  Quantum measurements are a fundamental component of quantum computing.
However, on modern-day quantum computers, measurements can be more error prone
than quantum gates, and are susceptible to non-unital errors as well as
non-local correlations due to measurement crosstalk. While readout errors can
be mitigated in post-processing, it is inefficient in the number of qubits due
to a combinatorially-large number of possible states that need to be
characterized. In this work, we show that measurement errors can be tailored
into a simple stochastic error model using randomized compiling, enabling the
efficient mitigation of readout errors via quasi-probability distributions
reconstructed from the measurement of a single preparation state in an
exponentially large confusion matrix. We demonstrate the scalability and power
of this approach by correcting readout errors without matrix inversion on a
large number of different preparation states applied to a register of eight
superconducting transmon qubits. Moreover, we show that this method can be
extended to mid-circuit measurements used for active feedback via
quasi-probabilistic error cancellation, and demonstrate the correction of
measurement errors on an ancilla qubit used to detect and actively correct
bit-flip errors on an entangled memory qubit. Our approach enables the
correction of readout errors on large numbers of qubits, and offers a strategy
for correcting readout errors in adaptive circuits in which the results of
mid-circuit measurements are used to perform conditional operations on
non-local qubits in real time.
