Afternotes and exercises on the Practical introduction to quantum computing: from qubits to quantum machine learning and beyond course given by Elías F. Combarro at CERN (Nov 2020).

Slides and course material can be found here.

• Lesson 1: Introduction - November 6
  • Resources:
    • VOD
    • Notebook
    • Slides
    • Recommended books
  • Contents:
    • What is quantum computing?
    • Applications of quantum computing.
    • Hardware and software for quantum computing.
    • Elements of the quantum circuit model.
    • Introduction to the IBM Quantum Experience.
• Lesson 2: One and two-qubit systems (Part 1) - November 13
  • Contents:
    • Quantum key distribution with the BB84 protocol.
    • Two-qubit gates.
    • The CHSH game.
• Lesson 3: One and two-qubit systems (Part 2) - November 20
  • Contents:
    • Quantum teleportation.
    • Superdense coding.
    • Deutsch algorithm.
• Lesson 4: Multiqubit systems - November 27
  • Contents:
    • Multiqubit gates and universality.
    • Quantum parallelism.
    • Deutsch-Jozsa algorithm.
    • Grover algorithm.
    • Shor algorithm.
    • HHL algorithm.
• Lesson 5: Quantum algorithms for combinatorial optimization - December 4
  • Contents:
    • Quantum adiabatic computing and quantum annealing.
    • Introduction to D-Wave Leap.
    • Quantum Approximate Optimization Algorithm.
• Lesson 6: Quantum variational algorithms and quantum machine learning - December 11
  • Contents:
    • Variational Quantum Eigensolver.
    • Introduction to Quantum Machine Learning (QSVM, QGAN, Quantum Classifiers...)
• Lesson 7: The future of quantum computing - December 18
  • Contents:
    • Quantum error correction.
    • What is Quantum Supremacy?
    • Prospects for quantum computing.

• CERN Openlab talk by F.Carminati: Quantum Computing for High Energy Physics Applications
• EP-IT Data science seminars
  • Quantum Machine Learning
• Academic Training Lectures
  • Introduction to Quantum Computing by Heather Gray (LBNL)