# Digital-analog quantum learning on Rydberg atom arrays

arXiv: https://arxiv.org/abs/2401.02940 · HTML: https://mlsanity.com/paper/2401.02940/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 5 Jan 2024.

## Built on by (most cited first, 6 of 6)

- [Classification of the Fashion-MNIST Dataset on a Quantum Computer](https://mlsanity.com/paper/2403.02405/) (2024)
- [Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows](https://mlsanity.com/paper/2402.17911/) (2024)
- [ReCon: Reconfiguring Analog Rydberg Atom Quantum Computers for Quantum Generative Adversarial Networks](https://mlsanity.com/paper/2408.13389/) (2024)
- [ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers](https://mlsanity.com/paper/2506.21537/) (2025)
- [Digital-Analog Quantum Machine Learning](https://mlsanity.com/paper/2411.10744/) (2024)
- [Analog Quantum Asynchronous Event-Based Graph Neural Network](https://mlsanity.com/paper/2606.11000/) (2026)

## Abstract (arXiv)

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.