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 midcircuit 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 toward lower error rates, we demonstrate that accurate correlated noise models are increasingly important for successfully predicting subthreshold behavior in quantum error-correction experiments.
- Received 18 April 2023
- Accepted 29 August 2023
DOI:https://doi.org/10.1103/PRXQuantum.4.040311
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
Published by the American Physical Society
Physics Subject Headings (PhySH)
Popular Summary
Building error-corrected, fault-tolerant quantum computers will be required for universal quantum computing. Such error correction 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. Here, we show how to do this in a scalable way and demonstrate the method on a 39-qubit superconducting device.
Our method allows for the extraction of detailed information of the noise in a device running syndrome extraction circuits. We show how to use this information to efficiently construct graphical models of the noise, which stay scalable as the number of qubits increases. These models provide efficient descriptions of relevant features of the noise in large-scale devices and allow tailoring of codes and decoders to relevant features of the noise. Finally, we also demonstrate that accurate correlated noise models are increasingly important for predicting subthreshold behavior in quantum error-correction experiments.
The proposed method provides efficient descriptions of detailed Pauli noise models for large-scale systems in the form of graphical models. Such models will, for instance, enable the creation of minimum weight perfect matching decoders or tensor network decoders tailored to the underlying noise correlations. They also provide a means to generate datasets to enable, say, the efficient training of neural network decoders. Our findings have significant implications for the development of efficient models of noise in large-scale quantum devices, which are essential for building error-corrected quantum computers.
