  We propose hybrid digital-analog learning algorithms on Rydberg atom arrays,
combining the potentially practical utility and near-term realizability of
quantum learning with the rapidly scaling architectures of neutral atoms. Our
construction requires only single-qubit operations in the digital setting and
global driving according to the Rydberg Hamiltonian in the analog setting. We
perform a comprehensive numerical study of our algorithm on both classical and
quantum data, given respectively by handwritten digit classification and
unsupervised quantum phase boundary learning. We show in the two representative
problems that digital-analog learning is not only feasible in the near term,
but also requires shorter circuit depths and is more robust to realistic error
models as compared to digital learning schemes. Our results suggest that
digital-analog learning opens a promising path towards improved variational
quantum learning experiments in the near term.
