Efficient verification and description of quantum devices are critical for emerging technological applications. Digital quantum computing and condensed matter physics approach these challenges from different directions, yet at the same time there is a wealth of physical models that emerge at the centre of both approaches. In this talk, I am going to offer a perspective on quantum error correction through the lens of learning algorithms for many-body systems, efficient wave-function representations and rich phase diagrams. Finally, I will discuss emerging connections between quantum computational complexity and machine learning representations of quantum states.

About the speaker:

Eliška is an assistant professor at Kavli Institute of Nanoscience at Delft University of Technology in the Netherlands. She is also a member of World Economic Forum’s Global Future Council on Quantum Applications. Eliška works at the boundary of quantum computing, artificial intelligence and condensed matter physics. Eliška obtained her PhD at Aarhus University in Denmark and postdoctoral fellowship at ETH Zürich in Switzerland.