Theory Seminar: Reconstructing quantum states with generative models

Prof. Roger Melko, University of Waterloo and Perimeter Institute for Theoretical Physics
Generative models are a powerful tool in unsupervised machine learning, where the goal is to learn the unknown probability distribution that underlies a data set.

Prof. Roger Melko, University of Waterloo and Perimeter Institute for Theoretical Physics
Online Seminar via Zoom
Wed, 07. April 2021, 16:00 pm (MEZ)

Abstract:

Generative models are a powerful tool in unsupervised machine learning, where the goal is to learn the unknown probability distribution that underlies a data set.  Recently, it has been demonstrated that modern generative models adopted from industry are capable of reconstructing quantum states, given projective measurement data on individual qubits. These virtual reconstructions can then be studied with probes unavailable to the original experiment. In this talk I will outline the strategy for quantum state reconstruction using
generative models, and show examples on real experimental data from a Rydberg atom quantum simulator.  I will discuss the continuing theoretical development of the field, including the exploration of powerful autoregressive models for the reconstruction of sign-problematic and mixed quantum states.

If you'd like to participate in the seminar, please contact us!