Flexible learning of quantum states with generative query neural networks. (arXiv:2202.06804v2 [quant-ph] UPDATED)

Deep neural networks are a powerful tool for the characterization of quantum
states.

Existing networks are typically trained with experimental data gathered from
the specific quantum state that needs to be characterized.

But is it possible to train a neural network offline and to make predictions
about quantum states other than the ones used for the training?

Here we introduce a model of network that can be trained with classically
simulated data from a fiducial set of states and measurements, …

arxiv learning networks neural networks quantum query