Randomised benchmarking for characterizing and forecasting correlated processes
arxiv(2023)
摘要
The development of fault-tolerant quantum processors relies on the ability to
control noise. A particularly insidious form of noise is temporally correlated
or non-Markovian noise. By combining randomized benchmarking with supervised
machine learning algorithms, we develop a method to learn the details of
temporally correlated noise. In particular, we can learn the time-independent
evolution operator of system plus bath and this leads to (i) the ability to
characterize the degree of non-Markovianity of the dynamics and (ii) the
ability to predict the dynamics of the system even beyond the times we have
used to train our model. We exemplify this by implementing our method on a
superconducting quantum processor. Our experimental results show a drastic
change between the Markovian and non-Markovian regimes for the learning
accuracies.
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