Digital-analog quantum learning on Rydberg atom arrays

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.

23 pages, 22 figures. Version 2 for Quantum Science and Technology: https://iopscience.iop.org/article/10.1088/2058-9565/ad9177

References in corpus (17)

• Probing many-body dynamics on a 51-atom quantum simulator
• An atom-by-atom assembler of defect-free arbitrary 2d atomic arrays
• High-fidelity parallel entangling gates on a neutral atom quantum computer
• Controlling many-body dynamics with driven quantum scars in Rydberg atom arrays
• Competing density-wave orders in a one-dimensional hard-boson model
• Continuous Symmetry Breaking in a Two-dimensional Rydberg Array
• Quantum optimization with arbitrary connectivity using Rydberg atom arrays
• Experimental quantum adversarial learning with programmable superconducting qubits
• Quantum phase transitions beyond Landau-Ginzburg theory in one-dimensional space revisited
• Quantum simulation of Ising spins on Platonic graphs
• Interpretable Quantum Advantage in Neural Sequence Learning
• Conformal and chiral phase transitions in Rydberg chains
• Landau-Forbidden Quantum Criticality in Rydberg Quantum Simulators
• Trimer quantum spin liquid in a honeycomb array of Rydberg atoms
• Large-scale quantum reservoir learning with an analog quantum computer
• Digital-analog quantum learning on Rydberg atom arrays
• A polynomial-time classical algorithm for noisy quantum circuits

Cited by in corpus (6)

• Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows
• Digital-analog quantum convolutional neural networks for image classification
• Digital-analog quantum learning on Rydberg atom arrays
• Typical Machine Learning Datasets as Low-Depth Quantum Circuits
• Digital-Analog Quantum Machine Learning
• Hamiltonian simulation with explicit formulas for Digital-Analog Quantum Computing