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Measurements are a vital part of any quantum computation, whether as a finalstep to retrieve results, as an intermediate step to inform subsequentoperations, or as part of the computation itself (as in measurement-basedquantum computing). However, measurements, like any aspect of a quantum system,are highly error-prone and difficult to model. In this paper, we introduce anew technique based on randomized compiling to transform errors in measurementsinto a simple form that removes particularly harmful effects and is also easyto analyze. In particular, we show that our technique reduces generic errors ina computational basis measurement to act like a confusion matrix, i.e. toreport the incorrect outcome with some probability, and as a stochastic channelthat is independent of the measurement outcome on any unmeasured qudits in thesystem. We further explore the impact of errors on indirect measurements anddemonstrate that a simple and realistic noise model can cause errors that areharmful and difficult to model. Applying our technique in conjunction withrandomized compiling to an indirect measurement undergoing this noise resultsin an effective noise which is easy to model and mitigate.

Randomized compiling for subsystem measurements