The MCQST Colloquium Series features interdisciplinary talks given by visiting international speakers. The monthly colloquium covers topics spanning all MCQST research units and will be broadcast live via Zoom for audiences worldwide. The main goal of the series is to create the framework for idea exchange, to strengthen links with QST leading groups worldwide, as well as to act as an integral part of the local educational environment.

MCQST Colloquium: Roger Melko

We are excited to invite you to the colloquium talk by Roger Melko (University of Waterloo and Perimeter Institute for Theoretical Physics).

Agenda

14:00 | Teaser talk by TBA

14:15 | Coffee break

14:30 | Colloquium talk by Roger Melko on “Language Models for Quantum Simulation”

Language Models for Quantum Simulation

In the last few years, generative machine learning has demonstrated a striking ability to scale, driving the rapid rise of GPT-like large language models. Over the same period, experimental quantum devices have advanced rapidly, with the simulation of quantum phases and phase transitions emerging as a key application. The growing availability of projective measurements from quantum simulators opens the exciting possibility of training custom generative models directly on quantum data. In this talk, I will demonstrate how such models can uncover hidden structures in quantum states, infer emergent properties, and predict outcomes of future experiments. I will close by speculating on how these and other AI tools might ultimately contribute to the challenge of scaling up quantum simulations, with the goal of discovering new physics in strongly-interacting quantum many-body systems.

About Roger Melko

Roger Melko is a Professor at the University of Waterloo, and Associate Faculty at the Perimeter Institute for Theoretical Physics. His research involves the development of computer strategies for the theoretical study of quantum materials, atomic matter, quantum information systems, and artificial intelligence.

Join in-person or via Zoom

https://lmu-munich.zoom.us/j/69761439704

Meeting ID: 697 6143 9704, Passcode: mcqst2526