Neural networks have shown their effectiveness in various tasks in the realm of quantum computing. However, their application in quantum error mitigation, a crucial step towards realizing practical quantum advancements, has been restricted by reliance on noise-free statistics. To tackle this critical challenge, we propose a data augmentation empowered neural model for error mitigation (DAEM). Our model does not require any prior knowledge about the specific noise type and measurement settings and can estimate noise-free statistics solely from the noisy measurement results of the target quantum process, rendering it highly suitable for practical implementation. In numerical experiments, we show the model's superior performance in mitigating various types of noise, including Markovian noise and Non-Markovian noise, compared with previous error mitigation methods. We further demonstrate its versatility by employing the model to mitigate errors in diverse types of quantum processes, including those involving large-scale quantum systems and continuous-variable quantum states. This powerful data augmentation-empowered neural model for error mitigation establishes a solid foundation for realizing more reliable and robust quantum technologies in practical applications.

翻译：神经网络在量子计算各任务中展现出显著有效性，但其在量子误差抑制这一实现实用量子进展的关键环节中的应用，长期受限于对无噪声统计数据的依赖。针对这一关键挑战，我们提出了一种基于数据增强的神经误差抑制模型（DAEM）。该模型无需预知特定噪声类型与测量配置的先验知识，仅需目标量子过程的含噪测量结果即可直接估算无噪声统计数据，因而特别适用于实际场景。数值实验中，相较既有误差抑制方法，该模型在马尔可夫噪声与非马尔可夫噪声等各类噪声抑制中均表现出更优性能。我们进一步通过将模型应用于多元量子过程（涵盖大规模量子系统与连续变量量子态）的误差抑制，验证了其通用性。这种基于数据增强的强效神经误差抑制模型，为在实用层面实现更可靠、更鲁棒的量子技术奠定了坚实基础。