Programmation Quantique

Introduction to Quantum Computing

Description: This Quantum Programming and Algorithms course offers a comprehensive dive into the fundamentals and practical applications of quantum accelerators. Students will explore contemporary quantum architectures, including the principles of analog and digital architectures, as well as innovations such as hybrid CPU-QPU, NISQ, and LSQ architectures. The course will cover the formalism of qubits and digital quantum programming, highlighting the importance of superposition and entanglement for quantum computations. The principles and methods of measuring results will also be discussed. An introduction to quantum circuits, including basic gates and early circuits, will enable students to understand the practical basics of quantum programming. Using tools such as QFT, Grover, QPE, and QMC, students will explore classical circuits and their applications, while examining the limitations on NISQ architectures. Finally, students will delve into variational circuits and algorithms, including QAOA and Vxx circuits, and study runtime and performance models for QPUs as well as CPU-QPU loops, familiarizing themselves with the orders of magnitude of current runtimes.

Bibliography:

  • Ref. [1] : R. Hundt, Quantum Computing for Programmers, Cambridge University Press (2022)
  • Ref. [2] : P. Kaye, R. Laflamme, M. Mosca, An Introduction to Quantum Computing, Oxford University Press (2006)

Learning outcomes: AA1: Identify basic gates and build initial quantum circuits – AA2: Effectively use tools such as QFT, Grover, QPE, and QMC algorithms to solve engineering problems – AA3: Understand and be able to assess the limitations of NISQ architectures and propose appropriate solutions – AA4: Design and implement variational circuits and algorithms, such as QAOA and Vxx – AA5: Evaluate the performance and execution times of quantum algorithms in various contexts, including QPUs and CPU-QPU loops.

Evaluation methods: Assessment of the mini project

Evaluated skills:

  • Physical Engineering Design
  • Physical Modeling
  • Data Processing
  • Systems Analysis

Course supervisor:

  • Damien Rontani
  • Stéphane Vialle

Geode ID: SPM-INF-004