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arXiv · 2311.01727

Noise-Agnostic Quantum Error Mitigation with Data Augmented Neural Models

Manwen Liao·Yan Zhu·Giulio Chiribella·Yuxiang Yang
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Abstract

Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural networks have a potential to lift this requirement, but current models require training data produced by ideal processes in the absence of noise. Here we build a neural model that achieves quantum error mitigation without any prior knowledge of the noise and without training on noise-free data. To achieve this feature, we introduce a quantum augmentation technique for error mitigation. Our approach applies to quantum circuits and to the dynamics of many-body and continuous-variable quantum systems, accommodating various types of noise models. We demonstrate its effectiveness by testing it both on simulated noisy circuits and on real quantum hardware.

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Manwen Liao, Yan Zhu, Giulio Chiribella, Yuxiang Yang. 2023-11-03. Noise-Agnostic Quantum Error Mitigation with Data Augmented Neural Models. https://doi.org/10.1038/s41534-025-00960-y

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