We propose a novel, scalable error mitigation method that applies to gate-based quantum computers.
The method uses training data from quantum circuits that are classically efficiently simulatable to generate fitting models that can be used to correct for the effects of noise on the output of classically intractable quantum circuits.
For details see “Error mitigation with Clifford quantum-circuit data” by Piotr Czarnik, Andrew Arrasmith, Patrick J. Coles, Lukasz Cincio. arXiv:2005.10189.