Quantum Machine Learning aims at using these near-term computers to solve computational tasks more efficiently than any classical algorithm. Variational algorithms, or more generally quantum neural networks, have emerged as one of the most promising strategies to reach computational advantage with NISQ computers.

Quantum machine learning algorithms have been proposed for tasks such as finding the ground state of a quantum chemistry Hamiltonian, solving combinatorial problems, or more generally for Quantum Machine Learning tasks.

In this course we aim to introduce the theoretical framework behind variational algorithms, as well as discuss some of the most promising applications of these computational models. Moreover, we will also present some of the difficulties that variational algorithms have to overcome in order to achieve quantum advantage.

PWF projects in Guatemala have been oriented to data science and data analysis. This course will focus on Quantum Machine Learning, particularly using a Variational Quantum Eigensolver approach, in order for students to learn one of the new algorithms coming from quantum computing.

More information on PWF Guatemala.