Automatic Differentiation

Differentiable programming (∂P), i.e., the ability to differentiate general computer program structures, has enabled the efficient combination of existing packages for scientific computation and machine learning1. The Julia2 language is well suited for ∂P, see also Chris’ article3 for a detailed examination.

Quantum control based on parametrized controllers trained with gradient information computed by (adjoint) sensitivity methods.