Practical hybrid digital-analog quantum learning on Rydberg atom arrays

We propose hybrid digital-analog learning algorithms on Rydberg atom arrays, combining the

potentially practical utility of quantum learning and near-term realizability 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; this “digital-

analog” scheme has already been achieved in recent experiments and will become widely available in

the coming years. We perform a comprehensive numerical study of our algorithms on both classical

and quantum data, given respectively by a modified version of handwritten digit classification and

unsupervised quantum phase boundary learning. We show in the two representative problems that

digital-analog learning is not only more 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. These

advantages can be explained by simple physical principles. Our results suggest that digital-analog

learning opens a promising path towards practical algorithm implementations in the near term, and

raises new questions about the power and limits of digital-analog circuitry for quantum learning.