Noise-Agnostic Quantum Error Mitigation with Data Augmented Neural Models
This addresses a key bottleneck for near-term quantum technologies by enabling more practical error mitigation in diverse quantum systems.
The paper tackled the problem of quantum error mitigation without requiring prior noise knowledge or noise-free training data, achieving effective error reduction across simulated and real quantum hardware.
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.