Randomized compiling for subsystem measurements
- URL: http://arxiv.org/abs/2304.06599v1
- Date: Thu, 13 Apr 2023 15:06:11 GMT
- Title: Randomized compiling for subsystem measurements
- Authors: Stefanie J. Beale, Joel J. Wallman
- Abstract summary: We introduce a new technique based on randomized compiling to transform errors in measurements into a simple form that removes particularly harmful effects.
We show that our technique reduces generic errors in a computational basis measurement to act like a confusion matrix.
We demonstrate that a simple and realistic noise model can cause errors that are harmful and difficult to model.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. 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. We further explore the impact of errors on indirect measurements and
demonstrate that a simple and realistic noise model can cause errors that are
harmful and difficult to model. Applying our technique in conjunction with
randomized compiling to an indirect measurement undergoing this noise results
in an effective noise which is easy to model and mitigate.
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