Quantum computing is one the most promising new trends in information processing. This course introduces basic concepts of the quantum circuit model (qubits, gates and measures) and important quantum algorithms and protocols.
Beyond the practical aspects of quantum computing, the course will cover the implementation of algorithms in quantum simulators and actual quantum computers (such as the ones available through the IBM Quantum Experience and D-Wave Leap).
The European Council for Nuclear Research, also known as CERN, is conducting a free online course on the basics of quantum computing.
Being offered by CERN, these sessions are online, free of charge, and can be attended by anyone interested in quantum computing.
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Description
- This is a series of weekly lectures that will be broadcasted through webcasts, which began on 6 November 2020.
- Every Friday a new lecture will be uploaded.
- For those who missed it, the content of the lecture held on November 6 has been uploaded to the site.
- Elias Fernandez-Combarro Alvarez—an associate professor in the Computer Science Department at the University of Oviedo in Spain since 2009 and a cooperation associate at CERN—will be delivering the lectures.
Duration of session
This session will be for a duration of seven weeks, beginning 6 November, 2020 and ending on 18 December, 2020.
Prerequisites
- No previous knowledge of quantum physics is required to follow these open, free lectures on quantum computing
- A good command of basic linear algebra is necessary.
- Some familiarity with the Python programming language would be helpful, but is also not required.
What will you learn?
- Basic concepts of the quantum circuit model such as qubits, gates and measures will be introduced.
- Important quantum algorithms and protocols, including those that can be implemented with a few qubits like BB84, quantum teleportation, and superdense coding, among others, will be spoken about.
- Recent applications of quantum computing in the fields of optimisation and simulation will be addressed.
- Special emphasis will be paid to the use of quantum annealing, the quantum approximate optimisation algorithm and the variational quantum eigensolver along with quantum machine learning.
- Examples of how these techniques can be used in chemistry simulations and high-energy physics problems will also be provided.
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At the end of the webcast, documents and content pertaining to the session will also be uploaded.
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