Sample-efficient quantum error mitigation via classical learning surrogates
A paper published in Nature Quantum Intelligence describes a method that uses classical learning surrogates to reduce the number of circuit executions needed for quantum error mitigation. The approach trains a classical model on noisy quantum circuit outputs and uses it to estimate error-mitigated expectation values more efficiently.
OutlookPlausible
The method could be integrated as an optional error mitigation layer in quantum software stacks within two years, reducing shot counts by an order of magnitude for routine VQE and QAOA experiments.