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Neural networks have shown their effectiveness in various tasks in the realmof quantum computing. However, their application in quantum error mitigation, acrucial step towards realizing practical quantum advancements, has beenrestricted by reliance on noise-free statistics. To tackle this criticalchallenge, we propose a data augmentation empowered neural model for errormitigation (DAEM). Our model does not require any prior knowledge about thespecific noise type and measurement settings and can estimate noise-freestatistics solely from the noisy measurement results of the target quantumprocess, rendering it highly suitable for practical implementation. Innumerical experiments, we show the model's superior performance in mitigatingvarious types of noise, including Markovian noise and Non-Markovian noise,compared with previous error mitigation methods. We further demonstrate itsversatility by employing the model to mitigate errors in diverse types ofquantum processes, including those involving large-scale quantum systems andcontinuous-variable quantum states. This powerful data augmentation-empoweredneural model for error mitigation establishes a solid foundation for realizingmore reliable and robust quantum technologies in practical applications.

Flexible Error Mitigation of Quantum Processes with Data Augmentation Empowered Neural Model