Digital-analog quantum learning on Rydberg atom arrays

2024-01-05

Quantum Science and Technology

AI Summary

This work addresses the low efficiency and poor robustness of variational quantum learning on recent neutral-atom quantum hardware. We propose the first digital-analog hybrid quantum learning framework tailored for Rydberg atom arrays. Methodologically, it integrates single-qubit gates with global Rydberg Hamiltonian evolution, enabling end-to-end training on two distinct tasks: supervised handwritten digit classification and unsupervised quantum phase transition boundary identification. Our key contribution is the systematic introduction of this hybrid paradigm to neutral-atom platforms—marking its first application in this context. The framework reduces circuit depth by over 30% compared to purely digital approaches and significantly enhances robustness against gate errors and decoherence. Validated via classical-quantum hybrid numerical simulations, the scheme maintains theoretical rigor while remaining compatible with near-term experimental capabilities.

Technology Category

• Machine Learning: Quantum Machine Learning
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Hybrid digital-analog quantum learning on Rydberg atom arrays
• Shorter circuit depths and robust to errors compared to digital learning
• Improving variational quantum learning experiments in the near term

Innovation

• Hybrid digital-analog learning on Rydberg arrays
• Single-qubit digital and global analog operations
• Shorter circuit depths and robust error tolerance