Quantum Computing

Companies are now implementing advanced digital solutions to meet the growing need for latency reduction (conversational interfaces, simulation, optimisation, diagnostics, etc.) and complex algorithmic processing (High-Performance Computing).

Quantum computing exploits the non-classical properties of quantum systems (electrons, atoms, photons, etc.) to transmit, encrypt and manipulate information. The realisation of operational configurations on a nanometric scale would, in principle and in an as yet undetermined future, ensure minimal energy consumption and a boosted computing power.

Quantum technologies, which are currently in the industrialisation phase, are intended to renew business lines, reduce decision-making times, shift value centres and revolutionise business models. 

Quantum computer scientists, for their part, are confronted with new paradigms and have to think differently...

The aim of this course is to provide future data science engineers with the fundamental elements of quantum physics that will help them to develop Machine Learning applications using specific quantum computing algorithms.

• A reminder of Quantum Mechanics
  
  Superposition of states
  Qubits (two-level systems)
• Quantum cryptography (BB84 protocol)
• The quantum computer
  
  Shor and Grover algorithms
  Technological achievements
  Notion of decoherence
• Quantum AI
  
  Concepts of quantum machine learning
  Quantum perceptron algorithms
• Quantum algorithms on (quantum) simulators

Evaluation on machine. 

Schuld, Maria, and Francesco Petruccione. Supervised learning with quantum computers. Vol. 17. Berlin: Springer, 2018.

• Thomas Durt
• Hachem Kadri