Distributed, Parallel, and Quantum Computing (M1 DiPaQ)

M1 DiPaQ students will gain competencies and skills in:

• Understanding current and emerging challenges in distributed, parallel, and quantum systems; assessing their impact on application domains in AI and data science.
• Designing, proving, and analyzing distributed, parallel, and quantum algorithms and protocols, with complexity analyses in time, memory, communication, and energy.
• Mastering the fundamentals of quantum algorithms and complexity; programming quantum circuits with standard libraries.
• Applying parallel programming models and performance engineering on distributed supercomputers and multicore machines.
• Designing and analyzing distributed algorithms with theoretical guarantees.
• Learning the basics of data analysis and AI for large-scale learning tasks.
• Performing code profiling and tracing, diagnosing bottlenecks, and tuning performance.
• Practicing software engineering for HPC using modern C++, profiling, testing, continuous integration, and reproducibility.
• Using version control with Git and maintaining rigorous documentation.
• Communicating technical content and scoping projects in research and industry contexts.
• Adopting responsible, reliable, and sustainable computing practices.

Computer systems are evolving toward higher efficiency and richer functionality across three major, interconnected scientific fields:

• Distributed systems deliver connectivity and dependable operation across the Internet, clouds, clusters, and sensors, addressing hard problems in synchronization, security, concurrency, and robustness.
• High-performance and parallel computing (HPC) tackles intensive workloads in science and AI by exploiting supercomputing architectures and rigorous performance engineering.
• Quantum computing provides algorithms and hardware that exploit quantum parallelism to achieve gains unreachable by classical paradigms.

The M1 DiPaQ master’s program provides students with a 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 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.

M1 DiPaQ establishes the foundations in HPC, distributed systems, and quantum computing for large-scale systems and applications in AI and applied science, with strong ties to the Paris-Saclay research and industry ecosystem leveraging these technologies. Students master the fundamentals of these three interconnected disciplines and thereby learn to design fast, scalable, and robust solutions for real-world computational challenges in big data analytics,  AI, scientific simulations, and quantum-enabled workflows with following objectives:

Knowledge objectives:

• Efficient parallel algorithms and programming on distributed and multi-core parallel machines with vector processing units,
• Advanced C++ programming, debugging, profiling, and performance tuning of HPC kernels
• Large-scale distributed algorithms and systems: replication, consensus, consistency, robustness
• Fundamentals of quantum technologies, algorithms, and programming
• Basics of AI, data science, and optimization for high performance data analytics, machine learning, and scientific computing

Skill objectives:

• Building high-quality parallel and distributed algorithms and software that meet latency, throughput, and performance targets on modern HPC architectures.
• Developing and analyzing scalable, robust algorithms with theoretical guarantees on scalability, consensus, termination, and fault tolerance.
• Optimizing HPC algorithms and code across the stack: complexity, memory locality, vectorization, communication, I/O, and networks.

Resources and practice:

• Access to university clusters and partner supercomputers for hands-on labs, course projects, code development, and tuning.
• Use of open-source toolchains and libraries widely adopted by the HPC community.
• Acquiring modern HPC software engineering practices with advanced C++, IDEs, version control (git), documentation, and continuous integration.