A Practical Introduction to Quantum Computing (CERN)

CERN openlab and the CERN Quantum Technology Initiative produced this lecture series as a genuinely accessible entry point to quantum computing for scientists and engineers who have not previously studied quantum physics. The recorded lectures are free to watch and cover both theory and near-term applications.

The course was recorded for CERN computing staff but is openly available to anyone. It stands out for its practical orientation: by the end, students have seen concrete implementations of variational algorithms and quantum machine learning techniques.

What you’ll learn

• The quantum circuit model: qubits, superposition, entanglement, and quantum measurement
• Basic quantum gates and how to compose them into circuits
• Small-qubit protocols: BB84 quantum key distribution, quantum teleportation, and superdense coding
• Quantum algorithms for larger systems: Deutsch-Jozsa, Grover’s search, and Shor’s algorithm
• Near-term quantum computing: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA)
• Quantum machine learning: quantum classifiers and the role of quantum data encoding
• Hands-on examples using Python and Qiskit throughout the series

Course structure

A series of recorded video lectures, each running approximately one hour, with accompanying slides available for download. The pacing is self-directed; there are no deadlines or enrollment requirements.

The course moves from introductory circuit concepts through advanced near-term algorithms. The final lectures on QAOA, VQE, and quantum machine learning make this series more forward-looking than many introductory courses that stop at Shor’s algorithm.

Who is this for?

• Scientists and engineers at research institutions who want a practical introduction
• Software developers interested in programming quantum hardware in the near term
• Students who want a concise overview before committing to a longer structured course
• Anyone curious about how quantum computing is being applied at a facility like CERN

Prerequisites

Basic linear algebra is the main requirement: vectors, matrices, and complex numbers. No prior knowledge of quantum physics is assumed or needed. Familiarity with Python is helpful for following the code examples but not required to understand the lectures.

Why take this course?

The CERN context gives this course a distinctive perspective. The instructors are motivated by real scientific computing problems at the world’s largest particle physics laboratory, and the near-term algorithm content reflects genuine interest in applications to simulation and optimization.

The course is concise enough to complete in a weekend of focused study while still covering material that most introductory courses omit entirely, including VQE, QAOA, and quantum machine learning. It makes a strong complement to longer structured courses by providing concrete application context.