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M1 Quantum and Distributed Computer Science

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  • Places available
    20
  • Language(s) of instruction
    English
Présentation
Objectives

Computer systems are fast evolving toward more efficiency and enhanced functionalities according to three major, interconnected scientific fields:

  • distributed systems, as many applications are deployed spatially over networks to enable ubiquity,
  • high-performance computing to take advantage of most recent parallel computers in scientific computing and data analytics applications,
  • quantum computing to exploit quantum parallelism and obtain otherwise unreachable performance gains.

The QDCS Master’s program will enable you to acquire deep knowledge of these three fields through both advanced theoretical courses and extensive practice of programming techniques.

Distributed systems deal with protocols and algorithms that allow connectivity and efficient functionality for network based systems, like Internet, Cloud, sensor networks, computing clusters, blockchain distributed systems, and even microbiological circuits. For these systems, the challenges include synchronization, security, concurrency and robustness. Similar issues arise in the field of High Performance Computing (HPC) which aims at efficiently solving computationally intensive problems in applied science or artificial intelligence. HPC pushes modern parallel computer architectures to their limits by using various forms of parallelism, data representations and code optimization. So does distributed computing by means of various methods of communication and algorithmics. They thereby draw the frontier between what can be achieved within the realm of classical Computer Science, and what will only be accessible through a new paradigm: that of Quantum Computing. Quantum Computing and Quantum Information feature novel algorithms and protocols, bringing radical performance gains, together with their own set of conceptual and technical challenges.  

Whilst all of these topics will be covered by default, students who want to deepen their knowledge in one of the three strands will be given the flexibility to do so. This will be the case in particular for students joining the program for the second year (M2) of QDCS. Notice that several available options will let you complete your profile e.g. in Machine Learning, Data Sciences, Security and more.

All courses are in English. 

A limited number of scholarships towards your living costs are available [1, 2, 3], with early deadlines (~May).

Location
ORSAY
GIF SUR YVETTE
Course Prerequisites

Prior study in Computer Science and related disciplines is desirable. However, students who have completed undergraduate studies in another scientific field (such as Mathematics or Physics), but have some foundations in Computer Science (algorithms, programming), will also be able to make the best of this Master’s program.

Skills
  • Understand the current and future challenges of distributed, parallel or quantum systems. Be able to assess their contributions to various fields of applications (Security, Machine Learning, Data Science…).

  • Be able to design and prove distributed, parallel or quantum algorithms/protocols and analyze their complexities (in time, memory, communication, energy, etc.). 

  • Understand advanced C++ programming techniques in order to design concise and efficient code. Become familiar with parallel programming paradigms.

  • Understand the quantum nature of information. Become familiar with quantum programming as well as error correction and simulation techniques.

Post-graduate profile

During the M1 QDCS, students will acquire solid knowledge of the fields of distributed algorithms, parallel computing, quantum computing, but also strong skills in advanced programming, especially for HPC (MPI, OpenMP, etc.). The choice of optional modules will allow them to become familiar with various other possible fields of application, including AI, data sciences or security. It is therefore a broad and complete training course that allows them to both acquire solid theoretical foundations and master their practical implementation. Thus, the course enables rapid integration into the industrial and scientific worlds, by developing the ability to anticipate technological developments.

Career prospects

Further studies: 2nd year of the Master in Informatics.

With the rise of the Cloud, the IoT, the constant development of supercomputers, the deployment of the European quantum flagship and the national quantum plan, the QDCS Master's program is targeting blossoming scientific and economic fields. The Master enables students to continue with a PhD, by preparing a thesis within a public research organization or the R&D department of a large company. The Master also enables students to easily integrate the industrial world, for example within companies having high computing needs, or more generally in the high-tech world, whether in the R&D departments of large companies or in startups developing cutting-edge software.

