Randomized compiling for subsystem measurements

Understand

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 model.
• In this paper, we introduce a new technique based on randomized compiling to transform errors in measurements into a simple form that removes particularly harmful effects and is also easy to analyze.
• In particular, we show that our technique reduces generic errors in a computational basis measurement to act like a confusion matrix, i.e.