  We demonstrate the design of a matterwave interferometer to measure
acceleration in one dimension with high precision. The system we base this on
consists of ultracold atoms in an optical lattice potential created by
interfering laser beams. Our approach uses reinforcement learning, a branch of
machine learning, that generates the protocols needed to realize lattice-based
analogs of optical components including a beam splitter, a mirror, and a
recombiner. The performance of these components is evaluated by comparison with
their optical analogs. The interferometer's sensitivity to acceleration is
quantitatively evaluated using a Bayesian statistical approach. We find the
sensitivity to surpass that of standard Bragg interferometry, demonstrating the
future potential for this design methodology.
