Flexible Error Mitigation of Quantum Processes with Data Augmentation
  Empowered Neural Model
Manwen Liao Yan Zhu Giulio Chiribella Yuxiang Yang
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.