SPRING SCHOOL

Title: "Variational Quantum Algorithms and their applications"

Abstract: Variational quantum algorithms are a general class of heuristic quantum algorithms which leverage both classical and quantum computing resources. They are typically used to approach optimization and eigenvalue problems and have potential applications in fields like quantum chemistry, logistics or quantum machine learning. In this session, we will explore the implementation of each step of the variational algorithm design workflow, its limitations, and current challenges. Moreover, we will showcase practical examples using Qiskit.

Bio: Joana Fraxanet is currently finishing her PhD at ICFO. Her research focuses on using quantum simulation and quantum computing to understand quantum many body systems such as strongly correlated phases of matter or models for quantum chemistry.  Recently, she started working at IBM quantum in the education and workforce development team, where she engages in outreach activities such as providing lectures and hands-on workshops for students, researchers or other technical profiles with various backgrounds.