Adaptive mitigation of time-varying quantum noise. (arXiv:2308.14756v1 [quant-ph])

Current quantum computers suffer from non-stationary noise channels with high
error rates, which undermines their reliability and reproducibility. We propose
a Bayesian inference-based adaptive algorithm that can learn and mitigate
quantum noise in response to changing channel conditions. Our study emphasizes
the need for dynamic inference of critical channel parameters to improve
program accuracy. We use the Dirichlet distribution to model the stochasticity
of the Pauli channel. This allows us to perform Bayesian inference, which can
improve the performance of probabilistic error cancellation (PEC) under
time-varying noise. Our work demonstrates the importance of characterizing and
mitigating temporal variations in quantum noise, which is crucial for
developing more accurate and reliable quantum technologies. Our results show
that Bayesian PEC can outperform non-adaptive approaches by a factor of 4.5x
when measured using Hellinger distance from the ideal distribution.



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