Mitigating measurement errors in multiqubit experiments

Reducing measurement errors in multiqubit quantum devices is critical for performing any quantum algorithm. Here we show how to mitigate measurement errors by a classical postprocessing of the measured outcomes. Our techniques apply to any experiment where measurement outcomes are used for computing expected values of observables. Two error-mitigation schemes are presented based on tensor product and correlatedMarkovian noise models. Error rates parametrizing these noise models can be extracted from the measurement calibration data using a simple formula. Error mitigation is achieved by applying the inverse noise matrix to a probability vector that represents the outcomes of a noisy measurement. The error-mitigation overhead, including the number of measurements and the cost of the classical postprocessing, is exponential in epsilon n, where epsilon is the maximum error rate and n is the number of qubits. We report experimental demonstration of our error-mitigation methods on IBM Quantum devices using stabilizer measurements for graph states with n <= 12 qubits and entangled 20-qubit states generated by low-depth random Clifford circuits.

Automated summary

Reducing measurement errors in multiqubit quantum devices is critical for performing quantum algorithms. This study demonstrates error mitigation through classical postprocessing and introduces two error-mitigation schemes based on different noise models. Experimental demonstration on IBM Quantum devices shows the effectiveness of the proposed techniques.