Scalable quantum measurement error mitigation via conditional
  independence and transfer learning
Mitigating measurement errors in quantum systems without relying on quantum
error correction is of critical importance for the practical development of
quantum technology. Deep learning-based quantum measurement error mitigation
has exhibited advantages over the linear inversion method due to its capability
to correct non-linear noise. However, scalability remains a challenge for both
methods. In this study, we propose a scalable quantum measurement error
mitigation method that leverages the conditional independence of distant qubits
and incorporates transfer learning techniques. By leveraging the conditional
independence assumption, we achieve an exponential reduction in the size of
neural networks used for error mitigation. This enhancement also offers the
benefit of reducing the number of training data needed for the machine learning
model to successfully converge. Additionally, incorporating transfer learning
provides a constant speedup. We validate the effectiveness of our approach
through experiments conducted on IBM quantum devices with 7 and 13 qubits,
demonstrating excellent error mitigation performance and highlighting the
efficiency of our method.
ChangWon Lee Daniel K. Park