Collaboration(s)
Academic partner

École Polytechnique

Télécom Paris

INRIA

Sorbonne Université

Université de Paris

Technion - Israel Institute of Technology

University of Tennessee

Old Dominion University

École Polytechnique Fédérale de Lausanne

Lisbon University

Karlsruhe Institute of Technology

University of Vienna

Laboratories

Formal Methods Laboratory (LMF)
Interdisciplinary Laboratory of Digital Sciences (LISN)
Computer Science Laboratory of the École polytechnique (LIX)
Information Processing and Communication Laboratory (LTCI)
Signals and Systems Laboratory (I2S)

Programme
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
[ANO] MPI programming 2.5
[MPRI] Foundations of Quantum Information 2.5 21
[QDCS] Auto-stabilisation 2.5 21
[QDCS] Robust distributed algorithms 2.5 21
[QDCS] Parallel algorithms 2.5 12 6 3
[QDCS] Initiation to quantum algorithms and programming 2.5 21
[M1 QDCS] Game, learning, and optimisation of 2.5 21
complex systems 2.5 21
[QDCS] Advanced C++ programming 2.5 9 0 12
[QDCS] Programmation orientée objet 2.5 11 10
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
[QDCS] Algorithmes de la nature 2.5 21
[QDCS] Big Data 2.5 12 3 8
[QDCS] Distributed computing with mobile agents 2.5 21
[M1 QDCS] High performance computing 2.5 8 13
[QDCS] Advanced quantum computing and error correction 2.5 21
[QDCS] Frontières du calcul parallèle, distribué et quantique 2.5 21
[QDCS] Stochastic optimisation 2.5 21
[QDCS] Ordonnancement et systèmes d'exécution 2.5 21
[QDCS] GPU programming 2.5 8 13
[QDCS] Quantum processor simulation 2.5 21
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
[AI] OPT 10: IMAGE INDEXING AND UNDERSTANDING 2.5 15 6
[AI] OPT 11: DEEP LEARNING FOR NLP 2.5 18 3
[AI] OPT 12: INFORMATION EXTRACTION FROM DOCUMENTS TO INTERFACES 2.5 10.5 10.5
[AI] OPT 13: Theorie de l'information 2.5 10.5 10.5 0 0
[AI] OPT1 : GRAPHICAL MODELS 2.5 15 6
[AI] OPT14:MULTILINGUAL NATURAL LANGUAGE PROCESSING 2.5 21
[AI] OPT2: IMAGE PROCESSING 2.5 21
[AI] OPT3 : REINFORCEMENT LEARNING 2.5 15 6
[AI] OPT4: DEEP LEARNING 2.5 10.5 10.5
[AI] OPT5 : VOICE RECOGNITION AND AUTOMATIC LANGUAGE PROCESSING 2.5 21
[AI] OPT6: LEARNING THEORY AND ADVANCED MACHINE LEARNING 2.5 21
[AI] OPT7: ADVANCED OPTIMIZATION 2.5 12 4.5 4.5
[AI] OPT8: GAME THEORY 2.5 12 4.5 4.5
[AI] OPT9: DATA CAMP 2.5 10 15
[AI] PRE1: APPLIED STATISTICS 2.5 10.5 10.5
[AI] PRE2: MATHEMATICS FOR DATA SCIENCE 2.5 12 4.5 4.5
[AI] PRE3: DATACOMP 1 2.5 12 9
[AI] PRE4: SCIENTIFIC PROGRAMMING 2.5 9 12
[AI] TC0 : Introduction to Machine Learning 2.5 15 6
[AI] TC1: MACHINE LEARNING 2.5 15 6
[AI] TC2: OPTIMIZATION 2.5 12 4.5 4.5
[AI] TC3: INFORMATION RETRIEVAL 2.5 9 12
[AI] TC4: Probabilistic Generative Models 2.5 16.5 4.5
[AI] TC5: SIGNAL PROCESSING 2.5 24
[AI] TC6: DATACOMP 2 2.5 12 9
[ANO] Blockchain 2.5
[ANO] Evaluation de performances 2.5
[ANO] Internet of Things 2.5 21
[ANO] Optimisation dans les graphes 2.5 21
[ANO] Optimisation discrète non linéaire 2.5 21
[ANO] Optimisation multi-objectifs 2.5 21
[ANO] MPI programming 2.5
[ANO] Programmation système et réseaux 2.5 21
[ANO] Réseaux mobiles 2.5 21
[ANO] Réseaux sans fil 2.5 21
[ANO] Tests fonctionnels de protocoles 2.5 21
[ANO] Théorie des jeux 2.5 21
[ANO] Virtualisation et cloud 2.5
[DS] Algorithms for Data Science 2.5 12 9
[DS] Bases de données avancées I : Optimisation 2.5 9 8 4
[DS] Bases de données avancées II : Transactions 2.5 9 8 4
[DS] Data Science Project 2.5 3 18
[DS] Distributed Systems for Massive Data Management 2.5 12 0 9
[DS] Intelligence Artificielle, Logique et Contraintes 2.5 10.5 10.5
[DS] Intelligence Artificielle, Logique et Contraintes : Projet 2.5 10.5 10.5
[DS] Knowledge Discovery in Graph Data 2.5 12 6 3
[DS] Semantic Web and Ontologies 2.5 12 9
[DS] Social and Graph Data Management 2.5 12 9
[HCI] Advanced Design of Interactive Systems 2.5
[HCI] Advanced Immersive Interactions 2.5 21
[HCI] Advanced Immersive Interactions - Project 2.5
[HCI] Advanced Programming of Interactive Systems 1 2.5
[HCI] Advanced Programming of Interactive Systems 2 2.5
[HCI] Career Seminar - Level 1 2.5
[HCI] Career Seminar - Level 1 : Project 2.5 21
[HCI] Career Seminar - Level 2 2.5
[HCI] Career Seminar - Level 2 project 2.5
[HCI] Creative Design 2.5
[HCI] Creative Design : Project 2.5
[HCI] Design of Interactive Systems 2.5
