CV4Quantum: Reducing the Sampling Overhead in Probabilistic Error Cancellation Using Control Variates
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Abstract
Quasiprobabilistic decompositions (QPDs) play a key role in maximizing the utility of near-term quantum hardware. For example, Probabilistic Error Cancellation (PEC) (an error mitigation technique) and circuit cutting (which enables large quantum computations to be performed on quantum hardware with a limited number of qubits) both involve QPDs. Computations based on QPDs typically incur large sampling overheads that grow exponentially, e.g., with the number of error-terms mitigated or the number of circuit-cuts employed, limiting their practical feasibility. In this work, we adapt the control variates variance reduction technique from the statistics literature in order to reduce the sampling overhead in QPD-based computations. We demonstrate our method, dubbed CV4Quantum, using simulation experiments that mimic a realistic PEC scenario. In more than 50% of the PEC-based estimations performed in the study, we observed a more than 50% reduction in the number of samples needed to achieve a given precision when using our technique. We discuss how future research on constructing good control variates can lead to even stronger sampling overhead reduction.