Title: Variational Quantum Algorithms

Abstract: Quantum computing holds the potential to solve problems that are too complex for even the most advanced classical computing systems. However, the current limitations of quantum hardware restrict the number of operations that can be performed before the results become unreliable. Variational Quantum Algorithms (VQAs) address this challenge by using a classical optimizer to adjust the parameters of a quantum algorithm. This approach minimizes a cost function with a limited number of quantum operations, making it feasible for execution on today’s quantum hardware. In this tutorial we will guide the participants through a couple examples using variational quantum algorithms to solve an eigenvalue problem and an optimization problem.

Biographies:

Dr. Eduardo Antonio Coello Pérez is a computational scientist at the National Center for Computational Sciences. His research focuses on evaluating the performance of computing systems that integrate classical and quantum processing units, and that of the hybrid algorithms that make use of such computing systems. As a member of the Center, he aims to develop software and computational capabilities to perform scalable simulations of the execution of diverse hybrid quantum algorithms.

Dr. Seongmin Kim is a postdoctoral research associate in the Quantum High-Performance Computing group at the National Center for Computational Sciences. He received his Ph.D. at Pohang University of Science and Technology, South Korea, in 2021. His research interests are the integration of quantum computing with high-performance computing, and the development of an active learning algorithm leveraging machine learning, high-performance computing and quantum computing.