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Quantum measurements are a fundamental component of quantum computing.However, on modern-day quantum computers, measurements can be more error pronethan quantum gates, and are susceptible to non-unital errors as well asnon-local correlations due to measurement crosstalk. While readout errors canbe mitigated in post-processing, it is inefficient in the number of qubits dueto a combinatorially-large number of possible states that need to becharacterized. In this work, we show that measurement errors can be tailoredinto a simple stochastic error model using randomized compiling, enabling theefficient mitigation of readout errors via quasi-probability distributionsreconstructed from the measurement of a single preparation state in anexponentially large confusion matrix. We demonstrate the scalability and powerof this approach by correcting readout errors without the need for any matrixinversion on a large number of different preparation states applied to aregister of a eight superconducting transmon qubits. Moreover, we show thatthis method can be extended to measurement in the single-shot limit usingquasi-probabilistic error cancellation, and demonstrate the correction ofmid-circuit measurement errors on an ancilla qubit used to detect and activelycorrect bit-flip errors on an entangled memory qubit. Our approach paves theway for performing an assumption-free correction of readout errors on largenumbers of qubits, and offers a strategy for correcting readout errors inadaptive circuits in which the results of mid-circuit measurements are used toperform conditional operations on non-local qubits in real time.

Quasi-Probabilistic Readout Correction of Mid-Circuit Measurements for Adaptive Feedback via Measurement Randomized Compiling