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:
- 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.
- Contents:
- Lesson 3: One and two-qubit systems (Part 2) - November 20
- Contents:
- Quantum teleportation.
- Superdense coding.
- Deutsch algorithm.
- Contents:
- Lesson 4: Multiqubit systems - November 27
- Contents:
- Multiqubit gates and universality.
- Quantum parallelism.
- Deutsch-Jozsa algorithm.
- Grover algorithm.
- Shor algorithm.
- HHL algorithm.
- Contents:
- 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.
- Contents:
- Lesson 6: Quantum variational algorithms and quantum machine learning - December 11
- Contents:
- Variational Quantum Eigensolver.
- Introduction to Quantum Machine Learning (QSVM, QGAN, Quantum Classifiers...)
- Contents:
- Lesson 7: The future of quantum computing - December 18
- Contents:
- Quantum error correction.
- What is Quantum Supremacy?
- Prospects for quantum computing.
- Contents:
- CERN Openlab talk by F.Carminati: Quantum Computing for High Energy Physics Applications
- EP-IT Data science seminars
- Academic Training Lectures
- Introduction to Quantum Computing by Heather Gray (LBNL)