Compulsory courses (12 ECTS)

• QCLG: Quantum circuits and logic gates, 3 ECTS, Quantum circuits are a common language in quantum information, blending insights and techniques from both computer science and physics. Single and two qubit gates, as well as Toffoli gates and some small circuits. Students from both backgrounds are paired to work together in tutorials and hand on sessions with digital tools to manipulate quantum circuits.

• QPh4CS: Quantum physics for computer scientists, 9 ECTS, Quantum cinematics needed to understand theoretical quantum information (Hilbert spaces in physicists notations, unitary transformations, projective measurements and POVMs, density matrices and partial traces). Quantum dynamics (Hamiltonian, evolution operator, dissipation). Link with some quantum information physical implementations (cold atoms, photonics). The focus will be on finite dimensional systems.

 Elective courses (18 ECTS) (subject to availability)

• BIMA: Fundamentals of image processing, 6 ECTS, this course include Fourier analysis, acquisition and theory of sampling, filtering and denoising, edge detection, segmentation. Applications are given on a few concrete problems (key-point detection, face recognition...), with practical works.

• DIGCOM: Digital communication, 6 ECTS, this course provide the tools that are necessary for analyzing, modeling and designing digital transmission systems. The first part of the course focuses on the necessary bases in deterministic and random signal processing. The rest of the course shows their application to the physical layer of communications systems: architecture of a digital transmission chain, models and performance evaluation.”

• MODEL, 6 ECTS, Mathematical algorithms play a central role in many fields of computing, whether it is to secure the transmission and/or exchange of data (by cryptography), to analyze large masses of data, or to optimize criteria under possible constraints. The underlying algorithms share paradigms and computational schemes pertaining to algebra and mathematical analysis. Also, the concepts of complexity (binary or arithmetic), and digital conditioning hold an essential place.

• ALGAV: Algorithmique Avancée  (only in French), 6 ECTS, étude de l'utilisation de structures de données avancées (files de priorité, arbres de recherche, hachage, arbres digitaux) permettant d'optimiser les performances des algorithmes dans des domaines d'application variés comme la gestion et la compression de données massives.

• COMPLEX (only in French), 6 ECTS, Etude des ressources de calcul (temps, espace mémoire ...) nécessaires pour résoudre les problèmes algorithmiques, en distinguant les problèmes dits "faciles" (problèmes dont la complexité est une fonction polynomiale de la taille du problème), des problèmes dits "difficiles". Introduction des classes de complexité fondamentales P et NP et définition de la NP-complétude. Introduction aux algorithmes d'approximation et de randomisation permettant de contourner la difficulté de résolution des problèmes difficiles, et permettant ainsi leur application en pratique avec des temps de calcul raisonnables (algorithmes de type Las Vegas, Monte Carlo, approximation avec garantie de performance, etc.).

• MOGPL (only in French), 6 ECTS, Introduction aux graphes et la programmation linéaire comme outils de modélisation et de résolution de problèmes d'optimisation ou de décision. Étude de modèles et analyse d'algorithmes fondamentaux de l'optimisation combinatoire. Constitue la base nécessaire à tout étudiant en informatique souhaitant acquérir une bonne maîtrise des modèles et algorithmes pour la résolution de problèmes d'optimisation, qu'il s'agisse de problèmes réels rencontrés dans un contexte industriel, ou de problèmes de recherche académique.

• FLE/AA, French for non French speaking students / Advanced English for French speaking students, 3 ECTS.

Compulsory courses (18 ECTS)

• QIIntro: Quantum information overview, 6 ECTS, The objective is to give the student a broad overview of theoretical quantum information, including fundamental concepts (entanglement, teleportation, nonlocality, decoherence), quantum communication protocol (teleportation, quantum key distribution), quantum algorithms (Shor, Grover and VQE) and some notions of quantum error correction.

• PQIG: Quantum Information Group Project, 3 ECTS, Project with physics students: Work in small groups with students from various backgrounds on a Quantum information problem, performing a short review of the state-of-the-art and an experimentation.<

• PQIA: Advanced Quantum Information Project, 9 ECTS, Project work on Quantum information problem, performing a review, an analysis and an implementation of one or several solutions.

Elective courses (12ECTS) (subject to availability)

• ANum, 3ECTS, This unit is the natural continuation of  MODEL. Provide the knowledge in mathematical tools and algorithms in order to be able to solve concrete problems of large sizes. We will study in particular algorithms and their implementation frequently used in the field of scientific computing and data science. The applications will be very diverse and may change each year: for example, we will see applications in finance (calculation of the price of options), in simulation of structures for 3D printing, in imagery (image compression), in deep learning (stochastic gradient algorithm), etc. We will endeavor for each algorithm to propose versions allowing an efficient implementation on parallel machines. The algorithms will be coded in MATLAB or in Python.

• FLAG: Basics of Algebraic Algorithms, 6 ECTS, This course present algebraic algorithms for basic and building blocks problems, targeting quasi-optimal complexity. To do so, we shall rely on linear algebra modeling of the problem. However, we shall show that this modeling yields a linear system with a structure that can actually be solved faster than general algorithms allow us to. These problems find applications in computer algebra, cryptography, coding theory or robotics. A thorough implementation of these algorithms will also be studied.

• IG3D: Introduction to Computer Graphics, 6 ECTS,This course introduces the domain of 3D computer graphics, including geometric modeling and processing, image synthesis, with implementation in OpenGL and C/C++.

• SDM: System Design and Modeling, 6 ECTS, Introduction to the problem of modeling and performance evaluation of systems. It aims at answering the following questions: Why models are important? When do we need to evaluate the performance of a system? How? What kinds of models and techniques are useful?

• HPC: High Performance Computing (only in French), 6 ECTS, Introduction au calcul haute performance et au parallélisme pour concevoir, implémenter et optimiser les programmes parallèles sur des architectures classiques (multi- processeurs et multi-cœurs). Les points suivants sont abordés : architecture des machines parallèles, algorithmique parallèle, parallélisme de données et de tâches, décomposition et équilibrage de charge, paradigmes standards de programmation parallèle sur machines à mémoire distribuée ou partagée (standards MPI et OpenMP), programmation mixte multi-thread / multi-processus, optimisation de code séquentiel pour le calcul haute performance, programmation vectorielle (SIMD), introduction à la parallélisation automatique. Mise en pratique sur une application réelle (projet).

• PC2R: Programmation Concurrente, Réactive et Répartie (only in French), 6 ECTS, L'objectif de ce cours est de comprendre la programmation concurrente et son utilisation pour l'expression d'algorithmes dans les modèles à mémoire partagée, distincte et répartie. Dans le modèle à mémoire partagée, on s'intéresse aux modèles de threads coopératifs et préemptifs puis à la programmation réactive pour récupérer la propriété de déterminisme. Dans le modèle à mémoire répartie on cherche à maîtriser le modèle client/serveur et de savoir déployer des objets répartis