Unifying and benchmarking state-of-the-art quantum error mitigation techniques
Error mitigation is an essential component of achieving practical quantum advantage in the near term, and a number of different approaches have been proposed.
In this work we unify three recently proposed error mitigation methods: zero-noise extrapolation (ZNE), Clifford-data regression (CDR), and virtual distillation (VD). We unify these three methods under a general data-driven error mitigation framework that we call UNIfied Technique for Error mitigation with Data (UNITED).
Our work represents a benchmarking of current error mitigation methods, and provides a guide for the regimes when certain methods are most useful.
For details see
“Unifying and benchmarking state-of-the-art quantum error mitigation techniques”, Daniel Bultrini, Max Hunter Gordon, Piotr Czarnik, Andrew Arrasmith, Patrick J. Coles, Lukasz Cincio. arXiv:2107.13470.