[HCI] Design project - Level 1 2.5 21
[HCI] Design project - Level 1 : Project 2.5 21
[HCI] Design project - Level 2 2.5 21
[HCI] Design project - Level 2 : Project 2.5 21
[HCI] Digital Fabrication 2.5
[HCI] Digital fabrication : Project 2.5
[HCI] Evaluation of Interactive Systems 2.5
[HCI] Experimental Design and Analysis 2.5
[HCI] Fundamental of Human-Computer Interaction 1 2.5
[HCI] Fundamental of Human-Computer Interaction 2 2.5
[HCI] Fundamental of situated computing 2.5
[HCI] Fundamentals of eXtended Reality 2.5
[HCI] Gestural and Mobile Interaction 2.5
[HCI] Groupware and Collaborative Work 2.5 21
[HCI] Groupware and Collaborative Work : Project 2.5 21
[HCI] Interactive Information Visualization 2.5
[HCI] Interactive Information Visualization : Project 2.5
[HCI] Interactive Machine Learning 2.5
[HCI] Interactive Machine Learning : Project 2.5
[HCI] Mixed Reality and Tangible Interaction 2.5 21
[HCI] Mixed Reality and Tangible Interaction - Project 2.5 21
[HCI] Programming of Interactive Systems 1 2.5
[HCI] Programming of Interactive Systems 2 2.5
[HCI] Serious games 2.5
[HCI] Serious games : project 2.5
[HCI] Studio Art Science 2.5 21
[HCI] Virtual Humans 2.5 21
[HCI] Virtual Humans : Project 2.5 21
[ISD] Algorithmique avancée 3 25
[ISD] Algorithmique distribuée 3 25
[ISD] Anglais 3 25
[ISD] Anglais 3 25
[ISD] Blockchain 3 25
[ISD] Cloud Computing 3 25
[ISD] Communication 3 25
[ISD] Data Lake 3 25
[ISD] Data Warehouse I 3 25
[ISD] Data Warehouse II 3 25
[ISD] Droit informatique 3 25
[ISD] Extraction et programmation statistique de l'information 3 25
[ISD] Introduction à l'apprentissage 3 25
[ISD] IoT (Internet des objets) 3 25
[ISD] langages Dynamiques 3 25
[ISD] Machine learning/Deep learning 3 25
[ISD] Mémoire 12 8
[ISD] Modèles Mathématiques 3 25
[ISD] Modélisation 3 25
[ISD] Optimisation 3 25
[ISD] outils pour la manipulation et l'extraction de données 3 25
[ISD] Politiques et concepts avancés en sécurité 3 25
[ISD] Probabilités/Statistiques 3 25
[ISD] Programmation système et réseau 3 25
[ISD] Projet étude de cas 3 25
[ISD] Projets tuteurés 6 25
[ISD] Rapport d'activité 6 5
[ISD] Représentation des connaissances et visualisation 3 25
[ISD] Réseaux 3 25
[ISD] Réseaux sans fil 3 25
[ISD] sécurité 3 25
[ISD] Services et applications Web 3 25
[ISD] Test et Vérification 3 25
[ISD] Traitement automatique des langues 3 25
[ISD] Traitement distribué des données. 3 25
[QDCS] Algorithms in the nature 2.5 21
[QDCS] Robust distributed algorithms 2.5 21
[QDCS] Parallel algorithms 2.5 12 6 3
[QDCS] Auto-stabilisation 2.5 21
[QDCS] Big Data 2.5 12 3 8
[M1 QDCS] High performance computing 2.5 12 9
[QDCS] [M2 QDCS] Recent trends in parallel, distributed, and quantum computing 2.5 21
[QDCS] Initiation to quantum algorithms and programming 2.5 21
[M1 QDCS] Game, learning, and optimisation of 2.5 21
complex systems 2.5 21
[QDCS] Stochastic optimisation 2.5 21
[QDCS] Ordonnancement et systèmes d'exécution 2.5 21
[QDCS] Advanced C++ programming 2.5 9 0 12
[QDCS] GPU programming 2.5 12 9
[QDCS] Programmation orientée objet 2.5 11 10
[SOFT] Soft skills - 1A (Langue) 2.5 21
[SOFT] Soft skills - 1B (Langue) 2.5 100
[SOFT] Soft skills - 2 (Communication) 2.5 21
[SOFT] Soft skills - 3 (Formation à la vie de l'entreprise - Initiation) 2.5 21
[SOFT] Soft skills - 4 Innovation et Entreprenariat 2.5 21
[SOFT] Soft skills - 5 Innovation et Entreprenariat avancé 2.5 21
[SOFT] Soft skills - Computer Sciences & Sustainable Development 2.5 9 12
[SOFT] Soft skills - Seminars (Fairness in Data Science) 2.5 20
[SOFT] Soft skills - Seminars B 2.5
[SOFT] Soft skills - Summer school 2.5 21
[SOFT] Soft skills - Transversal Project A 2.5 7 7 7
[SOFT] Soft skills - Transversal Project B 2.5 7 7 7
EIT - Business Development Lab 1 4
EIT - Business Development Lab 2 5
EIT - Innovation & Entrepreneurship Advanced 1 2.5 21
EIT - Innovation & Entrepreneurship Advanced 2 2.5
EIT - Innovation & Entrepreneurship Study 1 3 21
EIT - Innovation & Entrepreneurship Study 2 3
EIT - Innovation and Entrepreneurship Basics 1 3
EIT - Innovation and Entrepreneurship Basics 2 3
EIT - Summer School 4
French Language and Culture 1 2 30
French Language and Culture 2 2 21
Long internship 30
TER Stage 10
Modalités de candidatures
Application period
From 15/04/2024 to 31/05/2024
Compulsory supporting documents
  • Motivation letter.

