  As the complexity of the next generation of quantum sensors increases, it
becomes more and more intriguing to consider a new paradigm in which the design
and control of metrological devices is supported by machine learning
approaches. In a demonstration of such a design philosophy, we apply
reinforcement learning to engineer a shaken-lattice matter-wave gyroscope
involving minimal human intuition. In fact, the machine is given no
instructions as to how to construct the splitting, reflecting, and recombining
components intrinsic to conventional interferometry. Instead, we assign the
machine the task of optimizing the sensitivity of a gyroscope to rotational
signals and ask it to create the lattice-shaking protocol in an end-to-end
fashion. What results is a machine-learned solution to the design task that is
completely distinct from the familiar sequence of a typical Mach-Zehnder-type
matter-wave interferometer, and with significant improvements in sensitivity.
