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Title: Digital–analog quantum learning on Rydberg atom arrays
Abstract We propose hybrid digital–analog (DA) 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 DA 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 DA learning opens a promising path towards improved variational quantum learning experiments in the near term.  more » « less
Award ID(s):
2317134 2118310
PAR ID:
10581675
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
Quantum Science and Technology
Date Published:
Journal Name:
Quantum Science and Technology
Volume:
10
Issue:
1
ISSN:
2058-9565
Page Range / eLocation ID:
015038
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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