Digital-analog quantum learning on Rydberg atom arrays
This work addresses the problem of enhancing near-term quantum learning experiments for researchers in quantum computing, though it appears incremental as it builds on existing variational methods with a hybrid twist.
The authors tackled the challenge of improving variational quantum learning by proposing hybrid digital-analog algorithms on Rydberg atom arrays, showing that this approach reduces circuit depths and enhances robustness to errors compared to digital-only methods in tasks like handwritten digit classification and quantum phase boundary learning.
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.