, with many applications: Mathematical foundations and link with programming, quantum Shannon theory (various entropies, super additivity, Holevo theorem); communication complexity, with lower bounds and exponential separations; channel theory; entanglement theory, nonlocal games, information causality; quantum complexity theory, BQP, QMA, QMA(2), QIP=QIP(3)=PSPACE=QPSPACE.

• PhQC: Photonics Quantum Computing, 6 ECTS, Photonics is an essential platform for quantum communications and a promising one for quantum computing. These platforms are centered around measurement based quantum computing (MBQC). We willl study: Graph-states (in continuous and discrete variables), Graph-based cryptographic protocols, MBQC, delegated blind/verified universal quantum computing, photonic graph states implementations.

Elective courses (6-12 ECTS) (subject to availability)

• NDA: Network Data Analysis, 6 ECTS, Today's telecommunication networks produce a massive amount of data, based on which the operator needs to configure the system and optimise users’ quality of service and experience. There is an abundant number of data-related methods that can be applied in various aspects of network operation ranging from anomaly detection to traffic prediction and control. In this course, the student will be guided how to use in Python machine learning and statistical methods using established libraries (Scikit-learn), in order to resolve such real-world problems: Methods to be mastered include classification, regression, trees, clustering and time-series analysis.

• POSSO: Polynomial systems Resolution, 6 ECTS, Polynomial systems model static nonlinear situations. Their resolution is therefore more delicate than the resolution of linear systems, but nevertheless crucial since these systems appear naturally in post-quantum cryptology and various engineering sciences (robotics, biology, chemistry, artificial vision, etc.). In this course, we will study efficient algorithms allowing us to solve such systems and examples of applications will be studied.

• RDFIA: Pattern Recognition and Machine Learning for Image Understanding, 6 ECTS, this course presents theory and algorithms for classification and image understanding (Bayesian decision, machine learning, supervised and unsupervised learning, kernel-based methods, deep learning...). Illustrations are provided, on several applications for image classification.

• AAGA: Analyse d'Algorithmes et Génération Aléatoire (only in French), 6 ECTS, Ce cours introduit des méthodes pour étudier la complexité moyenne des algorithmes et la génération aléatoire de structures combinatoires. Divers types d'applications seront traitées, en liaison avec les structures arborescentes et l'algorithmique probabiliste.La seconde partie du cours introduit la combinatoire analytique afin d'étudier algorithmes et structures de données en moyenne.

• HPCA: Advanced High Performance Computing  (only in French), 6 ECTS, Algorithmes et techniques de programmation parallèles avancés pour le calcul haute performance. Conception , implémentation et optimisation des programmes parallèles sur des architectures hétérogènes et massivement parallèles (GPU, calcul haute performance à large échelle sur un grand nombre de noeuds ...). Introduction aux langages standards pour le calcul haute performance (extensions de langage et directives de compilation), optimisation de code dans un contexte hétérogène : multi- architecture et multi-paradigme, algorithmes parallèles et leur stabilité numérique pour l’algèbre linéaire numérique, algorithmes parallèles minimisant les communications, calcul haute performance et reproductibilité, algorithmique asynchrone. Mise en pratique sur une application réelle (projet).

Teaching units of SU Master of Physics, especially Lumi or ICFP, can also supplement the course offer.

5-6 month internship 30 ECTS