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.

我们提出了在 Rydberg 原子阵列上的混合数字 - 模拟学习算法，将量子学习的潜在实用性和近期可实现性与中性原子的快速扩展架构相结合。我们的结果表明，数字 - 模拟学习不仅在近期可行，而且需要更短的电路深度，并且对于现实错误模型更具鲁棒性，这为改进近期的变分量子学习实验开辟了有希望的道路。