Learning correlated noise in a 39-qubit quantum processor
- URL: http://arxiv.org/abs/2303.00780v1
- Date: Wed, 1 Mar 2023 19:07:35 GMT
- Title: Learning correlated noise in a 39-qubit quantum processor
- Authors: Robin Harper and Steven T. Flammia
- Abstract summary: Building error-corrected quantum computers relies crucially on measuring and modeling noise on candidate devices.
Here we propose a method of extracting detailed information of the noise in a device running syndrome extraction circuits.
We show how to extract from the 20 data qubits the information needed to build noise models of various sophistication.
- Score: 0.38073142980732994
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Building error-corrected quantum computers relies crucially on measuring and
modeling noise on candidate devices. In particular, optimal error correction
requires knowing the noise that occurs in the device as it executes the
circuits required for error correction. As devices increase in size we will
become more reliant on efficient models of this noise. However, such models
must still retain the information required to optimize the algorithms used for
error correction. Here we propose a method of extracting detailed information
of the noise in a device running syndrome extraction circuits. We introduce and
execute an experiment on a superconducting device using 39 of its qubits in a
surface code doing repeated rounds of syndrome extraction, but omitting the
mid-circuit measurement and reset. We show how to extract from the 20 data
qubits the information needed to build noise models of various sophistication
in the form of graphical models. These models give efficient descriptions of
noise in large-scale devices and are designed to illuminate the effectiveness
of error correction against correlated noise. Our estimates are furthermore
precise: we learn a consistent global distribution where all one- and two-qubit
error rates are known to a relative error of 0.1%. By extrapolating our
experimentally learned noise models towards lower error rates, we demonstrate
that accurate correlated noise models are increasingly important for
successfully predicting sub-threshold behavior in quantum error correction
experiments.
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