    (A letter detailing the motivation and reasons for willing to study quantum, parallel and distributed computing in the QDCS master's program in the light of previous studies and experiences as well as future career plans.)
  • All transcripts of the years / semesters validated since the high school diploma at the date of application.

    (Grades of all courses since high school.)
  • Curriculum Vitae.

    (A CV detailing all previous studies, internships, trainings, work experience (if any), distinctions and awards, as well as other personal interests and activities.)
  • Selection sheet completed.

    (Please fill out and attach the master's track preference sheet available at: https://master-info-orsay.lri.fr/ChoixParcours.pdf)
Additional supporting documents
  • Letter of recommendation or internship evaluation.

    (Please attach all recommendation letters from your professors or internship supervisors (if any) in a single PDF file.)
  • Detailed description and hourly volume of courses taken since the beginning of the university program.

    (Please provide a document detailing all undergraduate/masters courses you took in the past in case you performed these studies in a language other than French or English, in order to evaluate the adequacy of your past studies.)
  • VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).

    (only needed in case you have officially validated your prior professional experience to count as equivalent to a university degree)
  • Supporting documents :
    - Residence permit stating the country of residence of the first country
    - Or receipt of request stating the country of first asylum
    - Or document from the UNHCR granting refugee status
    - Or receipt of refugee status request delivered in France
    - Or residence permit stating the refugee status delivered in France
    - Or document stating subsidiary protection in France or abroad
    - Or document stating temporary protection in France or abroad.

Contact(s)
Administrative office
Admission