Reconstructing quantum states with generative models
Roger Melko1*
1Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada
* Presenter:Roger Melko,
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.

Keywords: Machine Learning, Quantum Computers, Rydberg Atoms, Unsupervised Learning, Quantum State Tomography