RQMP Research Seminar

Reconstructing quantum states with generative models

Roger Melko

University of Waterloo

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 powerful enough to reconstruct quantum states, given projective measurement data on individual qubits. These virtual reconstructions can then be studied with probes that may be unavailable to the original experiment. In this talk I will outline the strategy for quantum state reconstruction using generative models, and show examples on 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 mixed and time-evolved quantum states.