Enabling efficient characterization of measurement noise with measurement randomized compiling

Measurements are a vital part of any quantum computation, whether as a final step to retrieve results, as an intermediate step to inform subsequent operations, or as part of the computation itself (as in measurement-based quantum computing). However, measurements, like any aspect of a quantum system, are highly error-prone and difficult to characterize and model. In this talk, I will present a new noise tailoring method called measurement randomized compiling, which tailors the effective noise on measurements to a form which is simpler and easier to characterize accurately. The tailored noise is consistent with models of measurements used for many theoretical studies of quantum computing, including in the quantum error correction setting. In particular, our technique reduces generic errors in a measurement to act like a confusion matrix, i.e. to report the incorrect outcome with some probability, and as a stochastic channel that is independent of the measurement outcome on any unmeasured qudits in the system. footnote{The research presented in this talk was co-authored by Joel J. Wallman.}