  Adaptive feedback schemes are promising for quantum-enhanced measurements yet
are complicated to design. Machine learning can autonomously generate
algorithms in a classical setting. Here we adapt machine learning for quantum
information and use our framework to generate autonomous adaptive feedback
schemes for quantum measurement. In particular our approach replaces guesswork
in quantum measurement by a logical, fully-automatic, programmable routine. We
show that our method yields schemes that outperform the best known adaptive
scheme for interferometric phase estimation.
