Efficient quantum readout-error mitigation for sparse measurement outcomes of near-term quantum devices

The readout error on near-term quantum devices is one of the dominant noise factors, which can be mitigated by classical postprocessing called mitigation (QREM). standard QREM applies inverse calibration matrix to outcome probability distribution using exponential computational resources number measured qubits. This becomes infeasible for current with tens qubits or more. Here we propose two efficient methods finishing in $O(n{s}^{2})$ time distributions $n$ and $s$ shots, mainly aim at mitigating sparse such that only a few states are dominant. We compare proposed several recent following three cases: expectation values GHZ state, its fidelities, estimation maximum likelihood amplitude (MLAE) algorithm modified Grover iterator. cases state real IBM devices, while third numerical simulation. Using method, 65-qubit takes seconds, witness fidelity 29-qubit exceeding 0.5. also succeed reducing MLAE algorithm, outperforming results other general.