Experimental characterization of a non-Markovian quantum process

Most noise-characterization methods for quantum technologies assume Markovianity, meaning that the environment and system have no memory of their interactions with each other, because it is inefficient computationally demanding to take temporal correlations into account. Here, authors propose a more efficient machine learning method estimating non-Markovian noise implement in proof-of-principle optics experiment.